RISK FACTORS LEADING TO COST OVERRUN IN THE DELIVERY … · Risk factors leading to cost overrun in the delivery of highway construction projects. II Statement of Original Authorship

_________________________________

RISK FACTORS LEADING TO COST OVERRUN IN

THE DELIVERY OF HIGHWAY CONSTRUCTION PROJECTS

By: Garry D. Creedy

A thesis submitted in fulfilment of the requirement for the degree of Doctor of Philosophy

Principal Supervisor:

Professor Martin Skitmore

Associate Supervisor: Professor Tony Sidwell

Queensland University of Technology

Research Centre: School of Urban Development,

Faculty of Built Environment and Engineering 2006

Risk factors leading to cost overrun in the delivery of highway construction projects.

II

Statement of Original Authorship

DECLARATION

The work contained in this thesis has not been previously submitted for a degree or diploma at any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made. Signed: ______________________________ Date: ________________________

III

This research is dedicated to my wife, MT, who believed in me as she wrote .....

This is a dream that you thought you would never get the chance to experience, and here it has been presented to you in a very short time. Maybe, so you can’t think about your decision too long!!! Grab it and know that I am here to support you and know that I will not only be behind you but around you. I love you and you deserve this great opportunity to fulfil l your dream (MT, 2002).


IV

ACKNOWLEDGMENTS

I would like to express my sincere thank you to the following people, without whose help, this thesis would not have been possible. Many thanks go to my principle advisor and supervisor, Professor Martin Skitmore for his unwavering support and guidance throughout this research. Especially for his ability and skill in maintaining a focus on client and industry needs in highway construction. I thank Martin for re-introducing me to statistical theory and its practical applications in research. I am truly lucky to have him as my advisor and supervisor who guided me through my research project from the start to the end. Also, I would like to express gratitude to my associate advisor, Professor Tony Sidwell, for his comments and discussions. As well, I would like to thank all the staff in the school of Urban Design for their support. I am grateful to the members of the Cooperative Research Centre for Construction Innovation for their financial and personal support throughout the journey. I would like also to express gratitude to my employer, the Queensland Department of Main Roads for the support by way of a study scholarship during the first two years of this research. In particular I wish to thank Dennis Wogan who acted as my industry supervisor and was a leading member of the reference group who contributed to and supported my research project. I would like to give a very special thanks to Alan McLennan, Adjunct Professor in Engineering at the Queensland University of Technology. Allan contributed much of his valuable personal time in guiding me in the practical directions of the research. I am particularly thankful for his expert facilitation of the elicitation process that was so important to my research. To my mentor, Dr. Mel Silverman of New Jersey, I thank you for the confidence and determination you have instilled in me during our long friendship. In particular, thank you for the endless email support and direction you provided during this long journey — you picked just the right times throughout the journey to 'rattle my cage' and so get me refocused once more. Most importantly, I would like to thank my wife and best friend MT, for her love and support. She inspired me to keep on going, even when it became physically and emotionally tough — thank you, my Linda. To my family, I thank you for all your patience, love and support. To my three special daughters, Michelle, Suzanne and Rebecca, thank you for all you have done for me.

V

ABSTRACT

Accurate client budget estimates are critical to the initial decision-to-build process for the highway construction projects. This decision-to-build point in a project's development is seen as the international standard for measuring any subsequent cost estimate inaccuracies involved (National Audit Office/Department of Transport, 1992; World Bank, 1994; Nijkamp and Ubbels, 1999), with accuracy being defined as the difference between the initial project estimate at the decision-to-build stage and the real, accounted project cost determined at the time of project completion. Expressed as a percentage of estimated cost, this is often termed cost escalation, cost overrun or cost growth, and occurs as a result of many factors, some of which are related to each other, but all are associated with forms of risks. The analysis of these risks is often a necessary step for the improvement of any given estimating system and can be used to diagnose trouble spots and to pinpoint areas where project estimating accuracy improvement might be obtained. In this research, highway projects in Queensland, Australia that have suffered significant cost overrun are analysed. The research seeks to address the gap in the knowledgebase as to why highway projects overrun their costs. It focuses on understanding how client projects budgets go wrong, when dealing with project risk. The foundation for this research is drawn from the post-mortem analysis of highway projects, each costing in excess of A$1m and whose final total expenditure exceeded budget by 10% or greater. The research identifies client risk variables which have contributed to significant cost overrun and then uses factor analysis and also expert elicitation, using nominal group technique, to establish groups of importance ranked client risks. Stepwise multivariate regression analysis is then used to investigate any correlation of these risks, along with project attributes such as highway project type, indexed project cost, geographic location and project delivery method to the percentage of cost overrun. The research results indicates a correlation between the reciprocal of project budget size and percentage cost overrun that can be useful in clients determining more realistic decision-to-build highway budget estimates when taking into account project size in relation to economy of scale. Key words: Highway construction, project budgeting, cost performance, construction management.


VI

CONTENTS

STATEMENT OF ORIGINAL AUTHORSHIP............................................................................. II ACKNOWLEDGEMENTS .......................................................................................................... IV ABSTRACT ...............................................................................................................................V LIST OF FIGURES....................................................................................................................... XI LIST OF TABLES ....................................................................................................................... XII LIST APPENDICES ....................................................................................................................XV CHAPTER 1 Introduction to research project........................................................................................................1

1.1 Background to the Research................................................................................................1

1.2 Research problem ................................................................................................................3

1.3 Research purpose.................................................................................................................7

1.4 Research objectives .............................................................................................................7

1.5 Justification for the research................................................................................................7

1.6 Methodology .......................................................................................................................8

1.7 Outline of the research – thesis organisation.......................................................................9

1.8 Delimitations of scope and key assumptions.....................................................................10

1.9 Conclusion.........................................................................................................................11 CHAPTER 2 Literature review into project risks and cost overrun .....................................................................13

2.1 Preamble............................................................................................................................13

2.2 Introduction .......................................................................................................................13

2.3 Definitions .........................................................................................................................14 2.3.1 The Nature of Risk ...............................................................................................17 2.3.2 Risk and uncertainty.............................................................................................17 2.3.3 Risk management .................................................................................................18 2.3.4 Risk evaluation and acceptance............................................................................19 2.3.5 Risk assessment....................................................................................................20 2.3.6 Risk assessment models .......................................................................................20 2.3.7 Risk control processes ..........................................................................................21

2.4 Risk engineering................................................................................................................22

2.5 Qualitative risk assessment................................................................................................23

2.6 Quantitative risk assessment..............................................................................................23 2.6.1 Simulation ............................................................................................................24 2.6.2 Artificial logic applications ..................................................................................25

2.7 Assessment of probabilities and consequences .................................................................25

2.8 Elicitation of risk ...............................................................................................................27

VII

2.8.1 Expert elicitation ..................................................................................................27 2.8.2 Expert elicitation using the Delphi method..........................................................28 2.8.3 Elicitation using semi-structured interviews ........................................................28 2.8.4 Nominal group technique.....................................................................................29

2.9 Organisational risk culture ................................................................................................29

2.10 Project risk management ...................................................................................................30

2.11 Project site condition risks ................................................................................................33

2.12 Managing risks through procurement practices ................................................................35

2.13 Risk distribution in project procurement...........................................................................36 2.13.1 Risk allocation through standard-form contracts .................................................39

2.14 Payment methods for contract risks ..................................................................................39

2.15 Delivery processes for projects .........................................................................................40

2.16 The traditional method of project procurement ................................................................42

2.17 Improving project delivery methods for risk.....................................................................44 2.17.1 Build-Operate-Turnover project delivery ............................................................45 2.17.2 Relationship/Alliance/Partnering contracting ......................................................46

2.18 Project contracting ............................................................................................................48 2.18.1 Procurement auctions...........................................................................................49 2.18.2 The competitive tendering process.......................................................................49

2.19 Pre-qualification of contractors.........................................................................................50 2.19.1 Research into client pre-qualification practices ...................................................52 2.19.2 Choosing contractors on low bids ........................................................................54

2.20 Bid evaluation ...................................................................................................................56 2.20.1 Research into bidding...........................................................................................57 2.20.2 Sub-contractor selection.......................................................................................58

2.21 Project budget estimating..................................................................................................58 2.21.1 Estimating processes ............................................................................................59 2.21.2 Early project estimates .........................................................................................60 2.21.3 Project cost overrun .............................................................................................60 2.21.4 Research into project estimating ..........................................................................61 2.21.5 Factors influencing costs......................................................................................63 2.21.6 The relation between cost accuracy and scope.....................................................63 2.21.7 Scope change........................................................................................................64

2.22 Cost forecasting models ....................................................................................................65

2.23 Project cost contingency ...................................................................................................68

2.24 Estimating methods for cost contingency .........................................................................70

2.25 Literature summary ...........................................................................................................74


VIII

CHAPTER 3 Research methodology ...................................................................................................................77

3.1 Introduction .......................................................................................................................77

3.2 Research strategy...............................................................................................................77

3.3 The research procedure......................................................................................................80

3.4 Research steps ...................................................................................................................81

3.5 Stage 1: Review literature .................................................................................................81

3.6 Stage 2: Establish data source of highway construction projects ......................................82

3.6.1 Indexing of historical road project prices .............................................................84

3.7 Stage 3: (a) Determine cost overrun factors from historic project data.............................85

3.8 Stage 3: (b) Factor analysis using principal component analysis and factor rotation on cost overrun factors to consolidate data ..........................................................85 3.8.1 Factoring methods ................................................................................................85 3.8.2 Method of factor extraction ..................................................................................86 3.8.3 Sample size for factor analysis .............................................................................86 3.8.4 Type of factor rotation employed .........................................................................87 3.8.5 Number of factors in analysis...............................................................................87 3.8.6 Characteristics of samples in the factor analysis ..................................................87

3.9 Stage 4: Use nominal group technique (NGT) to elicit, review and prioritize cost overrun risk groupings and highway project types ....................................................88 3.9.1 Focus group ..........................................................................................................88 3.9.2 Delphi technique...................................................................................................88 3.9.3 Nominal group technique .....................................................................................89 3.9.4 Selection of experts for NGT process ..................................................................90 3.9.5 Group composition ...............................................................................................91 3.9.6 Ranking process ...................................................................................................91 3.9.7 NGT workshop evaluation ...................................................................................92

3.10 Stage 5: Undertake data analysis and statistical modelling ...............................................92 3.10.1 Regression analysis basis assumptions.................................................................95 3.10.2 Correlation analysis..............................................................................................96 3.10.3 Statistical regression.............................................................................................96 3.10.4 Sample size of data...............................................................................................97 3.10.5 p-value..................................................................................................................97 3.10.6 t-test ......................................................................................................................98 3.10.7 Other considerations.............................................................................................99

3.11 Ethical considerations........................................................................................................99

3.12 Conclusion.........................................................................................................................99

IX

CHAPTER 4 Data collection, analysis techniques and statistics .......................................................................101

4.1 Preamble..........................................................................................................................101

4.2 Stage 2: Establish data sources of highway construction projects ..................................101 4.2.1 Project number and location ..............................................................................102 4.2.2 Description of works..........................................................................................102 4.2.3 Method of project delivery.................................................................................103 4.2.4 Programmed cost................................................................................................105 4.2.5 Actual cost..........................................................................................................105 4.2.6 Percentage (%) ...................................................................................................106 4.2.7 Indexing of project costs to 2002–03 out-turn prices.........................................106

4.3 Stage 3 (a): Determine cost overrun factors from historic project data .........................107 4.3.1 Exclusion of some initial historical project data ................................................109

4.4 Stage 3 (b): Use factor analysis (principal component analysis) and factor rotation on cost overrun factors to consolidate data..................................................................................109 4.4.1 Factor analysis test parameters...........................................................................110 4.4.2 Principal component analysis.............................................................................111 4.4.3 Scree plot............................................................................................................113 4.4.4 Factor rotation ....................................................................................................114

4.5 Step 4: Use nominal group technique (NGT) to elicit, review and prioritise principal cost overrun risk groupings and highway project types .................................................117 4.5.1 Identification and selection of experts ...............................................................117 4.5.2 Agenda Item 1: Research project background ...................................................118 4.5.3 Agenda item 2: Workshop aims/desired outcomes and explanation of

NGT for workshop .............................................................................................118 4.5.4 Agenda Item 3: Principal cost overrun grouping exercise .................................118 4.5.5. Agenda Item 4: Ranking of risk groupings ........................................................121 4.5.6. Agenda Item 5 of workshop: Development of highway project types...............125 4.5.7 NGT Workshop Agenda Item 6: Workshop feedback and assessment..............127

4.6 Application of NGT workshop findings to project data..................................................129

4.7 Step 4: Undertake data analysis and statistical modelling ..............................................131 4.7.1 Model – dependent variable ...............................................................................131 4.7.2 Model – prediction variables..............................................................................131 4.7.3 Geographic project type .....................................................................................132 4.7.4 Geographic data and model coding....................................................................132 4.7.5 Highway project construction type reference number .......................................135 4.7.6 Construction type data and model coding..........................................................135 4.7.7 Project delivery types.........................................................................................136 4.7.8 Project delivery data and model coding .............................................................136 4.7.9 Indexed highway project programmed cost continuous variable.......................137 4.7.10 Project high level risks .......................................................................................138 4.7.11 Project high level risk data and model coding ...................................................138


X

4.8 Regression modelling ......................................................................................................139 4.8.1 Outlying data values...........................................................................................139 4.8.2 Multivariate regression analysis .........................................................................141 4.8.3 Forward selection mode for multivariate regression analysis ............................142 4.8.4 Backwards selection mode for multivariate regression analysis ........................145 4.8.5 Stepwise selection mode for multivariate regression analysis ...........................146

4.9 Summary of variable selection modes for multivariate linear regression analysis.........146

4.10 Sensitivity testing of project data used in regression analyses ........................................147

4.11 Testing the general assumptions of regression and residual analysis..............................150 4.11.1 Normality of error testing...................................................................................151 4.11.2 Standardised residuals testing.............................................................................152 4.11.3 Data transformation investigation ......................................................................153 4.11.4 Data transformation using reciprocal of X .........................................................153 4.11.5 Re-testing of general assumptions of residuals ..................................................154

4.12 Regression analysis using reciprocal indexed prog. cost $m ..........................................155

4.13 Sensitivity testing using discrete rural and urban data sets .............................................156 4.13.1 Rural project data regression analysis ................................................................156 4.13.2 Testing of general assumptions of residuals for rural project data.....................157 4.13.3 Urban project data regression analysis ...............................................................159 4.13.4 Testing of general assumptions of residuals for urban project data ...................160

4.14 Sensitivity testing using open tender and negotiated price data sets ...............................161

4.15 Regression analysis model summary...............................................................................162 4.15.1 Correlated model ................................................................................................162 4.15.2 Summary of regression analysis 163

4.16 Chapter summary ............................................................................................................163 CHAPTER 5 Discussion, conclusions and recommendations .......................................................................165 5.1 Discussion .......................................................................................................................165 5.2 Highway project data.......................................................................................................165 5.3 Cost overrun project risk factors .....................................................................................166 5.4 Factor analysis .................................................................................................................167 5.5 Expert elicitation findings ...............................................................................................168 5.6 High level project risks....................................................................................................168 5.7 Model development using multivariate regression analysis............................................172 5.8 Implications for highway cost management 'body of knowledge'...................................175 5.9 Implications for cost estimating practices .......................................................................176 5.10 Research limitations ........................................................................................................178 5.11 Further research ...............................................................................................................179 5.12 Conclusions .....................................................................................................................181

BIBLIOGRAPHY....................................................................................................................181

XI

List of Figures

Figure 1.1: Traditional project development phases....................................................................1

Figure 1.2: Stages of project estimates ........................................................................................4

Figure 2.1: Construction risk analysis .......................................................................................23

Figure 2.2: Risk-aversive, risk-neutral and risk-seeking ...........................................................30

Figure 2.3: Traditional project delivery model ..........................................................................36

Figure 3.1: Empirical research strategies...................................................................................78

Figure 3.2: Main research activities...........................................................................................80

Figure 4.2: Normal probability plot of regression standardised residuals ...............................151

Figure 4.3: Scatterplot of the standardised residuals versus predicted values of 220 cases ....152

Figure 4.4: Histogram of standardised residuals......................................................................153

Figure 4.4: Normal probability plot with transformed variable...............................................154

Figure 4.5: Scatterplot of standardised residuals versus predicted of transformed data ..........155

Figure 4.6: Normal probability plot of rural project data ........................................................158

Figure 4.7: Scatterplot of standardised residuals versus predicted values for rural projects ...158

Figure 4.8: Normal probability plot of urban project data .......................................................160

Figure 4.9: Scatterplot of standardised residuals versus predicted values of urban projects ...161

Figure 5: 1: Future research proposal on project risk contingencies .......................................180


XII

List of Tables

Table 1.1: Highway projects over A$1m that exceeded estimate by >10%.................................5

Table 1.2: Research procedures ...................................................................................................9

Table 2.1: Methods for determining risk acceptance .................................................................19

Table 2.2: Quantitative risk assessment methods.......................................................................21

Table 2.3: Qualitative risk assessment methods.........................................................................21

Table 2.4: Sources of common project construction risks .........................................................32

Table 2.5: Target accuracy of highway design estimates...........................................................62

Table 2.6: Classes of project estimates ......................................................................................69

Table 2.7: Contingency ranges in highway estimates ................................................................70

Table 3.1: Situations for differing research strategies................................................................79

Table 3.2: Research procedures .................................................................................................81

Table 3.4: Model techniques ......................................................................................................92

Table 3.5: Interpretation of p-values ..........................................................................................98

Table 4.1: Number of projects analysed...................................................................................101

Table 4.2: Sample of data of completed highway ....................................................................102

Table 4.3: Refined listing of highway project work types .......................................................103

Table 4.4: Composition of project delivery methods ...............................................................104

Table 4.5 Project delivery code................................................................................................104

Table 4.6: Programmed costs of projects .................................................................................105

Table 4.7: Actual costs of projects ...........................................................................................106

Table 4.8: Percentage of cost overrun for analysis periods......................................................106

Table 4.9: RICI indices and factors applied to project costs....................................................107

Table 4.10: Project cost overrun factors derived from historic highway data..........................109

Table 4.11: Sample display of project and cost overrun matrix data used in analysis.............110

Table 4.12: KMO and Bartlet tests...........................................................................................110

Table 4.13: Initial and extraction communalities for cost overrun variables ...........................111

Table 4.14: Total variance explained – unrotated ....................................................................112

Table 4.15: Rotated and ordered component matrix ................................................................115

Table 4.16: Principal factor groupings from rotated component matrix ..................................116

Table 4.17: Expert group composition.....................................................................................117

Table 4.18: Experience profile of expert group membership...................................................118

Table 4.19: Cost overrun risk factor grouping worksheet........................................................120

XIII

Table 4.20: Agreed mapping of cost overrun factors to groupings..........................................121

Table 4.21: Ranked groupings derived from importance index...............................................122

Table 4.22: Final NGT highway project construction types....................................................125

Table 4.23: Desired workshop project construction types for future grouping .......................126

Table 4.24: Workshop evaluation for Question 1 ....................................................................127




Table 4.28: Sample of low level to high level risk group mapping to projects .......................129

Table 4.29: Incidences across projects for the HL/ risk groups...............................................130

Table 4.30: Split of rural and urban geographic types.............................................................134

Table 4.31: Data coding for geographic types .........................................................................134

Table 4.32: Construction project type reference number.........................................................135

Table 4.33: Sample data coding for only project types 1 to 6 .................................................136

Table 4.34: Project delivery codes...........................................................................................136

Table 4.35: Sample data coding for project delivery codes .....................................................137

Table 4.36: Sample of indexed programmed cost $m continuous variables............................138

Table 4.37: High level risk groupings codes............................................................................139

Table 4.38 Sample of high level risk grouping codes against projects....................................139

Table 4.39: Case-wise diagnostics...........................................................................................140

Table 4.40: Project outlier details ............................................................................................140

Table 4.41: Variables entered/removed in forward selection mode ........................................142

Table 4.42: Forward selection mode summary using dependent variable of % over cost .......143

Table 4.43: Coefficients for forward selection mode ..............................................................143

Table 4.44: Excluded variables in forward selection mode .....................................................145

Table 4.45: Variables entered/removed in stepwise selection mode .......................................146

Table 4.46 Summary of multivariate linear regression for three modes..................................147

Table 4.47: Sensitivity testing of coefficients for secondary stepwise selection mode ...........148

Table 4.48: Sensitivity testing for stepwise selection mode summary ....................................149

Table 4.49: Coefficients for stepwise selection mode .............................................................149

Table 4.50: Excluded variables in stepwise selection mode using revised data ......................150

Table 4.51: Residual statistics for stepwise selection mode for revised project data ..............150

Table 4.52: Variables entered/removed in stepwise selection mode .......................................150

Table 4.53: Sample of data using reciprocal transformation ...................................................154

Table 4.54: Model summary using reciprocal indexed prog. cost $m transformed data.........155


XIV

Table 4.55: Regression coefficients for transformed data model.............................................156

Table 4.56: Residual statistics for transformed project data ....................................................156

Table 4.57: Model summary using rural project data ..............................................................156

Table 4.58: Regression coefficients for rural data model ........................................................157

Table 4.59: Residual statistics for rural project data ................................................................157

Table 4.60: Model summary using urban project data.............................................................159

Table 4.61: Regression coefficients for urban data model .......................................................159

Table 4.62: Residual statistics for urban project data ..............................................................159

Table 4.63: Indicative over-and-above contingency percentages for project size ...................162

Table 5.1: Project price adjustment percentages derived from RICI .......................................166

Table 5.2: Top twelve highway risk factors causing cost overrun...........................................167

Table 5.3: Experience profile of expert group membership.....................................................168

Table 5.4: Ranked principal highway cost overrun risk groups...............................................170

Table 5.5: Client risk sources during project ...........................................................................171

Table 5.6: Highway project construction types........................................................................172

Table 5.7: 1986 Texas Department of Transport highway project classifications ...................172

Table 5.8: Over-and-above contingency percentages for varying project sizes ......................175

XV

List of Appendices

Appendix A: Road Input Cost Indices for the period 1995-96 to 2002-03 .......................207

Appendix B: Programmed and actual project costs indexed to year 2003 ........................211

Appendix C: Questionnaire format to establish NGT membership...................................217

Appendix D: Questionnaire containing summarisied NGT responses ..............................223

Appendix E: Client risks in highway project delivery.......................................................233


XVI

CHAPTER 1 Introduction to research project

1

CHAPTER 1

Introduction to research project

1.1 Background to the Research Cost estimation is particularly difficult in the construction industry, often leading to considerable cost overruns that are explained by large uncertainties and uniqueness of projects (Baker et al., 1995). One might expect that cost overruns have that same probability as completing the project below the cost estimate. However, observations clearly indicate an overrepresentation of cost overruns (Emhjellen, 2003). Transport infrastructure projects are particularly prone to cost overrun, with actual costs on average being 28% higher than estimated (Flyvbjerg et al., 2003). The delivery of projects is performed using mainly traditional processes that have evolved from history and the industrial revolution, where specialisation of professional organisations was the key trend (Pakkala, 2002). This means that architects, engineers, specialty contractors, and the industry have adopted a segmented rather than an integrated type of process. In developed countries, the majority of projects have used the project delivery method of design-bid-build (DBB) (Gould, 1997), alternatively known as the tradition method, and typically consisting of discrete feasibility/preliminary design, full design, construction and operation phases (Fig 1.1).

Figure 1.1: Traditional project development phases

Highway infrastructure project delivery in Australia, Canada, England, New Zealand, Sweden and the US predominately use Design-Bid-Build (DBB) (Pakkala, 2002) which allows the design/engineering service to be produced first, and then another procurement contract tendered for the actual construction that is based on the design. The traditional procurement process has been developed to focus on clarity, separation of phases and provide a transparent and independent bidding stage. The process can be inefficient and take a long time and often sets up opposing stances between participants, however the clarity of this process is particularly attractive to clients who need to demonstrate probity (Sidwell et al., 2002). Although lack of cost control in all of the project development phases can contribute to cost control problems, of particular interest is the time the client makes the decision to build (Hester et al., 1991). In the traditional method, this is often made towards the end of the design phase. Accurate budget estimates are critical to the initial decision-to-build process

Design

Construction

Operation

Feasibility/ preliminary

design


2

for the construction of capital projects (Ward, 1999). These are usually based on a number of factors such as the complexity of the project, the speed of its construction, the location of the project and degree of unfamiliarity (Baker et al., 1999). Project cost overrun can be caused by rising costs from inflation and inadequate analysis of information (Kayode, 1979) and by costing methods (Akpan and Igwe, 2001). Engineering designs have a high level of influence on project costs and sometimes unsatisfactory design performance can lead to cost overrun (Barrie and Paulson, 1992). There have been few instances where an engineering design is so complete that a project could be built to the exact specifications contained in the original design documents (Chang (2002). Many construction problems are due to design defects and can be traced back to the design process (Bramble and Cipollini, 1998). Design and project specific factors such as vagueness in scope, design complexity and project size affect the cost estimate of a project (Akinci and Fischer 1998). For capital projects, cost estimates are first prepared to enable clients to make reliable decisions regarding economic feasibility and justification. Early project estimates are often prepared on limited scope definition and little information regarding the specific parameters that are needed in the completed facilities (Zeitoun and Oberlender, 1993). As well, they are often prepared under severe time constraints (Chang (2002). Estimates, even when grossly inaccurate, often become the basis upon which all future estimates are judged and for the project team, their performance and overall project success are often measured by the client by how well the final project cost compares to the initial cost estimate (Hester et al., 1991). For the project client, accurate cost estimates are vital for business decisions on strategies for asset development, potential project screening, and resource commitments for existing and proposed project developments. Accurate estimates are critical to the initial decision-to-build process for the construction of capital projects (Flyvbjerg et al., 2002). There are generally four stages involved in developing project cost estimates (Queensland Department of Main Roads, 2000:

1. Strategic stage: estimate of the cost of undertaking the planning functions required to produce the options analysis and business case. Usually applies to non-routine projects where significant cost will be incurred before the proposal is officially recognised as a project.

2. Concept stage: This provides comparative costs of options prepared as part of the options analysis. No project cost is produced at this stage. Concept approval gives authority for the development of the preferred option.

3. Preliminary Design: This develops the total project cost estimate based on the final design solution but usually before commencement of design, detailing and documentation.

4. Detailed design (Decision-to-Build): This is prepared on completion of the detailed design when final plans, specifications and bill of quantities are available. The detailed design generally forms part of the scheme prototype documents needed for approval to call tenders and forms the benchmark against which the final cost of completion is measured.


3

While cost estimates become more accurate over time, it is the cost estimate at the time of making the decision-to-build the project that is of primary interest (Flyvbjerg et al., 2002). This point in the project’s development is seen as the international standard for measuring the inaccuracy of project cost estimates (National Audit Office – Department of Transport, 1992; World Bank, 1994; Nijkamp and Ubbels, 1999). Accuracy is defined as the difference between the initial project estimate at the decision-to-build stage and the real, accounted project cost determined at the time of project completion. Expressed as a percentage of estimated cost, this is often termed cost-escalation, cost overrun or cost growth. Total project cost estimates include the costs of all component activities from the initiation of the project proposal to finalisation. These include the cost of developing the concept, investigations, developing the design, acquiring land, altering public utility plant, construction, project administration and handover (Queensland Department of Main Roads, 2000). Estimating methods and the accuracy of project cost estimates are major reason for project cost changes (Hester et al., 1991). When an inaccurate original estimate is prepared for a project and is used to compare the actual cost of that project, then there can be a noticeable difference, referred to as a cost change (Flyvbjerg et al., 2002). Clients assume that routine changes in projects project will only affect work in the change areas, whereas, in reality, the effects can extend well beyond specific change areas (Hester et al., 1991). Project cost accuracy is very important to clients as it enables them to have better cost control over projects. However, construction projects are notorious for running over budgets (Hester et al., 1991). 1.2 Research problem Contemporary project management practice is characterised by late delivery, exceeded budgets, reduced functionality and questionable quality (Williams, 1999) and while risk management is a recognised practice that helps clients deliver projects on schedule and within cost (Project Management Institute, 2000), the risk management performed in the construction industry has traditionally been that of gut feel or a series of rules-of-thumb (Al-Bahar and Crandall, 1990). Consequently, project risks are often not adequately dealt with (Thompson and Perry, 1992) and the complexities of projects, locations and types of contracts are significant contributors to risks in construction projects (Ahmed et al. 1999). While some industries such as the oil industry commonly respond to assessed risk, the construction industry concentrates almost exclusively on trying to reduce financial risk and less on managing technical risk (Baker et al., 1999). Clients’ estimating policies usually focus on the preparation of unlikely to be exceeded but not excessively conservative estimates (Flyvbjerg et al., 2002). In the case of highway client organisations, this usually means that the estimate prepared at any stage of a project has a 90% confidence factor of not being exceeded at the cost-at-completion (Queensland Department of Main Roads, 2000).


4

Figure 1.2 shows the required estimate ranges that are normally required by clients in the stages of project estimates.

Source: Queensland Department of Main Roads (2000).

Figure 1.2: Stages of project estimates

Transportation projects have historically experienced significant construction cost overruns from the time the decision to build has been taken by the client (Molenaar, 2005). Construction cost estimating on major transport infrastructure projects has not increased in accuracy over the past 70 years. The underestimation of cost today is in the same order of magnitude that it was then (Flyvbjerg et al., 2002). High profile highway projects in the US, such as Boston’s Central Artery/Tunnel, known as the Big Dig and Virginia’s Springfield Interchange have made engineers, contractors and public taxpayers acutely aware of the problem of cost overrun. For example, the Big Dig was estimated at a cost of US$2.6 billion (1982 dollars) but was expected to be completed at a cost of US$14.6 billion (2002 dollars) with completion then anticipated in 2005 (NAS, 2003). Cost overrun of highway projects in Queensland, Australia, has been significant over a relatively long period of time. The Queensland Government’s Roads Implementation Program 2004–05 (Queensland Department of Main Roads, 2005) reports on the proportion of projects costing more than $1m that have had significant cost overrun by exceeded their programmed estimate by more than 10%. As can be seen from Table 1.1, cost overrun in highway projects have had a serious impact on program budgeting from the view of the client. On average, 1 in 10 highway projects of => $1m has significantly exceeding their decision-to-build budget (Queensland Department of Main Roads, 2005).

halla

This figure is not available online. Please consult the hardcopy thesis available from the QUT Library


5

Source: Queensland Department of Main Roads (2005)

Table 1.1: Highway projects over A$1m that exceeded estimate by >10%

Planning and programming future highway construction projects are vitally important tasks in highway organisations (Wang and Chou, 2003). A construction program outlines how highway funds are to be spent over time and any deviation from the stated program often brings a quick response from the public, the press and politicians. When this occurs, the highway organisation loses creditability and time is often taken defending deviation from the published program (Flyvbjerg et al., 2002). On the other hand, if a highway organisation can produce realistic program estimates, especially at the decision-to-build stage that it is able to abide by, then the agency's image can be enhanced. To produce accurate construction programs, three conditions need to be met. Firstly, an accurate assessment of the future funding (i.e. supply of funds) needs to be available. Secondly, the cost of individual projects needs to be accurately estimated. Thirdly, any potential project risks that can lead to cost increases when the facility is constructed are to be adequately identified and managed accordingly (Wang and Chou, 2003). Project owners, such as highway agencies, are usually engaged in specific types of construction projects with unique features. For example, highway construction projects are characterised by their linear complexity, with their greatest risk lying below ground level due

halla

This table is not available online. Please consult the hardcopy thesis available from the QUT Library


6

to the relatively larger footprint, as compared with say building structures (Halligan et al., 1987). Project risks can be derived by reviewing historical data and thus ensuring consideration is given to potential cost overrun (Touran, 2003). Historical data may be used as a guide; however estimators and project managers also use their experience and professional judgement to weigh the competing factors to arrive at the most likely value (Yeo, 1990). The analysis of project risks is a necessary step for the improvement of any given estimating system and can be used to diagnose trouble spots and to pinpoint areas where greater improvement can be obtained (Touran, 2003). Formal and informal databases usually exist for specific domains, such as in assessing risks or cost overruns in construction projects. Many of these databases are deemed commercial-in-confidence, particularly in highly competitive industries (Al-Bahar and Crandell, 1990). In-house historical databases are often inadequate or disjointed, unavailable, or supplemented with personal knowledge. These databases are company and/or project specific and are not usually able to be uniformly applied across new projects (Al-Bahar and Crandell, 1990). Models have been established showing cost influencing factors derived from past records of construction costs (Wilmot and Cheng, 2003). Extrapolation of past trends has been used to forecast future overall construction costs (Koppula, 1981; Hartgen et al., 1997), however such models are usually only used for short-term forecasting because of their reliance on the notion that past conditions and specifications are not always retained into the future.Completed construction project data from the building industry has been used with regression analyses to forecast the actual cost of projects (Skitmore and Ng, 2003). Models of cost overrun across processing projects have been derived from quantitative data from completed projects using factor analysis and multivariate regression (Trost and Oberlender, 2003). The level of project risk contingency in estimates has a major impact on their financial outcomes for clients. If contingency is too high it might encourage poor cost management, cause the project to be uneconomic and aborted, or lock up funds that is not available for other projects (Dey et al., 1996). On the other hand, if the contingency allocation is too low, then it may be too rigid and set an unrealistic financial environment, resulting in unsatisfactory performance outcomes (Touran, 2003). In some areas of the public sector, there is a tendency to remove contingency provisions in budget submission, as contingencies are often seen as fats — leaving no allowance to express anticipation of any project risk (Yeo, 1990). The most common method of allowing for uncertainty in estimating has been the addition of around 10% contingency to the most likely estimate of the known works (Burger, 2003). Hartman (2000) argues that this is an unscientific approach and a reason why so many projects finish over budget. The common method of allocating 10% contingency is regarded as overly simplistic and heavily dependent on estimators' faith in their own experiences (Yeo, 1990). The Department of Main Roads Queensland adopts a 10% contingency for all detailed design estimates unless there are other reasons why they should be adjusted (Queensland Department of Main Roads, 2000) and as shown in previous Table 1.1 could be seen as being significantly inadequate to cover the client risks in highway construction. 1.3 Research purpose


7

The purpose of this research is to establish the nature and extent of cost overruns of Queensland highway projects and to develop a method for improving budget estimating practices. The research purpose is also to develop a more definitive risk contingency allocation regime for overall highway projects that can supersede the arbitrary models present in most highway projects. Two research questions are posed:

1. What client risks are present during the delivery of highway construction projects in Queensland, Australia that lead to significant project cost overruns?

2. How does the amount of highway cost overrun in such highway projects correlate

with their project types, size, delivery processes and client project risks when historical project data are analysed?

1.4 Research objectives The objectives of the research are:

• To undertake the analysis of historic project cost overrun factors and project parameters to ascertain if the findings can lead to more accurate client budget estimates in the future by estimators making more realistic allowances in estimates for identified cost overrun factors.

• Develop model relationships between the measure of the percentage of cost overrun in highway construction projects and:

o construction risks o types of highway construction projects o project delivery methods o project location o project size.

1.5 Justification for the research Little research appears to exist that relates the client project risks in highway construction and their correlation to the actual completed cost of a project (Williams, 2003). Research in this area may find relationships in construction cost data that can be usefully and simply applied to estimating processes. There are models derived from simple linear regression, for example, that can be used to predict the completed cost of competitively bid highway construction projects using only the low bid as input (Williams et al., 1998). However, there appears to be little research that identifies risk factors in specific highway project types and their relationships to budget cost overrun (Williams, 2003). Little research exists that relates the incidences of highway project types and client project risks to cost overrun in actual completed projects. An understanding of the reasons for consistent cost overruns will allow clients to focus on problem areas and implement systems into program budgeting procedures which will lead to more realistic project budget estimates. Numerous factors affect project construction costs and most construction cost models developed in the past have used only a few of the many possible influential factors identified to date.


8

Research of this type has also been hampered in the past because adequate data has not been available. However, more data sources of completed projects now appear to be becoming available, particularly in the public sector. Once identified, post mortem analyses of these for project risks can ultimately provide the client with more confident final project budgets that are unlikely to be exceeded. The interrogation of in-house historical databases is probably the best source of data to assess risk occurrences or consequences of risk events and in many cases these databases are inadequate or disjointed, unavailable or supplemented with personal information bias (Al-Bahar and Crandell, 1990). These databases are company and project specific and may not necessarily be able to be uniformly applied to new projects. Research such as that for the Washington State Department of Transportation has identified risk factors that have strong association with project construction costs, indicating that cost overruns, expressed as a percentage of the original contract amount, has tended to increase with the size of the project (Hinze and Walsh, 1997). Williams (2003), in his research into highway contract overruns, identified the need to study further correlations between different highway project types and constructed project cost overruns. There is little evidence in the research to date that has identified such correlations in highway projects in Australia. As well, many research projects to date consider only the final outcome of contracts within the project and have not considered the client’s risks associated with the full project budget. That is, the failures that lead to cost overrun in the overall project — from the time of the client's decision-to-build is made until its completion. Clients require different contingencies for different elements of projects (Eden et al., 2005). The establishment of a range of contingencies can require a considerable amount of work by estimators and so a simple contingency across the board is included in order to acknowledge the difficulty of pinning down project uncertainty (Baccarini, 2004). This research aims at addressing this issue by providing clients with a cost overrun model which correlates risk contingency with highway project attributes. 1.6 Methodology This research adopts an historical analysis as the foundation to the methodology and is an important approach for presenting information (Kirszner and Mandell, 1992). As well, the use of historical data assists in providing an insight into current problems relating to cost overruns in highway project estimates through the examination of what has happened in the past.


9

Five stages form the basis of the research as outlined in Table 1.2 and are designed to provide answers to the research questions. Stage Research procedure

1 Literature research to determine research focus.

2 Establish data sources of highway construction projects.

3 a) Determine cost overrun factors from historic project data

b) Use factor analysis (principal component analysis) and factor rotation on cost overrun factors to consolidate data.

4 Use nominal group technique (NGT) to elicit, review and prioritise principal cost overrun risk groupings and highway project types.

5 Undertake data analysis and statistical modelling using multivariate linear regression analysis. Establish correlations between client risks causing cost overrun, project attributes and project programmed cost.

Table 1.2: Research procedures

1.7 Outline of the research – thesis organisation This thesis is organized into six chapters as follows: Chapter 1 — Background Chapter 2 — Literature review into project risk and cost overrun Chapter 3 — Research methodology Chapter 4 — Data analysis Chapter 5 — Discussions on findings, conclusions and recommendations This Chapter 1 begins the background phase of the research by providing the objectives, proposed methodology overview, scope and project organisation. Chapter 2 is a literature review from professional journals, books, internet searches and from interviews with highway construction delivery experts. Chapter 2 essentially provides a review of the current state of the art in construction risk, contract delivery methods and project cost estimating and cost control processes used in and its management and cost estimation of risk. Brief definitions of aspects of construction risks, procurement and the project management of projects are also contained within Chapter 2. Chapter 3 discusses the research methodology necessary to achieve the research objectives. The research method adopted is that of analysing historical data collected on highway construction projects. The research techniques of factor analysis (FA) using principal


10

component analysis (PCA) and rotation and then nominal group technique (NGT) are proposed that will consolidate data on the types of construction risks and the types of highway projects that are associated with client budget cost overruns. Multivariate regression analysis is then used to identify potential correlations between budget cost overruns and the various attributes and risks present in projects with excessive cost overrun. Chapter 4 contains the various data analyses of the historical project information. Factor analysis and then nominal group technique are used to initially break down the bulk of the client risks identified from a post mortem of historical project data. The data analysis uses multivariate regression analysis to investigate the statistical significance of the available data between documented client project construction risks, highway project types, project size, location, project delivery methods. These are then correlated against the size of completed project budget cost overruns. Chapter 5 discusses the findings from the data analysis carried out in Chapter 4 and develops conclusions and recommendations that are derived from the research and how the research objectives align with the findings. Both specific and general recommendations are provided, as well as recommendations for future research. 1.8 Delimitations of scope and key assumptions The traditional method of DBB is by far the most generic construction process and is predominately used in the highway construction industry in the majority of developed countries (Gould, 1997), however the construction of some projects are performed using various other contract delivery methods. Only projects developed using the DBB delivery process have been used for the purposes of this research. Government contracts like the QDMR are typically awarded either through procurement auctions or are negotiated price contracts with local authorities of internal workforce units. All the highway projects chosen in this research have contractor pre-qualification so as to ensure that all project constructors have demonstrated their ability to do similar highway projects in the past. As well, where appropriate, contractor surety bonding has formed part of the project delivery process. Cost growth, cost changes and cost overrun are considered to have the same meaning for the purpose of this research, and they can be defined as the difference between the final project cost and the cost estimate at the time of the decision-to-build after design for a particular project. The total project cost estimate for the client includes the estimated costs of all component activities from the initiation of the project proposal to finalisation. These include the cost of:

• developing the concept design and business case • conducting investigations and developing the design • detailing the design • acquiring land • altering public utility plant • construction • project administration and project handover.


11

The definition adopted for a client or project owner is an individual or organisation for whom something is built or delivered under some form of contract agreement. The Queensland Department of Main Roads (QDMR) is the client/owner and is the organisation for which the highway projects are constructed. It should be noted that some historic project data utilised in this research may be additionally influenced by long term economic conditions and contracting climates that may vary from time to time. The variation in such external project conditions over the seven year analysis period representing the project data may provide some influence over any correlation of results. Only highway construction projects are considered in the research. Other transport infrastructure and building construction are not included. This research study uses QDMR data for projects completed for the financial years from 1997 through to 2003. The data used is unique due to the project variables such as project estimate and final costs, project location, project delivery type, highway project type and reasons for project cost overruns. All project data used came from published sources within Queensland and all relate to projects that were initially estimated to cost A$1m or greater and whose final completed cost substantially exceeded their initial budget (by more that 10%). 1.9 Conclusion A focus of research effort in the area of project cost overrun will provide a more basic understanding of the linkages of risk to highway project delivery. The purpose of the research project is to carry out an empirical analysis in order to ascertain whether the data support the hypothesis that there is a statistical correlation between highway project cost overruns and project attributes. The research concentrates effort on the study of aggregate data about highway construction projects in order to identify common trends that occur. While the research finds a history of underestimation of costs and the over-optimistic assumptions about performance of a substantial number of project budget estimates, the research also specifically contributes to the body of knowledge in project construction management and cost estimating in the following ways. It has:

• combined risk assessment and expert elicitation techniques in investigating project management estimating and cost control issues when estimating highway projects

• provided a quantitative and qualitative project assessment that will help highway project decision makers define unforeseeable disturbances more reliably ahead of time, so that corrective measures can be better taken into account in project design and estimating

• provided an in-depth post mortem analysis of client risks that have led to significant project cost overrun and thus has strengthened the understanding of why highway projects can overrun substantially above project decision-to-build budgets.

• identified client risk factors that are aimed at simply providing a better understanding of the risk parameters and management contingency requirements in project, thus validating better risk contingency weightings in budget estimates

• utilised an established expert elicitation technique in the research project in a way that required minimal impact on the time of construction experts, but at the same time providing an acceptable outcome to a difficult elicitation requirement

• accounted for economy of scale in highway projects by proposing ranges of over and above % contingencies for varying sizes of highway projects.


12

CHAPTER 2 Literature review into project risks and cost overrun

13

CHAPTER 2

Literature review into project risks and cost overrun

2.1 Preamble The change in project cost, or cost growth, occurs as a result of many related factors all of which are associated with some form of risk (Flyvbjerg et al., 2003). Analysis of the reasons for project cost overrun of construction projects is a necessary step for the improvement of any given cost estimating system and can be used to pinpoint areas where the greatest improvement can be obtained. As part of this process, Chapter 2 provides a literature review of aspects of project delivery risks which often contribute to the potential for cost overrun. It begins with some basic terms and definitions, followed by a discussion on the nature of risk — its identification and its management, particularly in the construction industry. The review then looks at the concept of risk engineering, followed by aspects of expert elicitation of risk probabilities. It then investigates the significance of risk assessment in tendering and contract administration by using varying forms of project procurement used within the construction industry. It then goes on to look at the various components that can influence estimate cost accuracy of projects during project construction. This literature review then focuses on the various project delivery mechanisms used in project construction and the influence that they have on minimising and managing project uncertainty, particularly for clients. Reviewed also are project cost estimating and procedures that are used in minimising client risk and in reducing project cost overrun. Project cost estimating and also project contingency, with emphasis on highway construction projects are also considered. 2.2 Introduction The procurement of constructed facilities is a process which involves many complex and interrelated steps incorporating questions of value for money, probity and fitness for purpose. Over more than three centuries, a procurement process has been developed that focuses on clarity, separation of phases and a transparent independent bidding stage. This is often referred to as the ‘traditional’ project delivery process (Flyvbjerg et al., 2002). Unfortunately, it has some reported serious drawbacks. For example, the process management can be inefficient and take an extended time and so often sets up opposing stances between the project participants which can eventually compromise the measures of success of a project in terms of time, budget and technical performance (Sidwell et al., 2002). On the other hand, the main barriers to achieving project success are the changes in the project environment (El-Choum, 1994). The problem multiplies with the size of the project as uncertainty in project outcomes increase (El-Choum, 1994). Large-scale construction projects are exposed to uncertain environments because of such factors as planning, design


14

and construction complexity. In addition, the presence of various interest groups (such as the project owner, consultants and contractors) as well as resources (such as materials, equipment, project funding, climatic, economic and political environment and statutory regulations) all add to project uncertainty. Other factors contributing to uncertainty include the complexity of the project, the speed of its construction, the location of the project, and its degree of unfamiliarity (Ahmed et al., 1999). In the report, Rethinking Construction – Report on Construction Industry, Egan (1998) identifies the need for continuous improvement and establishes key performance indicators for measuring performance in the UK construction industry. One of the major causes of business failures is related to the client. Client-generated risk factors can be stated as a client's financial ability to meet the cost of the work, its claims record, changing needs, and the construction sophistication. In turn, these risks can put a strain on the contractor's cash flow and can increase the actual cost of a project during construction (Kometa et al., 1996). 2.3 Definitions The Project Management Institute (Project Management Institute, 2000:21) defines a project as:

a temporary endeavour undertaken to create a unique product or service: ‘Temporary’ meaning that every project has a definite beginning and a definite end: ‘Unique’ meaning that the product or service is different in some distinguishing way from all similar products or services.

Much earlier, Steiner (1969:16) defines a project as: an organisation of people dedicated to a specific purpose or objective.

Projects generally involve large, expensive, unique or high-risk undertakings that have to be completed by a certain date, for a certain amount of money, and deliver some expected or anticipated level of performance. These three criteria of success have become widely used. It captures the essential task of the project manager, and the essential trade-offs that they can make. Kohrs and Welngarten (1986:87) report seeing a sign: Good! Fast! Cheap! Pick any two. This analogy can also be true in the project construction sense, where there is usually a trade-off of some sort on at least one of these parameters by the project owner. McCoy (1986) has tried to develop an integrated success criteria based on this three-fold criterion. At its most simplistic, Avots (1984) suggests that schedule is most important early in the project, but during the project cost becomes most important and after the project only technical performance is remembered. Salapatas and Sawle (1986) define success to have been achieved only when three groups perceive success: the client (based on performance, budget and reputation), the contractor (based on profitability, reputation, client and public satisfaction) and the customer/public (based on environment, reliability and cost). Potter (1987) has found from experience that success and failure can in fact be very close and Sykes (1982) supports this by pointing out that many large projects have been saved from disaster only because of fortuitous circumstances.


15

The Project Management Institute (2000:22) defines project management as the: application of knowledge, skills, tools, and techniques to project activities in order to meet or exceed stakeholder needs and expectations from a project.

Project management invariably involves balancing competing demands of:

• scope, time, cost and quality • stakeholders with differing needs and expectations • identified requirements (needs) and unidentified requirements (expectation)

(Project Management Institute, 2000). In project development, the project client plays a very important role. The term client refers to that person or organisation investing in the construction of built facilities (Mak and Picken, 2000). The Project Management Institute (2000) defines ‘stakeholder’ as individuals and organisations who are actively involved in the project, or whose interests may be positively or negatively affected as a result of project execution or successful project completion. Dake (1992) examines the concept of risk from an historical perspective. In the seventeenth century, risk was defined as the probability of an event occurring, with a focus on either the losses or gains that the event would represent if it occurred. The interesting aspect of this early definition is that there was as much attention paid to benefits as to losses. This is a perspective that has been lost in the twentieth and now twenty-first centuries. In the popular sense, the term risk carries largely negative connotations of loss or harm that generally has implications of negative or adverse results from an uncertain event. For example, Fishburn (1984) calls a certain bad event 'risky' and Statman and Tyebjee (1984) see risk as being a high probability of failure. Jaafari (1990) on the other hand, sees risk as being the presence of potential or actual constraints that could stand in the way of project performance by causing partial or complete failure during construction and commissioning, or at the time of using the project. Risk has been defined by Chapman and Ward (1997: 58) as being:

the exposure to the possibility of an economic and financial loss or gain, physical damage or injury, or delay as a consequence of uncertainty.

By comparison, Young (1996) sees risk in the project management environment as being any event that could prevent the project realising the expectations of the stakeholders as stated in the agreed project brief or agreed definition. Adameitz (2003: 43) describes various aspects of risk as follows:

Risk can be viewed as a four-letter word. There can be economic boundaries from the addition of extra ‘overhead’ activities, and political/cultural boundaries from an unwillingness to acknowledge that a risk exists and must be mitigated. To prevent this budget pressure, risk management and mitigation activities need to be factored into the project plans from the very beginning of a project. Construction projects involve numerous unpredictable and complex processes.


16

Dias and Ioannou (1995) conclude that there are two types of risk: 1. Pure risk that exists when there is the possibility of financial loss but no possibility of

financial gain (e.g. physical damages) 2. Speculative risk that involves the possibility of both gains and losses (i.e. financial

and production risk). Hillson (2002) argues that the common usage of the word ‘risk’ only centres on the negative outcomes. Ward and Chapman (2003) argue that risk is often associated with adversity, things that may go wrong, and threats to projects. According to the Project Management Book of Knowledge — PMBOK (Project Management Institute, 2000), risk management is seen as the processes concerned with identifying, analysing and responding to uncertainty throughout the project's lifecycle. It includes maximising the results of positive events and minimising the consequences of adverse events. The Australian/New Zealand Standard 4360:1999 sees risk management as a generic framework for establishing the context, identification, analysis, evaluation, treatment, monitoring and communication of risk. Royer (2000) sees risk management as:

• deciding what is an acceptable risk • how the level of the risk can be brought down to a level that is acceptable • monitoring the reduction in risk after exposure control actions have been taken.

However, Royer also points out that having such a focus of risk on adversity means project risk management tends to focus more on processes and methods that reduce the effect of threats. The general conceptualisation of procurement has been expanded by McDermott (1999) into the context of project construction. Rowlinson (1999) furthers this systems view of procurement by including elements such as contract strategy, culture and finance. Kumaraswamy and Dissanayaka (1998) trace and link the definition of procurement to the action or process of acquiring or obtaining material, property or services at the operational level. They also define building procurement as being the amalgam of activities undertaken by a client to obtain a building. Construction procurement, on the other hand, is defined as being the framework within which construction is brought about, acquired or obtained (Kumaraswamy and Dissanayaka, 1998). A high degree of specialisation has evolved in the procurement of the various goods, materials and services in the construction industry. This has led to a network of supply chains that include multiple layers of sub-contractors and interlinked suppliers. The next section looks at risk in both the clinical and project sense, as well as at the various processes for identify and managing risks within projects.


17

2.3.1 The Nature of Risk While risk is fairly well documented in the literature, the terminology is not consistently applied across construction, project management, engineering, health and safety, environment, business and other industries (del Cano and de la Cruz, 2002). Risk can be classified as voluntary or involuntary, depending on whether or not the events leading to the risk are under the control of the persons at risk or not (del Cano and de la Cruz, 2002). In the theoretical sense, Cvethovich and Earle (1992) view risk not as an inherent quality of the physical world but as a representation of the interaction between physical and psychosocial characteristics with the assessment of risk involving judgements about what is valued. Kumamoto and Henley (1996) identify five attributes of risk. These are:

1. Likelihood 2. outcome 3. significance 4. causal scenario 5. population.

Uher (1994) identifies 34 individual risks and categorises them into a single model, referring to some as activity risks that may affect individual activities, while others were global risks that were common to all activities. The majority of risks Uher identifies are global risks. Rutgers and Haley (1997) developed a model that identifies four distinct phases of risks in a project:

• developmental risks – technical, commercial/financial feasibility • project economics, permits/authorisation, third-party intervention and political

change • construction risks – schedule, cost, performance, design changes, interest rate

escalation, consequential damage, force majeure/country risk, currency changes • operational risks – market changes, statutory changes, unrest/strikes, acts of God,

third-party liability etc. 2.3.2 Risk and uncertainty Decisions are concerned with variables which are normally classified as risks or uncertainties. Risks are unknowns, the probability of the occurrence of which can be assessed by statistical means (risks are usually insurable). Uncertainties are unknowns, the probability of the occurrence of which cannot be assessed (uncertainties are uninsurable) (Chapman and Ward, 1997). It is possible, however, for a decision-maker to assign a subjective probability to an uncertainty. As knowledge increases in conjunction with the amount and detail of statistical data, areas of uncertainty are progressively converted to areas of risk (del Cano and de la Cruz, 2002). The evolution of weather data and associated forecasting techniques is such an example (Fellows et al., 2002). Risk links strongly with the uncertainty of the probability and consequence of a risk event. Ayyub and McCuen (1997) point out that uncertainty has two types of origins — non-cognitive and cognitive. Non-cognitive uncertainty results from physical randomness. This type of uncertainty is normally dealt with by employing current statistical and probabilistic


18

science (Chapman and Ward, 1997). Cognitive types of uncertainty result from humans expressing subjective judgements. Blair (1999) uses a fuzzy set theory approach for the development of costs and schedules for complex engineering systems. Uncertainty always exists in the modelling and project management of complex construction projects and this uncertainty is due to the model representing real systems and is also attributed to humans who express risk in subjective terms. Raftery (1994) points out that risk and uncertainty characterise situations where the actual outcome for a particular event or activity is likely to deviate from the estimate or forecast value. As well, risks exist in projects because of their uniqueness and temporary nature and can impact on the project contractor and sub-contractors, stakeholders and project owner in a variety of ways. Leu et al. (2001) point out that during project implementation, many uncertain variables dynamically affect the project duration and the costs can thus change accordingly (del Cano and de la Cruz, 2002). 2.3.3 Risk management Risk management is the process by which clients and their project managers make decisions based on data generated in risk assessments. Risk management involves making educated decisions about different configurations, construction scenarios and operational parameters. The Australian/New Zealand Standard 4360:1999 defines risk management as a generic framework for establishing the context, identification, analysis, evaluation, treatment, monitoring and communication of risk. However, the standard is not prescriptive, but rather describes the systems and processes required for risk management. Tuohey (2002) points out that the use of this standard is an integral element of good management practice across an enterprise and makes the Standard an ideal choice as the framework for project risk management. It provides a uniform, structured system that can be used across an organisation and by which clients can better understand risk issues on individual projects. The management of risk is seen as an essential part of the management of projects. Strategies for mitigating risks on projects include(Turner, 1999; OGC, 2002).:

• reducing the uncertainty associated with the project • avoiding the risk by finding a different way of doing the project • abandoning the project • reducing the likelihood of the risk occurring or the impact on the project • transferring the risk to other parties such as contractors or insurance companies • accepting the risk and creating a contingency plan

Risk management is also dependent on numerous factors such as industry sector, the size of the project, and the stage of the project life cycle (Baker et al., 1999). To include some of these ideas for the management of risk, the risk register is often been seen as the starting point (Williams, 1993a). Also, Williams (1993b) provides a discussion of how the risk register can assist in the allocation of risk and the preparation of risk management plans. The use of project risk registers is often seen as an important step in the reuse of historical project information (del Cano and de la Cruz, 2002). They can be seen as repositories of personal knowledge or organisational memories where experiences about risks and responses are continuously recorded (Tah and Carr, 2001).


19

However, Williams et al., (1997) point out that the project risk register fails to capture the inter-relationships between risks and the systemic structure within the risks. This makes it an inadequate tool for the capture and representation of risks, and therefore they question the basis for analysis and decision making. Dey (2001) has identified the following as general benefits that can be achieved from the application of risk management in any type of project:

• issues of the project are clarified and allowed for right from the start • decisions are supported by thorough analysis of available data • structure and definition of the project are continually and objectively monitored • contingency planning allows controlled and pre-evaluated responses to risks that

materialise • clearer definitions of the specific risk associated with a project • encourages problem-solving innovative solutions to problems within a project • provides a basis for project organisation structure and appropriate responsibility

matrices • builds up a statistical profile of historical risk for modelling future projects.

2.3.4 Risk evaluation and acceptance Evaluating risk involves combining an event’s probability and its corresponding consequence. Several methods have been developed to assist in determining risk acceptance. A summary of these methods, as adapted from Ayyub and Wilcox (2000), is shown in Table 2.1.

Source: Ayyub and Wilcox (2000)

Table 2.1: Methods for determining risk acceptance

Kaplan and Garrick (1984) assert that the purpose of risk analysis and risk quantification is always to provide input to an underlying decision model which involves not just risks but

halla



20

also other forms of costs and benefits. Risk analysis is a systematic process for evaluating a risk at the systems level. Once risks are determined, the next process involves the modelling and quantifying risks under risk assessment. The following sections deal with these aspects. 2.3.5 Risk assessment Risk assessment provides qualitative and quantitative data to decision makers for later use in risk management and when project risk assessment is undertaken properly and implemented correctly, and in sequence (Ayyub and Wilcox, 2000). It enables continual improvement in project decision making and can increase the likelihood of the successful completion of projects to cost, time and performance objectives. It is an effective tool in meeting clients’ needs in project delivery (Halpin, 1998). A strong risk management program is essential in all project delivery strategies and is the central aspect of project management (Shepherd, 1997). On the other hand, some people do not even consider risk assessment to be a form of science, referring to it as ‘art’ or even ‘hocus pocus’ (Gregory, 1989). Jackson (1989) argues that risk assessments are as full of unknowns and value-laden assumptions as to lend such processes to be of no real benefit. 2.3.6 Risk assessment models A number of systematic models have been proposed for use in the risk-evaluation phase of the risk-management process. Kangari and Riggs (1989) classify these methods into two categories — classical models (i.e. probability analysis and Monte Carlo simulation) and conceptual models (i.e. fuzzy-set analysis). They noted that probability models suffer from two major limitations. Firstly, some models require detailed quantitative information that is not normally available at the time of planning. Secondly, the applicability of such models to real project risk analysis is limited, because agencies participating in the project often have problems with making precise decisions. Such problems are often ill defined and, thus, require subjective evaluations that classical models cannot handle. The risk assessment control process attempts to answer the following three questions derived from Kaplan (1991):

1. What can go wrong? 2. What is the likelihood that it will go wrong? 3. What are the consequences if it does go wrong?

In order to perform risk assessments, several methods have been created to help answer these questions. Each of these methods is suitable in certain stages of a project’s lifecycle. The characteristics of particular risk assessment methods are shown in tables 2.2 and 2.3.


21


Table 2.2: Quantitative risk assessment methods

Qualitative risk analysis uses expert opinion to evaluate the probability and consequence of interaction within a system. Table 2.3 details some qualitative risk assessment methods used


Table 2.3: Qualitative risk assessment methods

2.3.7 Risk control processes An essential function of the construction project manager is the control of projects and hence the control of risks. Risk monitoring is required so as to respond to events that occur over the course of a project. Risk control can be achieved through the updating of risk management

halla


halla



22

plans with new information, identifying alternatives to unplanned risk events, and by mitigating unplanned risks (del Cano and de la Cruz, 2002). Perry and Hayes (1985) suggest a checklist of risks that may occur throughout the life span of any project. Bent (1988), in introducing the concepts of control, asserts that the fundamental elements of control are the cost element and the project schedule. However, Might (1984), in carrying out a survey on the effectiveness of project control systems on high-risk US Department of Defence and NASA projects, reports that such control mechanisms are unclear and hence ineffective. Williams (1995) describes various risk identification and analysis tools used by researchers and practitioners in project risk management that can be used in controlling risk. Tummala and Leung (1999) developed a methodology governing risk identification, measurement, assessment, evaluation and control and have applied it in managing costs associated with risk for transmission-line projects. Most risk analysis reported in the literature so far has centred around analysing the duration of projects and many authors, such as Farnum and Stanton (1987), present the distribution of duration of activities as classical beta distributions. For example, Berny (1989) proposes a distribution for practical simulations. Turner (1999), on the other hand, suggests expert judgement, plan decomposition, assumption analysis and brainstorming for effective identification of risk factors in projects. Delphi technique is used by Dey (1999) for identification of project risk factors. 2.4 Risk engineering Risk, as defined previously, and in Kumamoto and Henley (1996), is generally thought of in terms of the possibility of suffering harm, typically resulting in negative consequences. However, in project management, the aim is to maximise the results of positive events and minimise the consequences of adverse events (Project Management Institute, 2000). Thus, in the project management context, risk should be thought of as being concerned with opportunities as well as threats or negative consequences. The Association for the Advancement of Cost Engineering sees negative risk as having consequences that adversely affect a project’s cost (AACE, 2000). Opportunistic risk is viewed as having the potential to improve or lower the project’s cost. Wang and Roush (2000) point out that risk management helps to limit the potential for negative consequences arising from uncertainties and also maximises the possibilities of results being better than target values. Cooper et al. (1985) suggests a ‘risk-engineering’ approach where systematic risk evaluation could be performed by subdividing a project into its major elements and analysing the risk and uncertainty associated with each in detail. A classic risk analysis process is shown in the upper tiers of Figure 2.1. The dotted box of risk engineering is shown to highlight this as a backdrop to risk assessment and risk management.


23

R

Source:(Cooper et al., 1985) Figure 2.1: Construction risk analysis

2.5 Qualitative defined According to the Oxford Dictionary of English (Soanes and Stevenson, 2003), the term qualitative is described as:

Relating to, or involving quality or kind.

Patton (1986:35) offers a more technical definition in the context of program evaluation by describing qualitative data as consisting of:

Detailed descriptions of situations, events, people, interactions, and observed behavior; direct quotations from people about their experiences, attitudes, beliefs, and thoughts; and excerpts or entire passages from documents, correspondence, records, and case histories.

2.6 Quantitative risk assessment Quantitative risk assessment relies on statistical methods and this quantitative approach is difficult to document because it relies on quality data. As well, each project is unique and what data exists is based on past projects that may contain significant uncertainty when applied to current projects (del Cano and de la Cruz, 2002). Literature on quantitative risk assessment appears to be limited for eliciting risks from experts. Charette (1989) attempts a structured approach for software projects, describing a risk estimate of the situation and an associated information gathering process. The classic treatment of quantifying probability distributions from experts by direct encoding is attributed to Merkhover (1987) who discusses the fact that experts tend to underweight distributional information or regress towards the mean. The simplest such

halla



24

approach categorises risks into high/medium/low probability and for this purpose can be defined arbitrarily. Construction simulation, fault tree analysis, fuzzy stochastic applications, risk premium and expected net present value are just a few of the many quantitative risk assessment techniques that can be used. These are explained in detail in the following paragraphs. 2.6.1 Simulation Ang et al. (1984) define simulation as being the process of replicating the real world on a set of assumptions and conceived models of reality. Simulation can be applied to construction, manufacturing, public health, transportation, business process re-engineering and a host of other industries (Banks et al., 1996). Simulation is one method to quantitatively assess project construction risks. It is normally used to represent large, complex projects or systems because it is much less expensive to experiment with models than real systems. Simulation is particularly useful for studying systems in the design stage, because it is good for predicting performance or cost of a proposed facility to be built. Simulation is also particularly suited to construction because it allows experiments in construction operations to evaluate potential impacts or improvements to costs and schedules (Halpin, 1985) and assumptions made in building a simulation model can be changed to investigate ‘what if’ questions about the real project or process. As new information becomes available during the life of a project the model can be updated to provide a better simulation of the final costs. Discrete event (DE) simulation is the particular modelling of a system as it evolves over time in which state variables change instantaneously at separate points in time (Law and Kelton, 1991). As an example, the simulation of the process of bridge concrete components being manufactured has the discrete steps of:

• an order arrives at a concrete precast yard • component moulds are manufactured • multiple step casting processes • inspections • shipping to the bridge site.

At each point in the process the variables change, that is from raw material to finished components or structure. A construction simulation that is built on potential construction scenarios can identify risky elements, resource constraints and potential bottlenecks (Mak and Picken, 2000). As well, it can define sequences, construction times, facilities, materials, transport, labour etc. Simulation is also well suited to calculate the consequences of a potential risk event that may result in a cost escalation. For example, the environmental concern that occurs when building on former industrial sites through the uncovering ground contaminants. The subsequent risks of schedule delays and cost escalations can be approximated through the use of simulations of various rehabilitation treatments (Hegazy and Ayed, 1998).


25

2.6.2 Artificial logic applications Artificial logic applications can be developed to consistently emulate the way humans think and judge by following accountability rules. Fuzzy sets and logic are used to capture qualitative domain expert opinion for developing affordable project schedules and budgets. Although this technique cannot substitute for deterministic scheduling and costing methods, it does complement modelling methods in cases where vague and incomplete project information is present. Blair (1999) explains that expert opinion can be used to model construction simulations using fuzzy stochastic techniques. Applications include:

• estimating project cost (Paek et al., 1993) • developing a risk-based cost and schedule (Blair, 1999) • developing the schedule (Ayyub and Haldar, 1984) • performing risk-based decision making (Blair et al., 2000)

The use of fuzzy theory is particularly suited to construction issues where there is a severe lack of realistic historical data (Hegazy and Ayed, 1998) 2.7 Assessment of probabilities and consequences There has been a considerable amount of research and published material, on the meaning of probability. Schafer (1976) defines two types of probability. Firstly, aleatoric probability, relates to the outcome of an intrinsically uncertain situation (from the Latin alea, meaning dice). Secondly, epistemic probability relates to a measure in belief in a proposition, or more generally to a lack of complete knowledge. This is also referred to as the Dempster – Shafer Theory of Evidence (Shafer, 1976). In general, epistemic risk constitutes the biggest problem in project planning and risk prediction, where there is little historical evidence on which to base predictions (del Cano and de la Cruz, 2002). The definitions of probability and risk have implications for how these are perceived by project owners. Key work on the perception of uncertainty has been carried out some time ago by Tversky and Kahnernan (1981). Around the same time, MacCrimmon and Wehrung (1986) describe a study of managers' attitudes to taking risks and how they choose options and risk action/outcome benefits in standardised risk situations. Fellows et al. (2002) point out that a decision will have a range of possible outcomes between quite well-defined extremes. They point out that it is not likely that the probability of the occurrence of each possible outcome is equal everywhere within the range. The probabilities may be represented as a graph of probability against possible outcomes and is referred to as a probability density, with the normal distribution curve being the most common example (Fellows et al., 2002). In a perfect risk scenario, an objective assessment of probabilities and consequences can be exactly determined (Kumamoto and Henley, 1996). Therefore, the best method of establishing associated probabilities and consequences is through the use of representative data. However, this is difficult in the construction industry because each project is unique in form, function and the resources that build it.


26

Elkington and Smallman (2002) make the point that, in approaching risk management, managers have the ‘luxury’ when working in the world of financial risk of vast statistical databases to instruct probability. Whereas project managers outside of finance, who face operational risks constantly need to decide the chances of an identified risk occurring. Some use historical project data, and others use unwritten past experience. But in many cases, the likelihood of a risk occurring is derived by means of an educated guess (Elkington and Smallman, 2002). A quantitative approach to construction risk assessment is hard to document because these methods rely on construction data that is often difficult to obtain because each project is unique. Data that is based on past projects may contain significant uncertainty when applied to current problems. Databases, either formal or informal, usually exist for specific domains, such as in assessing risk in construction projects. Many of these are commercial-in-confidence, particularly in highly competitive industries such as the oil industry. Two early examples of established databases of project risks are Niwa and Okuma (1982). They describe a well-structured database in use at Hitachi, and the value of the database for knowledge transfer of project risk. Ashley (1987) describes a number of examples of expert systems that are based on risk knowledge. In-house historical databases are probably the best source of data to assess risk event probability or consequence. However, in many cases these databases are inadequate or disjointed, unavailable, or supplemented with personal knowledge (Al-Bahar and Crandell, 1990). These databases are company and/or project specific and are not usually able to be uniformly applied across new projects. While industry–average data exists for the some construction operation variables, (say for excavators), a firm’s own historic database can provide improved accuracy in forecasting production risk and hence costs. Such databases can be developed by project owners, as well as contactors, and can be used for future projects and comparison with industry averages. However, many organisations do not maintain such detailed databases. For example, Iseley and Gokhale (2003) provide an example where the equipment selected for the land clearing and grubbing activities for a project was based on only the estimator’s own experience. Production estimating is extremely effective when experienced estimators are responsible for making the necessary productivity risk decisions. However, more detailed databases would permit decisions to be made at a lower level by less experienced people without sacrificing accuracy (Iseley and Gokhale 2003). This would allow the more experienced estimators to use their time more effectively. In addition, more detailed databases provide better information to substantiate the impact if a change in conditions should develop (Iseley and Gokhale 2003). When available, government-collated data is generally overly broad and may be used to gauge or forecast overall trends. For example, the number of accidents per thousand hours of work can be broken down by specific industry, and yet, this data is difficult to apply to specific construction project risk probabilities as the data usually considers all types of construction, from residential, road-building, heavy-civil, to large commercial developments.


27

Databases for construction projects may be available from in-house governmental agencies, such as the Australian Bureau of Statistics (ABS), from industry sources or from published academic literature. 2.8 Elicitation of risk In risk assessment, probability theory is used to represent engineering uncertainties. However, uncertainty is a vague concept as it refers to events that occur with periodic frequency, such as demands on equipment and resources, as well existing but unknown conditions, such as poor ground foundations (Mak and Picken, 2000). By contrast, probability is a precise concept having a mathematical concept with an explicit definition. The mathematics of probability theory is used to represent uncertainties, despite the fact that such uncertainties take many forms. Uncertainty versus the specificity of probability is particularly significant in the discipline of project management where subjective methods are better suited for the assessment of risks. However, this technique requires an application of personal knowledge or documented research and, in certain situations, the application of standard probability distributions may represent the appropriate risk assessment (del Cano and de la Cruz, 2002). Deriving probability values for different cost elements of an estimate was first suggested by Hertz (1964) as a means of reducing uncertainty. It was recommended as an integral part of project appraisal by the World Bank, over 30 years ago (Reutlinger, 1970). With this method it is possible to quantify the range of a particular cost estimate. Probability analysis, in the context of cost estimating, entails assigning estimated values of each cost element and analysing the risk associated. The individual probability distribution of each cost component is then aggregated so as to derive the overall probability distribution of the total cost. 2.8.1 Expert elicitation While risk assessments have a place in risk decision-making, it is increasingly difficult to argue that this should be the sole basis. Freudenburg and Pastor (1992) have found risk analysts guilty of evoking the tyranny of illusionary precision, by conveying an impression of a higher level of accuracy and confidence than is actually warranted. In fact, there is a growing body of evidence which suggests that risk experts may be subject to the same foibles and judgemental errors that affect the general public (Freudenburg, 1988). Such errors can include:

• failure to see the way systems are interrelated • failure to predict the cumulative impact of individually minor problem • overlooking non-technical aspects of a technological system or those aspects outside

their area of expertise • insufficient attention paid to the sensitivity of assumptions and the problems of small

sample size • conscious decisions to simplify analysis by excluding low-probability events from

consideration • over-confidence in the reliability of analyses.


28

Expert elicitation is a formal way of obtaining information or answers to specific questions about certain issues where the information is highly subjective. The process is used by quite a number of disciplines. The expert elicitation process consists of the following steps (Ayyub, 1997):

1. selection of issues 2. selection of experts 3. issue familiarisation for expert 4. training of experts 5. elicitation of experts about the issues 6. aggregation and presentation of results 7. discussion and revision by expert 8. revision of results and reporting.

Expert elicitation is a valid method to develop risk assessments and is the formal process of obtaining assessment probabilities and consequences when the information is subjective. The expert elicitation process needs to be systematically structured and can use a participative method such as Delphi or modified Delphi techniques to quantify probabilities and consequences (del Cano and de la Cruz, 2002). Personnel with a risk-analysis background that are familiar with the construction, design, operation, and maintenance of projects need to define these issues in the form of specific questions. 2.8.2 Expert elicitation using the Delphi method The Delphi technique (Linstone and Turoff, 1975) can be used to anonymously elicit the opinions of experts concerning risks. It provides feedback to experts in the form of distributions of their opinions and reasons. They are then asked to revise their opinions in light of the information contained in the feedback. This sequence of questionnaire and revision is repeated until no further significant opinion changes are expected. The technique is designed to protect anonymity of the experts’ opinions and reasoning. Sackman (1972) points out some shortcomings of the Delphi and these can be characterised as:

• The information and questions provided to experts need to be carefully reviewed to ensure objectivity

• It is difficult to summarise and present to the group a common evaluation scale that is interpreted uniformly by the experts

• The benefit of experts participating in active dialogue is missed • It can be difficult and time consuming to explore disagreements between experts.

For the rapidly paced project management world, any expert elicitation method needs to be efficient because project managers are continually confronted with not having enough time for all the activities required of them (Laufer, 1996). 2.8.3 Elicitation using semi-structured interviews The purpose of semi-structured interviews is to extend the scope of a study to a wider range of exponents and to test the conclusions drawn from exploratory work. This can be achieved by developing an outline list of questions developed for consideration by the interviewee with key questions about the issues being researched. The technique of personal construct


29

elicitation, as described by Stewart and Stewart (1981), has been used in project management technique assessment. Bannister and Fransella (1989) define a personal construct as being a bipolar distinction, or scale, which is used when contrasting different people, objects or situations. Semi-structured interviews can provide opportunity to evaluate tacit knowledge based on the experience of individual practitioners. Koskinen, Pihlanto and Vanharanta (2002) explain tacit knowledge as that which expresses itself in human actions in the form of evaluations, attitudes, points of view, commitments and motivation 2.8.4 Nominal group technique Originally developed as an organisational planning technique by Delbecq, Van de Ven and Gustafson in 1971, the nominal group technique is a consensus planning tool that helps prioritise issues (Delbecq et al., 1975). In the nominal group technique, participants are brought together for a discussion session led by a moderator. After the topic has been presented to session participants and they have had an opportunity to ask questions or briefly discuss the scope of the topic, they are asked to take a few minutes to think about and write down their responses. There are three main advantages of nominal group technique:

1. Voting is anonymous 2. There are opportunities for equal participation of group members 3. Distractions in the form of communication ‘noise’ that can be inherent in other

group methods are minimised. Some disadvantages of nominal group technique include:

1. Opinions may not converge in the voting process 2. Cross-fertilization of ideas may be constrained 3. The process may appear to be too mechanical.

2.9 Organisational risk culture When considering risk in terms of monetary outcome, there are three general attitudes toward risk (Kumamoto and Henley 1996). These are:

• risk-aversive • risk-neutral • risk-seeking.

These attitudes are shown in Figure 2.2.


30

Source: Kumamoto and Henley (1996) Figure 2.2: Risk-aversive, risk-neutral and risk-seeking

An example of this can be seen in say an individual’s investment practice. Some investors prefer to buy stock of small companies in the high technology sector. Typically this is considered a risk-seeking stock choice because there is a chance of losing an initial investment but the potential monetary gain can be quite substantial. Economists agree that those who take over or bear the risk have to be rewarded for doing so. Begg et al., (2000) comment that many economic activities consist of:

the more-risk-averse bribing the less-risk-averse to take over their risks. A very early study by Reutlinger (1970) reports on the results of the attitude to risk taking of US executives in relation to corporate investment decisions. It showed that, although there were widely different attitudes towards risk taking, even within the same corporation, there was a tendency for most of those surveyed to be conservative when assessing risk.

In the project management context, it is generally reported that large established companies are risk-aversive, governmental ventures are risk-neutral to risk-aversive, and smaller newer companies are risk-seekers. Therefore, risk analyses will account for the acceptability of risk according to an organisation's established risk attitudes (Kumamoto and Henley, 1996). Paul and Gutierrez (2005) point out that when a contractor is risk neutral, then the risk premium will not vary across contract type. They assert that, in general, a contractor will tend to be risk neutral if the contract is small relative to their total volume of business. In such cases, risk neutral decisions will maximise the contractor’s profit. However, if a contractor is risk averse, then he or she will tend to factor in a higher risk premium in a fixed price contract since more financial risk needs to be borne than in say cost-plus contracts (Paul and Gutierrez, 2005). 2.10 Project risk management Thompson and Perry (1992) show evidence from projects worldwide that project risks are not being adequately dealt with. Complexities of the project, location, type of contract, familiarity with the work and breakdown in communication are some of the significant

halla



31

contributors to risks in construction projects (Ahmed et al. 1999). Baker et al. (1999) report that in a survey of over 100 companies in both the oil and construction industries, companies commonly respond to assessed risk, however the construction industry concentrates almost exclusively on the reduction of financial risk and less on managing technical risk. Construction is an industry associated with many risks. Sampling of the most prevalent risk will convince any causal observer that the construction process is risky. The literature has several case studies of construction risk management from general building construction (Baker et al., 1995), offshore construction (Curole, 1995), or highway construction (Wang and Chou, 2003). In the field of project management, risk management is a recognised practice to help clients deliver projects on schedule and within cost (Project Management Institute, 2000). However, the risk management performed in the construction industry has traditionally been through the use of gut feel or a series of rules-of-thumb (Al-Bahar and Crandall, 1990). This may be due to a lack of understanding of the benefits and cost, the perceived difficulties, or cumbersome processes in developing risk management (McKim, 1992; Ward, 1999). In discussing the US construction industry, Kangari (1991:46) supports this notion by pointing out that:

Most managers rely primarily on their judgements, rules of thumb, and expertise and that the conventional algorithmic models developed for risk analysis are not generally accepted by management for decision making under uncertainty.

As well, Dalchar (1993) in Williams (1999: 273) claims that:

Contemporary project management practice is characterised by late delivery, exceeded budgets, reduced functionality and questioned quality. As the complexity and scale of attempted projects increase, then the ability to bring these projects to a successful completion dramatically decreases.

Since the early 1990s, a variety of risk management processes have been written about. These include Al-Bahar and Crandall (1990), Del Cano (1992), BSI (1999), NASA (Rosenberg et al., 1999), and the US Department of Transportation (FDOT, 2003). De Cano and de la Cruz (2002) have described the most noteworthy, comprehensive, and sound project risk management processes. These include:

• PRAM — this was the first highly comprehensive process developed by a large team, including both practitioners and academics (Simon et al., 1997; Chapman and Ward, 1997)

• RAMP — this risk management process has characteristics similar to those of the PRAM process in scope, structure, and conception but importantly has been conceived for the construction environment. RAMP has been developed jointly by the Institute of Actuaries and the Institution of Civil Engineers to assess all kinds of risks and uncertainties so that they can be identified, evaluated, reduced and controlled (ICE, 1998).

The Project Management Institute (2000) points out that both PRAM and RAMP tend to reflect more a British – European way of performing project risk management.

• PUMA (Project Uncertainty Management) — This is an integrated methodology based on a hierarchically structured, flexible, and generic project risk management


32

process for construction projects from the point of view of the owner for individual projects (del Cano and de la Cruz, 2002).

Project construction risk can have physical or capability related aspects (Zack, 1997). Physical risks are those events that prevent one from completing the project or increase the costs and schedule such as acts of God, weather, impracticability, or other things that are beyond the control of the project team. Capability related risks are those that interfere with performing the work but management has a choice in minimising the risk such as poor quality, safety and equipment selection. Project Management Institute (2000) classifies risks as:

• external (uncontrollable) • internal (controllable).

Project size can be a major cause of risk, as can other factors such as:

• the complexity of the project • the speed of construction • location of the project • degree of project unfamiliarity.

Edwards (1995), Smith and Bohn (1999), and Kim and Bajaja (2000) list potential construction risks. Table 2.4 details some of the most common risk sources in construction projects.

Risk source Description of risk source Cost Estimate is uncertain because it is based on past and projected costs Schedule Schedule is uncertain because it is based on past and predicted performance Labour Labour strength and productivity uncertainties Project management Uncertain experience levels, team cohesiveness and composition

Safety Potential for accidents and consequences of injuries or higher costs Change orders Potential increased cost, schedule delay, and poor technical performance Unforeseen Conditions

Undefined underground or hidden site conditions that can cause cost and schedule growth

Environmental concerns

Regulatory approvals and mitigation of environmental concerns may cause time delay or cost escalation

Inflation Potential for material and labour price increases Weather Delay causing costs and technical non-performance from adverse weather Construction Complexity Level of difficulty increases the potential for cost and schedule growth

Fire Probability of fire hazard from work operations, vandalism or lightning

Suppliers Non-performance from vendors, sub-contractors or suppliers that can cause impacts to cost and schedule

Property loss Potential for loss due to flood, fire, theft, sabotage or vandalism Design Incomplete or lack of design elements that considers construction aspects Quality Potential for consequence of poor quality and technical non-performance Political Potential loss of support leading to less opportunity to acquire new projects Table 2.4: Sources of common project construction risks


33

One of the main sources of construction risk derives from the site conditions in which projects are planned to be constructed. The next section looks at project site conditions as sources of risks. 2.11 Project site condition risks A major risk in any civil engineering project is that the construction may encounter physical obstructions or conditions on the project site which were unexpected and unforeseeable at the time of making the decision to build the project and which may delay work or cause increased costs. Highway construction projects, for example are characterised by their linear complexity and they have intrinsic design parameters such as minimum pavement subgrade strength conditions that need to be satisfied throughout the whole of their project footprints. Ground conditions may be substantially harder or softer than anticipated and sub-surface water may be discovered where none was foreseen. In bridge structures, sound foundations or rock may be found only at substantially lower levels that anticipated in foundation designs (Queensland Department of Main Roads, 2000). Studies from around the world have consistently highlighted the recurring frequency of claims for unforeseen ground conditions. In Canada, Revay (1992) reports that inadequate site and/or soil investigations and unexpected ground conditions were among the six most frequent causes of claims. A study in Hong Kong by Kumaraswamy (1997) aimed at identifying root causes of claims for extension of time and extra payments on construction projects, found that unforeseen ground conditions was ranked fourth in the top 10 common categories of construction claims. In the US, studies by Halligan et al. (1987) on Federal Highway Administration funded state highway construction projects indicate that claims for ground conditions accounted for approximately 35% of the total dollar amount paid to contractors for claims. The traditional position on site information has been that there is no obligation on the owner to provide the contractor with any information on the nature of the site and sub-soil conditions (Mak and Picken, 2000). It is therefore the responsibility of the contractor to investigate these matters and to be satisfied with the feasibility of constructing the works on the designated site. Even if there is relevant information in the owner’s possession, there is no obligation in English law to disclose it to a contractor. There are a number of civil law judgements which establish precedence in this area:

1. Thorn v. London Corporation (1876) 1 App Case 120 2. Dillingham Construction v. Downs (1972) NSWLR 49 3. Morrison-Knudsen International v. The Commonwealth of Australia (1972)

13 BLR 114. On the other hand, some jurisdictions impose such an obligation only in special circumstances. For example, US public project owners have an obligation to supply site information to the contractor where the circumstances are such that the contractor cannot otherwise obtain it (Glower, 1990; Smith, 1995). If the owner decides to provide such information and the information is incorrect, then the contractor may have remedies against the owner for breach of warranty, misrepresentation or negligent misstatement.


34

In Great Britain, legislation gives the contractor some protection against exclusion of liability for inaccurate information even if it was not provided fraudulently such as that of Section 3 of the Misrepresentations Act 1967 (as amended by Section 3 of the Unfair Contract Terms Act 1977). However, because of the uncertainties of geotechnical engineering it has been suggested that such riders would be effective in excluding the normal consequences of misrepresentation, provided that the owner has not been careless or fraudulent (Wallace, 1995). In judging the physical site conditions, the contractor is assumed to have taken into account:

• the site information • publicly available information referred to in the site information • information obtainable from a visual inspection of the site • other information which an experienced contractor could reasonably be expected to

have or to obtain. Ndekugri and McDonnell (1999) point out that it is commercially impracticable for tenderers to carry out full geotechnical investigations even if there are no time constraints in the particular bid. In general, a contractor’s entitlement operates on the basis of the test of foresee ability. If the physical conditions or obstructions are not foreseeable by an experienced contractor then the contractor may claim. Jones (1996) notes that there has been no widespread acceptance of foresee-ability as a general philosophy of risk allocation, but there has been a strong international acceptance of alternative philosophies such as those put forward by Abrahamson (1984). By contrast, Eggleston (1996) suggests that a contractor can be compensated for time and money if the physical conditions are such that an experienced contractor would have decided at the time of entering into the contract that they had such a small chance of occurring that it would have been unreasonable to allow for them. Shadbolt (1990) believes that this is as it should be because it is reasonable for an owner to expect a contractor to exercise their skills and judgement and for the owner to rely on them in that respect. ASCE (1989), Smith (1995) and O’Reilly (1995) all propose that risk allocation should be motivational and risks should be allocated to the party with the greatest opportunity to influence the magnitude of the risk. With regard to ground condition, this is with the project owner. It is their site and they can commission a thorough site investigation that will, within limits, sensibly define the ground conditions and safeguard against the risks. Attempts have been made in contracts to limit the contractor’s entitlements to make claims on account of climatic/weather conditions. Climatic conditions on site have been removed from some prominent contract provisions because it has always been difficult to assess whether such conditions could or could not have been foreseen by an experienced contractor (Seppalla, 1991). However, while the contractor may not claim for costs in such cases, a claim for an extension of time for ‘exceptionally adverse climatic conditions’ may be made under the appropriate clause. In some standard contracts like the NEC, a contractor can claim for weather conditions that occur, on average, less frequently than once in 10 years, but their entitlement includes both costs and time.


35

2.12 Managing risks through procurement practices All construction projects are, by their very nature, economically risky undertakings and so projects let on the basis of competitive tenders can add to such risks. Smith (1995) talks about a quantity surveyor writing over a century ago when Blake (1900) reported:

Responsibilities are sometimes thrown on the builder when estimating, by leaving to his discretion some matters of doubtful yet ascertainable value; a matter which little investigation on the part of those arranging the contract would free from any doubt or uncertainty whatever, and thus enable the competing builders to price it at fair value instead of allowing a good margin to cover such contingency. Every point on which there is any doubt, and every matter as to which there may be the slightest misunderstanding during the execution of the contract should, in justice to all parties, be thoroughly threshed out before the contract is signed.

Construction industry studies, such as by Latham (1994) and Egan (1998), echo an underlying lament that can be traced back many decades to the Simon Report in 1944, the Emerson Report in 1962, the Banwell Report in 1964 and the Tavistock Report in 1966. These all point out many problems arising from poorly structured project procurement systems. The procurement of constructed facilities is a process which involves many complex and interrelated steps. Along the way there are questions of value for money, probity and fitness for purpose. Over more than three centuries, the “traditional” procurement process has been developed that focuses on clarity, separation of phases and a transparent independent bidding stage. Unfortunately, there are some serious drawbacks. For example, the process can be inefficient and take a long time and often sets up opposing stances between participants (Sidwell et al., 2002). However, the traditional process does have some notable characteristics which continue to hold relevance in today’s climate. These characteristics are illustrated in the RIBA Plan for Work (RIBA, 1984) which emphasises the step by step functional contribution and progressive definition, the output from each stage being the input into the subsequent stage. The clarity of this process is particularly attractive to clients who need to demonstrate probity. Project procurement processes in the Australian construction industry are largely derived from the United Kingdom where the traditional process which separates design and construction, and awards the construction to the lowest bidder, has been developed over the centuries, particularly since the industrial revolution. The traditional approach to project management is shown in Figure 2.3 below.


36

Source: Queensland Department of Main Roads (2000).

Figure 2.3: Traditional project delivery model

2.13 Risk distribution in project procurement A most important aspect of risk for either client or a contractor is: ‘Who is liable for the risk, and who, thus, has the motivation to avoid or vitiate the risk?’ (Curtis et al., 1991). Therefore, the contract is at the heart of any analysis of risk. A prime function of any contract is to identify, assess and allocate risk (NEDO, 1982). Ownership of a risk implies responsibility for the management of that risk as well as responsibility for bearing its consequences. Chapman and Ward (1997), however, point out that in certain circumstances it may be important to distinguish between responsibility for managing a risk and responsibility for bearing the consequences of the risk. In particular, they assert that it may be desirable to allocate these responsibilities to different parties, recognising which party is best able to bear the consequences of that risk. Thus, while one party, perhaps a contractor, may be best placed to manage a source of risk, it may not be appropriate or desirable for that party to bear all the associated financial risk (Chapman and Ward, 1997). Wang and Chou (2003) assert that risk management needs to be made more efficient and effective so all parties can understand:

• their respective responsibilities • risk event conditions • risk preferences • risk management capabilities.

They also point out that if a project contractor has different perceptions of risk allocation to those of the client or even a lack of clear understanding of risk management, then the contractor will inappropriately manage the risks in construction projects by assuming that the risk events or consequences are not the contractor’s responsibilities. On the other hand, Carmichael (2000) argues that there may be an overall saving to the project client by accepting some risk. Generally client expect that contractors bear some risk,

halla



37

though some take this to an extreme and, through contract condition mechanisms, require contractors to bear essentially all risks. Contracts can be considered as having three main functions (Barnes, 1981):

1. Work transfer — defines the work which one party will do for the other 2. Risk transfer — defines how the risks inherent in doing the work will be allocated

between the two parties 3. Motive transfer — implants motives in the contractor party, which, ideally, should

match those of the client party.

There is a basic conflict between the risk and motive provisions in most contracts. If a risk is placed on the contractor, they have a strong motive to minimise that risk, whereas the client has no such motive. Many standard forms of contract hardly mention risk and frequently contracts are not clear on who bears the risk. Robinson (1987) claims this as the strength of design-and-build contracts A survey by the Victorian Government, Australia (Victorian Government, 1999) provides some interesting insights into the strategic decision making by various parties in the allocation of risk in public projects. A factor that tends to defeat the efficient allocation of risk in construction contracts is that project owners may have significantly more power at the contract formation stage than does the contractor. This power can be derived from a number of sources, such as:

• the competitive bidding process, with unreasonable time pressures and often involving a large number of bidders

• in-house risk management and legal expertise which greatly exceeds that of the contractor

• a general reluctance by some contractors to price adequately for the risks that they are being required to accept.

The joint use of formalised risk management processes can provide all parties with a way out of this bind by providing a revised contract form of potentially greater benefit to both the contractor and owner. However, Tuohey (2002) points out that most contracts are drafted unilaterally by the owner and risks are allocated without the benefit of a risk management process. Tuohey (2002) also reports that, in some Australian Government Agencies, it is now a common requirement for the tenderer to include draft risk management plans for the entire project. He adds that it is easy to believe that in the near future, risk management will become a necessary and formal process in the execution of all projects. More projects will be likely to succeed as a result. Construction contracts are the written agreements signed by the contracting parties which bind them and also define relationships and obligations. In any particular contract, the project owner’s goals can best be achieved by selecting the contract type that will most effectively motivate the contractor to the desired end and is also dependent on completeness of information for the bidder(s) at tender time and the extent the owner wishes to take specific risk (Zaghloul and Hartman, 2003). Given the opportunity, an owner should favour


38

efficient allocation of risk between parties to a project that simultaneously reduces risk and improves performance. The contract terms define how such risk is distributed between the owner and the contractor. In establishing the context of a contract which is composed of specifications and clauses, both owners and contractors strive to understand each other’s goals and capabilities, and risks should be allocated accordingly (Akinci and Fischer, 1998). However, a comprehensive empirical study about the allocation of risk in contract clauses conducted by Ibbs and Ashley (1987) concluded that on an aggregate basis, owners are at risk 11% of the time, contractors are at risk 46% of the time, and both parties are at risk 43% of the time. These results confirm that owners use contracts to minimise their risk exposure. Liabilities and responsibilities of each contracting party are allocated through the conditions of contract. It has been postulated that inappropriate and unclear risk allocation among the contracting parties generates avoidable construction claims and disputes (McGowan, 1992; Fisk, 1997; Kumaraswamy, 1997). One of the main areas where risk management can be applied is in developing clearer and more appropriate conditions of contract that provides clearer definition of the risks and hence lead to their proper allocation and management during the construction process. However, contract language alone is not sufficient to clearly specify risk apportionment between the contracting parties as contract clauses can be interpreted in different ways by different parties (Hartman et al., 1997). Zaghloul and Hartman (2003) report that appropriate risk allocation is a significant contributor to low transaction costs in specific projects and is a vital issue in the success of the contracting process. On the other hand, Gransberg and Ellicot (1997) point out that, in an owner–contractor relationship at least, a common aim of owners appears to be to avoid risk as far as possible by allocating as many risks as it can to the contractor. Carmichael (2000) notes that a general belief is that the owner should, wherever possible, define the work as carefully as possible beforehand, irrespective of the type of contract used so as to reduce the risks to all parties. The appropriate contracting method and the contract documents for any construction project depend on the nature of the project, but an appropriate contracting method coupled with clear and equitable contract documents do not by themselves ensure project success. This is especially the case where parties work together in the face of uncertainty and complexity, whilst having diverse interests and conflicting agendas. The attitudes of the contracting parties and the co-operative relationships among the project participants are important for successful project delivery. The theme of risk allocation in procurement methods is seen as a critical issue in the selection of construction contracts. Latham (1994) points out that clients often prematurely select procurement methods without consideration of the appropriate division of risks. The risk involved could be physical in nature but is more likely to be mercantile (Langford et al., 2003). This supports Taylor’s (1993) argument that consideration of risks at the procurement stage could save money and time and also improve quality. As well, Smith (1995a) has confirmed that inappropriate risk management can also lead to higher costs for the client. According to Hibbert et al. (1990), procurement choice advice is often made to clients even without hard factual evidence of judging the successes of previous procurement decisions.


39

2.13.1 Risk allocation through standard-form contracts Any construction project involves risk and there is no possibility of eliminating that risk. All that can be done is to regulate the risk allocated and then to properly manage the risk. This is usually done through the language of the construction contract. Not only are procurement issues a factor influencing risk, but, as Uff and Odams (1995) noted, contractual risks are seldom analysed and quantified. Hence it is important that clients select appropriate procurement routes and contract strategies which fit the client’s ambitions for a project. Invariably, this will include factors which will deliver value for money (Langford et al., 2003). Kozek and Hebberd (1998) point out that decisions regarding risk sharing or risk shifting are made within the context of a project owner’s contracting policy. Hughes (1997) asserts that the purpose behind using standard-form contracts is to allocate risks fairly between the parties Zaghloul and Hartman (2003) define one way in which the contracting parties can attempt to address the right responsibilities for risk by dealing directly with the issue of legal liability arising from certain contract clauses such as disclaimer (exculpatory) clauses. Such clauses attempt to transfer one party’s risk (which may be legal liability) to another by contractual terms (Hartman, 2000). The use of disclaimer clauses to allocate risk has been identified by studies and industry practice as a key reason for increasing the overall cost of a project (Khan, 1998; Zack, 1996). When a risk is shifted to a contractor and the contractor has no means by which to control the occurrence or outcome of the risk, the contractor either insures against it or adds a contingency to the bid price (Jergeas and Hartman, 1994). A study conducted by de Neufville and King (1991) in the US found that contractors add significant premiums, in the order of 3%, to their risks when they have a low need for work or projects that have high risks. Zaghloul and Hartman (2003) state that recent studies of Canadian contracts indicate that the use of disclaimer clauses can carry a premium of between 8 and 20%, depending on whether business conditions were favourable — low need for work, fair contract administration, suitable contract type, and completeness of design works; or adverse — high need for work, high technical complexity, unfair contract administration, and incomplete design works. 2.14 Payment methods for contract risks Different project procurement types offer different incentives to the contractor to influence the cost, delivery time and performance of contracts. On any given project there may be a whole range of contract payment types between owner, contractor, consultant, sub-contractors, suppliers etc. (Carmichael, 2000). The choice of any given procurement approach to deliver a project should only be made after a detailed and carefully considered risk analysis that considers the objectives, opportunities and risks involved in successfully delivering the project.


40

A number of possible payment schemes can be used within a contract to manage risk. A good overall summary of the main types of payment scheme is given in In't Veld and Peeters (1989). They describe cost-reimbursable schemes such as:

• cost plus percentage • cost plus fixed • cost plus incentive • schemes that contain both fixed price and fixed price with incentive provisions.

Carmichael (2000) describes two contract groups that are generally classified according to the form the consideration from the project owner takes:

1. Where the consideration is either a stipulated sum of money covering all the work or is a set of monetary rates covering the components of the work, the contracts are referred to as fixed-price contracts

2. Where the contractor is paid the cost of the work together with an additional amount for the use of the contractor’s services, the contracts are referred to as prime-cost contracts.

It is also common to have contracts with a combination of fixed-price and prime-cost components. 2.15 Delivery processes for projects Delivery methods for capital construction projects are mainly traditional processes that have evolved from history and the industrial revolution, where specialisation of professional organisations was the key trend (Pakkala, 2002). This means that architects, engineers, specialty contractors, and the industry have adopted a segmented rather than an integrated type of process. Subsequently, laws have sometimes been changed and adapted to reflect this movement. The polarisation of construction from design in the construction industry may have arisen from expected efficiencies in specialisation and the perceived need for independent design and oversight. But the resulting fragmentation and adversarial contractual cultures have now been seen by many to be an unfortunate departure from the single-point procurement solutions provided by master builders in previous centuries. Attempts to redress these imbalances have led to experimentation with a proliferation of procurement options. These include various approaches to:

• the division of a big construction project into work packages • the allocation of design, construction, supervision and management functions • the distribution of risks as reflected in various conditions of contract • the methods of payment • the selection of project teams/sub-teams.

Research into procurement best practice has identified a need for wider consideration of what constitutes project deliverables (Rowlinson and McDermott, 1999; Walker and Hampson, 2002). The construction industry is usually characterised by its complexities,


41

reluctance to change and resistance to innovations. The increasingly demanding present-day scenarios such as scarcity of resources, growing competition and cost–benefit/value-for-money issues require a clear understanding of what works best and how it can be applied to implement a philosophy of continuous change and improvement in organisations (Palaneeswaran and Kumarasamy, 2000). Not until more recent years has there been interest in seeking alternative methods, even though these so-called ‘alternative methods’ were used in the past procurements of a number of public sector projects. Below are terms applied to procurement types from Pakkala (2002). Design-Bid-Build (DBB – Traditional Method): This system was developed during the industrial revolution and resulted in the creation of specialised professional movements of architects, contractors and engineers. This approach has been the standard choice of project delivery systems for many years. In this model, the owner/client procures the services of a design consultant to develop the scope of the project and complete design documents which are then considered as legal documents for use in selecting a contractor who builds in accordance to the specifications developed by the design team. The owner maintains most of the risks. Typically in a public organisation, the proposal is in an open competition for a low price. The contractor that wins the award is legally bound to produce the project at a certain price, schedule, and minimum level of standard care. After completion of the project, the owner is responsible for operations and maintenance of the project. The owner is also responsible for all the financing needs. Design-Build (DB): This system’s heritage dates back to the construction of the pyramids, when it was referred to as the master builder. Design-build is simply a project delivery method in which the owner/client selects an organisation that will complete both the design and construction under one agreement. Upon completion, the owner is then responsible for all the financing aspects. Design-Build-Operate-Maintain (DBOM): This is a project delivery mechanism in which the owner/client selects an organisation that will complete the design, construction, maintenance and an agreed-upon period of operational parameters under one agreement. Upon termination of the operational period, the owner is responsible for the operations and maintenance of the project; unless the operations are continued under a separate procurement method. Design-Build-Finance-Operate (DBFO): This project delivery method is similar to DBOM, except that the contractor is also responsible for financing the project. The contractor assumes the risks of financing until the end of the contract period. The owner is then responsible for operations and maintenance of the asset. Build-Own-Operate Transfer (BOOT): This project delivery method is similar to DBFO, except that there is an actual transfer of ownership. The contractor is responsible for the design, construction, maintenance, operation and financing of the project. The contractor assumes the risk of financing until the end of the contract period. Subsequently, the owner is then responsible for the operations and maintenance of the asset.


42

Construction Management (CM – At Fee Agency): This is the process similar to DDB, the traditional model, in which the owner/client is responsible for the design, bidding, and construction of the project. However, the construction management organisation takes on the responsibility for the administration and management, the constructability issues, day-to-day activities, and also assumes an advisory role to the owner/client. The CM organisation has no contractual obligations to the design and construction entities. Again, the owner is responsible for operations and maintenance of the project as well as the financing aspects. Construction Management – At Risk Advisor (CM – At Risk): In this scenario the owner/client has one agreement with the construction manager, who then manages the contracts with the design consultant and the general contractor. CM – At Risk assumes many of the risks of the project, which differentiates this model from CM-Agency and DBB, where the owner maintains most of the risk. Again, the owner is responsible for operations and maintenance of the project as well as the financing aspects. Lump Sum: This is considered as a fixed-price agreement for the total work and products of a given project. Sometimes this is also referred to as a fixed-price contract. Any changes to the contract should be agreed upon by both parties, and they are usually described under change orders. Unit Price or Schedule of Rates: This refers to price considerations for specific aspects of construction and materials. For example, the cost per unit is for physical work or products, such as the price base for so many metres of guardrail etc. Program Management (sometimes referred to as Project Management of Full Delivery): This is considered as a construction entity that provides a comprehensive list of services to an owner/client from the planning stage throughout the entire process. This can also include maintenance and operations. This may be considered as the best alternative when in-house expertise is lacking, staffing reductions are needed, and outsourcing issues are current. Pure O & M (Operations & Maintenance): This is a delivery method in which the owner/client secures both operations and maintenance for a project under one agreement from a single provider, usually called the contractor. The owner is also responsible for financing aspects. 2.16 The traditional method of project procurement Pakkala (2002) writes that, in a global perspective, delivery of infrastructure services and products for capital projects vary in practice from country to country. He reports on highway infrastructure project delivery in Australia, Canada, England, New Zealand, Sweden and the US and notes that all use a common practice for the main delivery model, known as the traditional model, or Design-Bid-Build (DBB). This means that design/engineering services are produced first, and then another procurement contract is tendered for the actual construction or physical works, based upon the design/engineering portion of the contract. Also, he reports that variations and a range of combinations of the traditional process, such as parallel–prime contracting, are not common practice now.


43

Pakkala (2002) also identifies that Great Britain is one country which deviates substantially from this practice and uses the Design-Build (DB) models and the Design-Build-Finance-Operate (DBFO) model (sometimes referred to as Public Private Partnerships or PPP), which integrates the philosophy of integration and external financing for these processes. He also notes that the DB model is beginning to be used more frequently for many types of infrastructure projects, particularly in power plants, water works, bridges, rail, road and also in some local authority infrastructure projects. Langford et al. (2003) investigated construction costs in 11 similar motorway project parcels in the UK between 1990 and 1995. Five of these projects were carried out by a traditional design, tender, construct method. Five were undertaken by a procurement system by which the contractor bid a lump sum for the work. One project was done as a DB project. The results of their analysis indicate that, in road works, the construction costs per kilometre was some 11% less expensive when lump sum contracts were used. Langford et al. (2003) also reports that their research shows that lump sum projects are much more likely to be completed within budget and require less management by the client organisation. They also report that lump sum also delivers a more harmonious working relationship between the client and contractor. In the traditional contract, the majority of cost overrun is borne by the owner, whereas in lump sum bids, the performance risk is shifted to the contractor. Langford et al. (2003) point out that the pattern of risk sharing due to that of just chance (aleatoric factors) is unlikely to be changed as both types of contract arrangements permit the usual range of claims for unforeseen events. However, sharp differences may be seen in risk situations which may be defined as epistemic (which relates to the knowledge base of those taking the risks). For example, a contractor who perceives a particular item of work to be particularly high (or low) risk will price that item accordingly since the contractor sees its knowledge base and hence their ability to manage the risk as different from that of their competitors. For example, a contractor considering the risk of delay and additional cost due to a highway excavation through rock would include in their assessment any special knowledge of the geology of the area; and consequently the risk of encountering rock. Situation-dependent factors can determine which contract payment type or combination is most suitable in any given project. In 1975, the Aqua Group expresses a particular preference in that they believe that:

Fixed price should be the norm and it should rest with those advising the owner to prove that some other form of payment…would be in the best interest of the owner.

Carmichael (2000) states that there is an increasing transfer of financial risk from the contractor to the owner as the contract type moves from fixed-price to prime-cost contracts. As the owner takes more and more risk, it is expected that the contract price would decrease. De Neufville and King (1991) identify two ways of compensating for risk when developing a bid. One is to develop a standard cost estimate not considering risk and varying the mark-up depending on the risk. The second method is to develop a cost estimate that adjusts productivity factors or adds contingency based on the risk of each item being estimated and then apply a standard mark-up to this risk-compensated estimate. They report that, in practice, most contractors used the latter method. With regard to contract type, a contractor


44

experienced in undertaking contracts of a similar type would seem to require less risk premium, because the contractor is likely to have greater confidence in being able to complete the contract in accordance with the client’s brief. In respect of size of contract, Hillebrandt and Cannon (1990) point out that risks, including errors by the company itself, technical risks, financial risks of the project and onerous contract conditions, bear little relation to the size of contract and so it is difficult to put a percentage on the mark-up for them. They also state that, in general, a company that has a large number of smaller projects is likely to be subject to less risk overall than a company of similar size with a few large contracts. 2.17 Improving project delivery methods for risk A significant volume of literature discusses possible options for improving contracting methods and better risk allocation processes. Some studies present new ways of doing business such as partnering/alliances, risk sharing/reward systems, incentive-based contracts and others. However, Zaghloul and Hartman (2003) point out that those new delivery strategies between project owners and contractors are still founded largely on the self-interest of each participant. Motivation for performance under these strategies has focused largely on the retribution for non-performance and in most cases the relationships of the contracting parties are still defensive, although, in some cases, adversarial. The atmosphere created by such relationships has not been conducive to innovation or co-operation between parties (Hartman, 2002). Palaneeswaran and Kumaraswamy (2000) report that few public clients venture to adopt some sort of innovative measures in their project delivery methods, other than limited trials in pilot or experimental projects. Current trends indicate an industry-wide acknowledgement that many existing procurement practices lead to inefficiencies and inequities and that this can result in compromised project outcomes. In responding to this need in Australia, government and industry are working together to develop innovative procurement methods with a view to enhancing project outcomes. Balanced with this, however, is the need for project delivery methods to meet the highest standards of probity and public sector tendering requirements. Zaghloul and Hartman (2003) surveys more than 300 respondents in the Canadian and US construction industry. They report that, to reach a better risk allocation process, a trust relationship between contracting parties needs to exist first, and that this can be achieved through the following stages:

• a clear understanding of the risks being borne by each party and who owns or who can manage the risk

• more time and effort in the front end of a project and sufficient expertise to manage or mitigate the risks and administer the contract

• a negotiation phase prior to the start of the contract should exist, this phase is required to build a trust relationship between the contracting parties (this negotiation phase can be part of the contract itself)

• an adequate risk-sharing or risk-reward system should exist to share the benefits if the risk does not occur during the project life cycle.


45

Some of the main criticisms of the traditional DBB method are the lack of innovation, delayed completion periods and cost overrun that are sometimes encountered in projects. Since the client bears most of the risks of both the design and construction aspects, there desirably needs to be better practices to ensure the client’s needs are being met, quicker project completion times and cost effective solutions (Pakkala, 2002). Highway administrators and other public organisations (rail transport, airports and others) are seeking better practices. Some are using or evaluating alternative models such as DB, DBOM and BOO. In Pakkala (2002, the main goals of these innovative project delivery methods are to:

• produce projects that have better quality • bring cost savings to the client • transfer risk to the organisation best able to manage the risk • complete projects faster than the traditional methods.

Pakkala (2002) also reports that there are some tools available for predicting which delivery method might be applicable to projects that are being considered or planned and identifies the following:

• Design-Build Selector (from the University of Colorado) • Project Procurement Selection Model (PPSSM).

The rationale for better risk allocation between owners and contractors ought to be based on meeting the above conditions as far as possible. Missing one of these criteria is very likely to trigger an inappropriate risk allocation process for any given project and hence bring additional cost for the contracting parties (Zaghloul and Hartman, 2003). 2.17.1 Build-Operate-Turnover project delivery The Build-Operate-Turnover (BOT) approach to infrastructure delivery is where the private sector has to finance, design, build, operate and maintain the facility and then transfer it to the government after a specified concession period. This method has gained widespread popularity over the last 10 years, especially in developing countries (Chee and Yeo, 1995). The opportunity for profit and reward, however, does not come easily. The responsibilities are heavy and the stakes are high (Tiong, 1995). An essential part of the agreement between the government and the private contractor is the allocation of risk between the parties, that is, when an event occurs that influences the cost or quality of the contracted service, which party should pay to rectify the situation or, alternatively, which party should gain the resulting benefits (Arndt, 1999). Compared with conventional delivery methods, there is a higher risk exposure for the BOT sponsors because of the following:

• high front-end development costs • extensive and lengthy negotiations with the host government • multi-party involvement • long-term commitment • equity contribution from sponsors.


46

BOT infrastructure projects carry higher-than-traditional levels of risk as they typically involve high capital outlays, long lead times, and long-lived assets with little value in alternative use. The high-risk exposure associated with BOT projects means that special attention should be paid to analysing and managing risks (Chee and Yeo, 1995). Risk in the delivery of construction projects cannot be eliminated, but it can be minimised or transferred from one party to another (Kangari, 1995). The identification, analysis, and allocation of various types of risks are important for the validation of privately promoted infrastructure projects. Dias and Ioannou (1995) have classified sources of BOT risk in the following 10 risk categories:

1. country (political and regulatory) 2. force majeure 3. physical 4. financial 5. revenue 6. promoting 4. procurement 5. developmental 6. construction 7. operating.

Determining the relative importance of these types of risks is essential for BOT management decision makers. The decision makers of construction companies should evaluate and rank BOT projects with respect to their risk (Zayed and Chang, 2002). Zayed and Chang (2002) propose a risk index for evaluating BOT projects. The index assesses the risk and rank of BOT projects. To do this, they identify and analyse the main areas of BOT project risk and a model for calculating a risk index is constructed. The accuracy and robustness of their model has been verified by the comparison with holistic evaluations. They also advocate their newly developed approach as being more convenient than other approaches in dealing with BOT project risk. Nevertheless, this study relies upon a small academic study group for collecting the evaluation data and they recommend the data collection zone be increased more widely to practicing professionals in BOT projects in order to improve the accuracy of the developed risk model. 2.17.2 Relationship/Alliance/Partnering contracting The construction industry has used many delivery approaches to implement major infrastructure projects. Over time, dissatisfaction with the adversarial nature of some of the more traditional approaches, lead to sponsoring organisations adopting various means to work in more collaborative manners, particularly during the late 1980s and 1990s. Rahman et al. (2001) suggest that a relational contracting (RC) approach can be mobilised as a potential route towards improving relationships and team work. The transaction cost economics approach provides a useful framework for analysing the inevitable differences in interest between different contracting parties who are members of the project coalition (Winch, 1989). On the other hand, relational contracting encourages long-term provisions


47

and mutual future planning, and introduces a degree of flexibility into the contract by considering a contract to be a relationship among the parties (MacNeil, 1980). Relational contracting principles embrace and underpin various approaches, such as partnering, alliancing, joint venturing, and other collaborative working arrangements that provide better risk sharing mechanisms (Alsagoff and McDermott, 1994; Jones, 2000). At the core of relational contracting, the legal mechanisms offered by specific contracts are not strictly followed, but the parties themselves govern the transaction within mutually accepted social guidelines (Macaulay, 1963). Partnering is a good example of relational contracting principles. Thompson and Sanders (1998) observes that benefits from partnering increase with a migration of teamwork attitude from competition to co-operation, through to collaboration and finally to coalescence. Rahman et al. (2001) reinforce this by reporting that more relational and performance-oriented contractor selection practices encourage amicable relational contracting environments and more collaborative teamwork. Ho (2000) depicts cost savings of 11 to 38% on similar office building construction in Hong Kong when the contractor was brought into the team at the very outset of a project. Extending the relational contracting approaches to sub-contractors and suppliers through the supply chain and ensuring their participation at early stages of projects can further increase benefits (Kumaraswamy and Mathews, 2000). However, relational contracting approaches are expected to work in almost any environment, if applied properly. This requires transforming traditional relationships towards a shared culture that transcends organisational boundaries (CII 1996). In the US, the partnering model has evolved as a typically non-legal commitment to teamwork and collaboration. In the United Kingdom, the Institution of Civil Engineers launched the New Engineering Contract and at the same time project alliances evolved (initially in the North Sea oil and gas industry). Since then, project alliancing has become more established in the oil and gas industries in particular and some other major infrastructure industries in Europe. Incentive contracts are also widely accepted practices around the world. These differ from alliances in that, under defined incentives, there can be the traditional scope of work contracts and allocation of risks. In Australia, project alliances have been taken to a deeper level of sophistication and use, particularly in major public sector infrastructure projects. A project alliance, to describe it most simply, is a project/program delivery strategy where the clients’ and commercial participants’ objectives are aligned to maximise performance, proactively manage risk, reduce cost and achieve outstanding outcomes. The first two project alliances in Australia were oil and gas industry projects – the Ampolex-Wandoo Alliance and the Western Mining Corporation – East Spar Alliance. It was after completion of these two projects in 1998 when the first three government project alliances were created. Sydney Water became the first public sector organisation to create a project alliance on the Northside storage tunnel project, a 26 kilometre tunnel under the northern suburbs of Sydney with a firm schedule deadline (the Sydney Olympic Games). This was followed by the National Museum of Australia, known as the Acton Peninsula Alliance


48

(A$150m) (Walker et al., 2000), and the Western Australian Water Corporation’s Woodman Point waste water treatment alliance. Since 2002, numerous project alliances have been undertaken, with the Department of Main Roads in Queensland being the government agency with the most alliance experience at the end of 2002 in Australia. Not all projects are suited to an alliance approach. Project alliancing is best suited to those projects where the traditional risk transfer strategy is not appropriate. In many projects, outcomes can be enhanced and the project optimised by embracing risk through collaborative and co-operative contracting against the traditional blind faith transfer or shifting of risks to others. A fundamental design principle of a project alliance commercial framework is that if one participant wins, all win; or if one looses, all loose. The key tangible and intangible benefits of a project alliance delivery approach include:

• cost reduction • early completion • improved quality of the asset through enhanced workmanship and finish • improved operability.

As well, benefits for commercial participants in alliancing include:

• enhanced profit margin • enhanced market-place reputation • outstanding risk management.

Project alliances are not only better equipped to analyse the real risks of the project, but can also be better equipped to deal with their consequences. On the other hand, Tuohey (2002) points out that, in practice, there have been a number of issues in the execution of alliance type contracts. These include:

• the risk/return ratio can be such that the rewards to the contractor are low, particularly if the contractor is required to have some key people tied up in the alliance for extended periods

• the integrated team environment can be quite stressful for people • the nature of long-term alliance contracts are that there is one winner only, with the

consequence that there is the potential for competition in the market to be reduced, particularly in thin markets like Australia.

The next section of this literature review document focuses on the linkage of risk to project contracting in project delivery. 2.18 Project contracting Once the project delivery method has been selected by the owner and pre-planning processes have been completed, there is usually some form of Request for Proposals (RFP), sometimes referred to as Request for Tenders (RFT), which includes the type of contract and the criteria that are being used to determine the eventual winner of the tender. This contractor selection method can vary its criteria range between a 100% price to a 100% quality-based selection criterion.


49

Various researchers such as Russell et al. (1992), Holt et al. (1994), Kumaraswamy (1996), Rankine et al. (1996), Russell (1996), Hutush and Skitmore (1997a), Holt (1999), Kumaraswamy and Walker (1999) and Palaneeswaean and Kumaraswamy (2000) have studied and reported on various contractor selection practices. All suggest improved methodologies, but also highlight their strengths and weaknesses in different practices. Despite the increasing use of alternative forms of project delivery systems in the past two decades, the methodologies and procedures for bid evaluation, selecting contractors and awarding contracts have remained relatively unchanged (Hatush, 1998). Hatush and Skitmore (1998) suggest there are five process elements that are common across all contract award practices. They are:

1. project packaging 2. invitation 3. pre-qualification 4. short-listing 5. bid evaluation.

2.18.1 Procurement auctions The background to the traditional foundation in procurement of bidding for works in the form of auctioning goes back to ancient times but the earliest academic treatments are relatively recent, with the contributions of Friedman (1956) from an operations research perspective and Vickery (1961) from a game theoretic perspective. Auctions have developed into four basic allocation methods – English (first-price open-cry), first-price sealed-bid, second-price sealed-bid and Dutch (descending). A fundamental implication of the common-value model that stands in marked contrast with private value auction theory is that, other things being equal, the bidder with the largest estimate will make the highest bid. Consequently, even if all bidders make unbiased estimates, the winner will find that he had overestimated (on average) the value of the rights he has won in the auction (Milgrom and Weber, 1982). This is commonly known today as the winner’s curse, due to the winner being said to be cursed by:

having paid more for the asset than its true value (Marks, 2000:11). Analytical game theory assumes that rational bidders will anticipate the ‘winner’s curse’ and bid very conservatively to avoid it (Camerer, 2003). However, field study research by Dyer and Kagel in 1996 in the construction industry found no evidence of winner’s curse. Neither does there appear to be any anecdotal evidence of the effects of this (Dyer and Kagel, 1996). 2.18.2 The competitive tendering process Competitive tendering, particularly for public works, has a long history in places like the UK. The origins of the practice are reported by Powell (1980) to have come from the early 19th century and basically arose out of dissatisfaction with the then existing system of measure and value. This was where trades were paid directly by the client for work as it proceeded. Powell (1980), in quoting Elsam (1826), wrote that:


50

Measurement of completed building work prior to payment causes enormous difficulties and frequent disputes, with the ruinous uncertainty of common estimates by measurers who were said to be seldom if ever right. The response of Commissioners reporting on the Public Office of Works in 1812–1813 was to favour competitive tendering as a superior alternative to measure and value.

The client’s objectives in the competitive tendering process are: 1. to obtain a fair market price for work, bearing in mind the general state of the

construction market at the time 2. to enter into an agreement with a contractor who possesses the necessary technical

skill, resources and financial backing to give the project owner the best possible chance that the works will be completed within the required time, cost and quality standards.

To achieve these objectives, Smith (1995) suggests the involvement of the following:

• selection of an appropriate procurement methodology • the preparation of appropriate tender documentation • identification and selection of an appropriate number of suitable contractors who are

willing to tender for the work • choice and acceptance of the most suitable tender.

The nature and form of the competitive arena for the contractor in construction contracting are largely determined by the project owner or their representative. The choice of bidding system, coupled with bidder selection practices, has a direct bearing on the degree of competition, because it affects both the number and the identities of bidders competing for a particular contract. Selective tendering systems appear to be more restrictive than open tendering systems, because the contractor can bid only upon receiving an invitation. For open tendering, the onus is on the contractor to bid after responding to an advertisement. Private sector clients appear to have more flexibility in contractor selection, because public sector clients, mainly for the sake of public accountability, are forced to adopt strict procedures. The tendering process is an important stage in project delivery. However, the activity involving contactor selection is an important decision required by any client so as to minimise project risk exposure. The next section of this literature review looks closely at research aspects of contractor pre-qualification. 2.19 Pre-qualification of contractors Contractor failure is an extremely disruptive force to every stakeholder in a construction project. Unfortunately, the construction industry is such that it is highly susceptible to varying economic conditions and to increases in competition. These characteristics sharply distinguish the construction industry from other sectors of the economy and explain why contractors are far more at risk than their counterparts in almost all other industries (Kangari and Riggs, 1989).


51

If signs of contractor distress can be detected early on, then it may be possible for a project owner to either not engage a particular contractor or become more actively involved with the project before the contractor has suffered irreversible damage which can lead to bankruptcy and increased losses to all stakeholders. One of the most difficult and important decisions taken by a project owner for the success of a project is the selection of the contractor. Such a decision deserves careful attention because of the complexity and sensitivity involved (Hatush, 1998). The following four weaknesses are found in contractor selection practices (Holt et al., 1993; 1995):

1. lack of a universal approach 2. long-term confidence attributed to results of pre-qualification 3. reliance on tender sum in decision making 4. inherent subjectivity of the process.

Choosing a contractor based solely on the lowest bid price is seen as one of the major causes of project delivery problems. Another disadvantage of using the lowest bid as a principal discriminating criterion is that some contractors who are facing a shortage of work may enter unrealistically low bid prices, simply to try and maintain cash flow. Therefore, as Hatush and Skitmore (1998) indicated, financial and technical criteria should be considered in order to assess the potential of contractors finishing projects on time and to assess whether contractors have the resources necessary to complete any contract awarded to them. Disastrous consequences from awarding contracts to the lowest bidders, without due consideration of their competencies, have led to growing awareness of the needs to incorporate non-price parameters into contractor selection methodologies. Such fresh approaches have been documented, for example by Schexnayder and Ohrn (1997), Arditi and Yasamis (1997), Kumaraswamy and Walker (1999) and Palaneeswaran et al. (1999). While the risk of contractor default cannot be eliminated by the project owner, it can be transferred. When the owner signs a contract with a contractor, the risk of contractor default is normally transferred partly or totally to a surety company. The general contractor may do the same when signing contracts with sub-contractors. While the end point may be the surety company, there is still a cost to transfer risk in terms of premiums assessed and the cost of dispute resolution. The project owner is the one who pays for it in the end (Al-Sobiei, et al., 2005). For an owner, the costs of default may also include loss of profits due to the project not being completed on schedule, administrative expenses to analyse the situation, cost to complete the project beyond the money yet paid to the contractor, fees associated with the resolution of conflicts, and negative publicity and loss of goodwill within the community. Given the fact that contractor default depends on environmental, operational and strategic factors, it is important to use the past history of situations to minimise contractor default. For any given project, pre-qualification is a highly important decision for the project owner. The ability to optimise the short listing from a larger number of potential contractors can be as important as the selection of the right bidder. This is because the quality of the final bidder can only be as good as those short-listed (Khosrowshahi, 1999).


52

Pre-qualification is widely used as an important procedure to help a project owner to short-list proper contractors and is a screening process to evaluate candidate contractors’ ability to complete a contract satisfactorily before being allowed into the bidding process. The project owner needs to assess a contactor’s overall capacity, including their technological ability, financial status, management skills, organisational structure and so on. Shen et al. (2003) suggest that, to engage in a fair pre-qualification process, the parameters used for conducting the assessment and method for analysing the parameters should be consistent. The pre-qualification process basically consists of evaluating the available pool of potential tenders, identified either by advertisement or invitation, against some given set of criteria in order to find some group of firms who are all considered competent to carry out the work and who are expected to provide an acceptable range of prices. The current practice of pre-qualification is normally made by exercising accumulated experience and judgement in assessing a given set of input variables such as reputation, past performance, financial stability, current workload, firm’s resource capacity, experience records and technical expertise (Lam et al., 2002). Different project owners operate their pre-qualification processes under different objectives and constraints. There are certain characteristics that distinguish public clients from private clients. Khosrowshahi (1999) asserts that private clients tend to put emphasis on best service, lowest price, best value-for-money and competitive advantage to their own organisation, whereas public clients aim to demonstrate accountability for public funds and even-handedness to suppliers and contractors. On the other hand, Lam et al. (2000) points out that the criteria for pre-qualification may differ from each other because the characteristics of the project and contractor are distinct and dynamic. Masterman (1994) also points out that, in spite of different clients and projects, there are common characteristics of contractor pre-qualification such as:

• reasonable cost • reasonable quality • reasonable security.

In many cases, and particularly in the case of small contracts or much specialised work, a number of contractors might be chosen simply on the basis of work that they have done for an owner in the past. Most firms tend to accept such invitations even if they have no intention of submitting a bona fide tender. Drew and Skitmore (1992) point out that this method has deficiencies and should not be used on its own except for very small projects as it is rather unreliable and may result in the selection of bidders that are either not interested or are unable to provide competitive bids for a particular contract. 2.19.1 Research into client pre-qualification practices In their study, Palaneeswaran and Kumaraswamy (2001) examine pre-qualification practices in different countries such as the US, Hong Kong and Australia. Many project owners in the US public sector use various pre-qualification ratings providing a basis for a more structured and dynamic approach in order to define bidding boundaries for contractors. In the US, some departments of transport use pre-qualification ratings such as:

• aggregate rating


53

• current bid capacity • maximum rating • project rating • maximum capacity rating • work class rating • performance rating.

These ratings can be used to define parameters such as the maximum monetary amount of work that can be allowed to a contractor or the maximum value of a work that a contractor can bid for a particular project. The Department of Public Works Bureau of Hong Kong prepare approved lists according to relevant expertise, financial status, and technical and managerial capabilities of contractors as well as their completion of other contracts. Only contractors on the corresponding list can apply for contracts. Hong Kong Housing Authority maintains a comprehensive Performance Assessment Scoring System to review the registered contractors’ performance levels of their contracting works in the ongoing projects. Scores for contractors are calculated and a comparative score league is formed. Contractors who fall in the upper section of this league are invited for bidding in upcoming projects. The Mass Transit Railway Corporation of Hong Kong, on the other hand, uses a set of pre-qualification criterion for the evaluation of contractors. Members of a committee score candidate contractors with respect to these and then use the scores for recommending contractors’ pre-qualification. These criteria and sub-criteria are:

• Corporate structure o management o relations

• Experience o performance on corporation’s contracts o construction performance in similar projects o work experience

• Resources and facilities o staffing o labour o construction plant o planning/programming o design o manufacturing/fabrication o sub-contractors

• Workload o current o future

• Support functions o safety o quality management.


54

The Construction Industry Development Agency in Australia also uses pre-qualification procedures. The Agency prescribes mandatory criteria to screen out ineligible or unsuitable applicants. As well, additional and reserved criteria are used to further describe and/or evaluate the applicants’ skills and philosophies in order to discriminate them properly. Mandatory pre-qualification criteria include:

• technical capacity • financial capacity • quality assurance • time performance • occupational health and safety • human resources management • skills formation.

Claims performance, compliance with legislative requirements, and management for continuous improvement can be prescribed as additional criteria. Although contractors are generally pre-qualified so as to minimise risks and failures, their performance levels differ widely under different workloads and dissimilar environments. Palaneeswaran and Kumaraswamy (2000) point out that many Federal states of the US have recognised this and incorporate well-structured capacity rating assessments in their contractor selection procedures. For example, the US Washington State Department of Transportation looks at the total value of uncompleted prime contract work and also maximum work class type value, such as asphalt/concrete paving, bridges etc., which a contractor is permitted to have under contract at any one time within the pre-qualification period. In Australia, the Queensland Department of Main Roads specifies similar maximum capacity ratings in their Project Delivery System Guidelines (Queensland Department of Main Roads, 2000). Once these levels are determined, this is used to determine a contractor’s eligibility to bid for a new project. The pre-qualification systems of the Queensland Department of Public Works and Housing, as well as the Queensland Department of Main Roads, use aspects of technical capacity, management approach, people involvement, business relations and financial capacity as the pre-qualification criteria. By evaluating applicants with respect to these criteria, contractors are pre-qualified for two years and are placed at a pre-defined level. The aim of the system is to ensure proper matching between the size and the complexity of the projects and the abilities of the contractors (Topcu, 2004). 2.19.2 Choosing contractors on low bids Hatush and Skitmore (1998) have all published research works on decision criteria used by clients for choosing a construction contractor. Holt et al. (1994a) carried out a survey of 53 major UK construction client organisations to determine the decision criteria used for contractor selection and the importance of these criteria in terms of influencing their choice of contractors. Hatush and Skitmore (1998) report that all clients use a similar set of criteria for contractor selection, but that the way clients quantify these criteria can be very different in practice. In these works, a contractor’s bid amount appears to be the most dominant and important criterion (Holt et al., 1994; Hatush and Skitmore, 1998).


55

Holt et al. (1994a) developed a method to evaluate contractor pre-qualification criteria and provided guidelines for practitioners, highlighting areas to address when evaluating a contractor based on a particular criterion. In Holt et al. (1996), they used cluster analysis as a means of reducing a large number of potential bidders in order to identify only those suitable to tender for a particular project. Ng (1996) investigated different decision support systems (DSS) for contractor pre-qualification. Among the surveyed decision support systems were database management systems (DBMS), expert systems (ES), fuzzy sets (FS) and case-based reasoning (CBR). For the project owner, it is vital to be able to select the best available contractor and currently a standard pre-qualification model that can be used widely in the construction industry is not readily available. Most public agencies and companies who perform pre-qualification have their own specific pre-qualification model. Tran (2002) lists and describes a number of international pre-qualification models found in literature. These include:

• Qualifier-1, Contractor Pre-qualification Model. This employs a dimensional weighting procedure that produces aggregate weighted ratings of candidate contractors from questionnaires

• Qualifier-2, Knowledge-based system for Contractor Pre-qualification. This is a knowledge-based system in which the decision of pre-qualification is based on the user using decision rules, not calculated scores, and where contractors meet the decision criteria to proceed to the next step in the decision model

• Fuzzy Set Pre-qualification Model. This uses fuzzy set theory to include the uncertainties in the contractor evaluation process and takes into account the uncertainty associated with the information from contractors, data reliability and the uncertainty associated with the decision makers

• Hypertext Decision Support Pre-qualification Model. This model uses pair-wise comparison of factors and an aggregate weight for each contractor is developed

• Cluster Analysis in Contractor Classification. This involves the evaluation of candidate contractors using predetermined selection criteria. Cluster analysis is used on raw data to divide it into a series of sub-sets by which contractors are ranked

• Neural Network Pre-qualification Model. Neural networks are used to learn from historical contractor data

• Contractor Pre-qualification Process. This is based on fuzzy logic and is a three-stage model employing a hierarchical framework

• University of Toronto Contractor Pre-qualification Model. This method considers contractors’ efficiency measures and calculates the efficiency score of each contractor.

Ng (1996) developed an integrated object-oriented expert system for contractor pre-qualification, whereas Taha et al. (1995) proposed a knowledge-based decision support system for predicting construction contract bond claims using contractor financial data. This decision support system employs inductive learning and neural networks to extract the problem-solving knowledge. Once contractor pre-qualification is carried out satisfactorily by the client, the next stage in managing risks in the delivery of projects is that of bid evaluation. The next section looks at this process.


56

2.20 Bid evaluation Public agencies typically acquire construction services through competitive procurement, awarding contracts to the lowest responsive bid read out at a public bid opening. Policy-makers have seen this lowest bid method as the most risk-averse method of awarding general construction contracts. There are inherent risks in using competitive procurement in public agencies. The principal risk is contracting the wrong firm and the owner’s dilemma, as a corollary of the winner’s curse, where the low bidder is cursed because they are the one most likely to have underestimated the project cost. The intensity of price competition between contractors has been blamed for many industry ills, particularly in scenarios where price considerations have been deemed paramount. The apparent, but often misleading, economy and convenience of awarding contracts to the lowest bidder is particularly comforting in the public sector, where high accountability regimes require onerous justification when awarded to other than the lowest bidder. Selecting the best contractor is one of the most important decisions a client has to make. Conversely, it is equally important for contractors to know why their bids are rejected. In the case of a public client, the results and reasons for awarding a contractor and/or rejecting others should be explicit because of public accountability. If the client is a private one, contractors may also wish to know the reasons for their failure. In either case, the feedback of contractors’ weaknesses can only help improve contracting firms to the betterment of the industry. Palaneeswaran and Kumaraswamy (2000) point out that all necessary information should be provided in tender documents and all project procurement procedures and practices followed by public procurement agencies should be transparent so that contractors can be become aware of their strengths and weaknesses. This will thus improve their performance levels to obtain more contracting opportunities while remaining competitive. A low tender price may seem appealing to the client at tender stage, but the project may encounter problems if the contractor is, for example, not able to complete the work on time or even compromises on the construction quality in an attempt to reduce the contractor’s cost (Mahdi et al., 2002). Crowley and Hancher (1995) caution that the awarding of a construction contract to the lowest bidder without considering other factors can result in problems such as cost overrun, delays and poor performance. They point out that it is very real that the lowest bidding contractor may tend to adopt a confrontational ‘claims oriented position’ once the project is awarded as a means of making-up any financial short fall. Smith (1995) argues that what is really required from a public sector point of view is not simply to obtain the lowest price, but to obtain the best value for the money spent. According to Smith, there is considerable doubt s to whether this can be achieved by taking the lowest price in a largely unrestricted open competition (Smith, 1995). Pakkala (2002) points out that, although the low bid process with 100% price criteria is the easiest and simplest method of choice because it is quantatively measured, objective, unbiased and not contestable, his study find that this low-bid process is not conducive to the creation of innovation and long-term savings in the longer term. He also points out that


57

especially for road projects in particular, most project owners usually desire some elements of quality and technical criteria since road project are used by the general public and there is demand for quality and reliability. Some of the more common quality criteria identified are:

• technical skills • personal skills (resources) • management team • supply chain management • methodology • environmental criteria • relevant experience • past performance.

Some of the quality criteria can be clearly identified as relevant, and important. However, the difficulty arises when evaluating these aspects because some of them are personal skills and not totally objective criteria. Some of the common forms of bid selection methods used include:

• Low bid or lowest price, • Lowest price conforming tender (lowest price which meets certain pass/fail criteria) • Weighted Average — WA formula (for weighting price and non-price criteria) • Quality Price Trade Off — QPTO (a trade off system for providing quality) • Negotiated (where both parties negotiate until a satisfactory price is agreed upon).

Wang (2003) proposed that, before considering bids submitted by competing contractors for a public procurement project, the owner should determine a project ceiling price or cost estimate to use as a threshold or reference point for evaluating the bids and points out the dilemma for the project owner is setting a ceiling price that is sufficiently low to satisfy the owner’s interests in cost savings, yet sufficiently high to tender out the project. Wang (2003) also points out that while some project owners will fix a ceiling price by an average bidding ratio (winning bid divided by ceiling price) of past projects, most owners merely make a decision based on gut feeling. Wang (2003) goes further in saying that, despite its popularity, the historical average bidding ratio is inferior to more systematic evaluation methods. This is because the major problem with it is that the ratio tends to be unrealistically low, especially in a slow construction economy when bidders tend to propose unsustainably low bids simply to get a contract. A further drawback is that the unique characteristics and risks of the project are ignored. 2.20.1 Research into bidding Research in bidding has focused either on the development of bidding models to assist bidders in winning contracts (Dozzi et al., 1996; Fayek, 1998) or on the evaluation of competitive bids (Crowley and Hancher, 1995; Crowley, 1997; Williams, 2003). Statistical methodologies have been developed which allow the detection of bids which are too low and are usually considered to be discordant bids because they are in disagreement with the other bids (Crowley, 1993). Furthermore, Crowley asserts that there is a historical relationship with data between discordant bids and the magnitude of cost growth experienced within the


58

particular project, with the owner taking the perceived benefit of a lower bid but incurring the financial risk of having higher project cost growth. Williams et al. (1999) have shown that there is a systematic relationship across highway projects between the low bid and the completed project cost for competitively bid highway projects in New Jersey and Great Britain. They found that as the magnitude of the low bid increased, the resulting completed project cost tendered to increase absolutely and as a percentage of the original bid. Williams (2003) suggested that large highway projects tend to have much higher relative cost increases than small projects. His research also suggested that methods of minimising the size of competitively bid highway projects, such as breaking a project into smaller components, may have been useful in minimizing cost overruns. Williams (2003) also concluded that cost increases for different types of construction will follow different patterns and that it was not possible to generalise that the low bid and related project were related in the same way for all types of projects. 2.20.2 Sub-contractor selection Currently in construction, many project activities have been sub-contracted out by the main contractor. Although sub-contracting has many advantages, it also brings additional risks to the main contractor and to the client. These risks are uncertainties related to a sub-contractor's technical qualifications, timeliness, reliability and financial stability (CII, 1989a). A large proportion (up to 90%) of construction activities can be sub-contracted on a given project and so it is important to probe deeper down the supply chain in order to ensure that appropriate sub-contractor selection is also a vital element managing risk in construction projects. Improvements in sub-contractor selection processes have not received the attention that one would expect from such a significant contribution to the industry. Price-based selection often squeezes out the more responsible sub-contractors, driving down both prices and performance levels. A growing appreciation of such anomalies has led to recent initiatives towards longer term main-sub-contractor relationships (Matthews, 1996). The evaluation of sub-contractors by main contractors (and vice versa) is a complex undertaking. An example of a neural network based approach to sub-contractor rating is proposed by Albino and Garavelli (1998). 2.21 Project budget estimating An estimate can be defined as the calculated prediction of the amount of money required to undertake a specific amount of work expressed in dollar values in the year in which it was prepared. Out-turn dollars is the cost, for the period in which the work was or is to be performed. Estimates prepared at a particular date can be converted to out-turn dollars by applying an appropriate inflation rate to the time-series cost of the project. It is prepared in a systematic manner appropriate to the size and complexity of the project, and to a level of accuracy commensurate with the available information and the intended use of the information developed. It may include some prior expenditure in a mix of year dollar values. The project cost estimate is primarily concerned with the cost of resources needed to complete the project activities and include all the processes which are employed to maintain financial control over a project. Common value auctions generally assume that a true cost exists but it can only be estimated (Friedman 1956). Management theory, on the other hand,


59

tends to assume that the forecast or budget cost is fixed and that production activities are sufficiently variable to be somehow manipulated to be kept within budget. In project construction, this fixing of budget costs is often done by reference to the estimator’s little black book of figures that are known from experience and thus can provide a reasonable target cost. The traditional approach to cost estimating is the derivation of a best estimate from the knowledge of existing conditions based on current rates and prices in similar situations, but with adjustments to reflect anticipated differences in say ground conditions, site accessibility and other factors. A contingency allowance which usually varies according to type of work, anticipated risk and project stage is added to cover unforeseen expenditure. 2.21.1 Estimating processes Abbasi and Al-Mharmah (2000), Hutchings and Christofferson (2000) and White and Fortune (2002) examined various management tools and techniques that are used in project estimating as well as in cost control processes. The most popular areas are detailed as:

• analogous • parametric • detailed.

Analogous estimating: This is an approximate estimating method that compares costs with similar past projects and which often depends on expert judgement. It can be used in the preparation of the earliest price estimates for the client. The estimator should have the relevant experience of estimating the cost of similar projects (Ashworth, 1994). Analogous estimating using the actual cost of a previous, similar project is the basis for estimating the cost of a current project and is frequently used to estimate the cost when there is a limited amount of detailed information about the project. Usually estimators retain their own database of historical project costs from which equivalent or similar cost information may be drawn (Loftus 1999). Parametric estimating: Parametric estimating uses project characteristics in a mathematical model to predict project cost. This method is considered fairly accurate when historical information used to develop the model is accurate. Conceptual estimates based on parametric costs are most commonly used in building and highway construction. The parametric cost approach relates all costs of a project to just a few physical measures, or parameters that reflect the size or scope of the project. For example, the gross floor area of a pre-stressed concrete deck surface over water would be a typical overall parameter for a structure such as a bridge over water. Some costs, say for concept road construction projects, can be expressed in lane/km. With good historical records on comparable structures and associated risks, parametric costing can give reasonable levels of accuracy for preliminary estimates (Barrie and Paulson, 1992). Linear measure is the most widely used parameter. Detailed estimating (Bottom-up): Unit price estimates can be compiled when quantities of work items may not be precisely determinable but the nature of the work is well defined (Clough, 1986). This is best suited for works which are relatively simple and repetitive in nature such as buildings. It involves estimating the cost of individual work items and the synthesis of cost estimates from resource estimates made at the lowest possible level of


60

work-breakdown-structures (Project Management Institute, 2000). The addition of indirect cost, plant and equipment, office overheads, profit, escalation and contingency develops the total estimated project cost (Barrie and Paulson, 1992). A study by Hester et al. (1991) indicates that the estimating method and the accuracy of project cost estimates could be a major reason for having cost changes. They found that that most project owners assume that routine changes in the project will only affect the work in the change area, whereas. In reality, the effects can extend well beyond that specific change area Hester et al. (1991). 2.21.2 Early project estimates Various cost estimates are made at different stages of the process: project planning, decision to build, tendering, contracting, and later renegotiations. Cost estimates at each successive stage typically progress toward a smaller number of options, greater detail of designs, greater accuracy of quantities, and better information about unit price. Thus, cost estimates become more accurate over time, and the cost estimate at the time of making the decision to build is far from final. Accurate, early cost estimates for engineering and construction projects are extremely important to the sponsoring organisation and the project team. For the sponsoring organisation, early cost estimates are vital for business unit decisions that include strategies for asset development, potential project screening, and resource commitment for further project development. Inaccurate early estimates can lead to lost opportunities, wasted development effort, and lower than expected returns. Early estimates are typically plagued by limited scope definition (and thus high potential for scope change) and are often prepared under tight time constraints. Furthermore, reliable cost data are often difficult to obtain during the early stages of a project, particularly if basic design and geographic issues remain unresolved. Early estimates, even when grossly inaccurate, often become the basis upon which all future estimates are judged (with future estimates sometimes being ‘corrected’ to be consistent with early estimates). However, final cost often exceeds the initial estimate. The accuracy of early cost estimates for capital projects has been a major concern and a subject of much scrutiny over the last 35 years. Hackney (1986) proposed the use of the definition checklist for applying contingency to capital cost estimates and then validated the checklist by comparing the definition ratings of 30 projects to their respective levels of cost overrun. Hackney later revised the checklist in 1986 to specifically address process projects and developed a separate checklist to apply the definition rating method specifically to hazardous waste remedial projects (1986). 2.21.3 Project cost overrun The problem of cost overrun, especially in the construction industry, is a worldwide phenomenon, and its effects are normally a source of friction between clients (especially government clients), project mangers and contractors on the issue of project cost variation. In 1882 for instance, the Amazon province of Brazil, at the height of its financial might,


61

awarded a contract to the Portuguese Engineering Council of Lisbon to construct a theatre at Manaus at the cost of 500 cruzerio. As reported by Carelli (1989), the project was completed at a staggering cost of 20,000 cruzerio 12 years later. Akpan (1987) also reported the case of the British National Westminster Bank Headquarters’ building where the cost overrun was reported to have risen to five times more than the original estimate. When the Suez Canal was completed in 1869, actual construction costs were 20 times higher than the earliest estimated costs and 3 times higher than the cost estimate for the year before construction began. The Panama Canal, which was completed in 1914, had cost escalations in the range of 70 to 200% (Summers, 1967). More recently are examples such as Denver’s US$5 billion airport that was 200% overspent (Szyliowicz and Goetz, 1995), the 800 million Danish Kroner Oresund rail/road bridge between Copenhagen and Malmo, Sweden that was 68% overspent, (Flyvbjerg et al., 2003). An example of cost underestimation in Australia is the Sydney Opera House, with actual costs of approximately 15 times greater than those projected (Hall, undated: 3). On the other hand, Yeo (1990) points out that the entire Apollo space program cost was about US$21 billion, which was only 5% above its initial estimated budget. But few are aware that there was, in fact, a hefty $8 billion contingency already built into the initial estimate (Morris and Hough, 1987). Despite the large number of reported cases, it seems that construction ranging from the simplest to more complex projects such as transportation systems and oil and gas platforms have increasingly faced overrun. Raftery (1994) points out that construction projects tend to have poor reputation for excessive time and cost overrun. Morris and Hough (1987), during a study of records from different types of projects funded by the World Bank between 1974 and 1988, found that 63% out of 1778 projects had experienced significant cost overrun. In a study of 204 construction contracts let between 1986 and 1990 in Italy, Tagliaventi (1991) reported that an average cost overrun of 50%. Research carried out around 1992 by the Transportation Research Board evaluated construction cost overruns on projects completed for the Washington State Department of Transportation. The objective was to identify factors that have the strongest association with construction cost overruns. Results of the analysis, which examined information from 468 construction projects, indicated that cost overruns, expressed as a percentage of the original contract amount, tended to increase with the size of the project. Evidence also suggested that the cost overrun rate increased with the number of bidders and with the increased dispersion of the various bids submitted per project (Hinze and Walsh, 1997). Construction cost estimating on major transport infrastructure projects has not increased in accuracy over the past 70 years. The underestimation of cost today is in the same order of magnitude that it was then (Flyvbjerg et al., 2002; Molenaar, 2005). 2.21.4 Research into project estimating In the late 1970s, the US Department of Energy recognised the importance of accurate early cost estimates and contracted with the Rand Corporation to study the capital cost estimation problems associated with pioneer energy, process plants (Merrow, 1978). During the study,


62

Merrow determined that 74% of cost growth is caused by underestimation (i.e. improper estimation). In 1988, the US Federal Construction Council commissioned the Building Research Board to study construction estimating practices and to recommend techniques for improving the accuracy of early cost estimates. The Board identified three recommendations for improving estimating procedures (Morris, 1988):

1. developing standard terminology and formats for budgets and estimates 2. taking steps to ensure that estimators are properly qualified for conceptual estimating 3. expanding the use of parametric and probabilistic estimating techniques for

conceptual estimates. Ogunlana (1989) carried out research into monitoring cost estimators’ accuracy of the last cost forecast prior to tender for projects. Data for the research were collected from seven County Councils Offices in the United Kingdom. The projects used in this study were all road projects for new construction or facilities improvements such as roundabouts, maintenance works, and expansion of existing roads or bypasses. A total of 36 projects were analysed. The research included a survey to ascertain what level of error was acceptable in seven design offices. Table 2.5 below shows that all the offices fell within a +/– 15% range.

Source: Ogunlana (1989)

Table 2.5: Target accuracy of highway design estimates

Later Ogunlana (1991) reported on the accuracy achieved on different stages of road estimates where the mean accuracy of statutory utility estimates was reported as 35.7% for 12 projects, with the overall value of accuracy for all elements of the projects being 12.8% with a coefficient of variation of 11%. Transportation projects have historically experienced significant cost overrun from budget estimates. A recent study of 258 infrastructure projects spanning a time period of more than 70 years found that project costs are underestimated in approximately 90% of the cases/projects, and the actual costs averaged 28% higher than estimated on this sample (Flyvbjerg et al., 2002). Although highway projects fared better than rail and fixed-linked projects, the sample still displays an increase in project costs of more than 20%. High-profile highway projects in the US, such as Boston’s Central Artery/Tunnel, known as the Big Dig and Virginia’s Springfield Interchange have made engineers, contractors, and the public acutely aware of the problem of cost overrun. For example, the Big Dig was estimated at a cost of US$2.6 billion (1982 dollars) and was expected to be completed at a cost of US$14.6 billion (2002 dollars) with completion then, anticipated for 2005 (NAS, 2003).

halla



63

2.21.5 Factors influencing costs The literature shows that a wide variety of factors influence construction costs. In a study conducted in Newfoundland on highways, Hegazy and Ayed (1998) found that season, location, type of project, contract duration and contract size had a significant impact on individual contract costs. Herbsman (1986) determined that, in addition to input costs of materials, labour and equipment and the total volume of contracts bid each year (so-called bid volume) all influenced project costs. Some factors are intrinsically related to construction organisations which are solely responsible for managing them, whereas others are closely related to socio-cultural, economic, technological and political environments within which most organisations operate. Cost overrun arises primarily because of four factors (Yeo, 1990; Minato and Ashley, 1998):

• external risk due to: o modifications in the scope of a project o changes in the legal, economic and technologic environments

• technical complexity of the project due to: o size o duration o technical difficulty

• inadequate project management due to: o the control of internal resources o poor labour relations o low productivity

• unrealistic estimates because of the uncertainties involved. 2.21.6 The relation between cost accuracy and scope Project scope describes the work to be performed in a project and so a cost estimate heavily depends on this scope definition. Lack of proper scope definition has been stated to be a major source of bad estimates (Cowie, 1987). Vagueness in scope has two implications for cost control.

1. It decreases the accuracy of cost estimates 2. It creates a potential for changes in scope during the construction stage, which

generally results in an increase in cost to both owners and contractors.

The accuracy of a cost estimate is highly dependent on the level of detail in the project scope definition. Peurifoy and Oberlender (1989) have divided the accuracy of an estimate into three levels depending on the preciseness of scope definition. They found that a conceptual estimate prepared from a project scope definition that does not include design information is usually accurate within +40 and –10% of the actual cost. A cost estimate prepared upon completion of the preliminary design is usually accurate within +25 and –5% of the actual cost. A detailed cost estimate prepared upon completion of the final design is expected to be accurate within +10 and –3% of the actual cost.


64

Deciding on the likely cost-variation range of a project is subjective. However, the more experienced the estimator, the greater likelihood that the subjective decision will be based on objective experience (Bradley et al., 1990). One inherent difficulty in having an estimator quantify the likely variation of an estimate is that estimators will not always agree on the magnitude of the variation. It is an established fact that individuals’ attitude to risk vary. Some people are by nature risk-seeking, whereas others prefer to avoid risk. These attitudes are not necessarily based on past experiences in related fields; rather, they reflect the individual’s basic personality. Those individuals with a risk-aversion personality tend to assume that, regardless of the apparent reliability of the data base, events will arise that should be covered by an adequate contingency factor added to the best estimate. By comparison, individuals who are by nature risk-takers will tend to underestimate the likelihood of the best estimate being too low (Bradley et al., 1990). There is frequently a substantial contrast when comparing the importance of early estimates with the amount of information typically available (i.e. scope definition) during the preparation of an early estimate. Although lack of scope definition often leads to inaccurate estimates, early estimates (accurate or not) often become ‘cast-in-stone’, and future estimates are unrealistically expected to agree with the early estimate. In addition to the adverse effect on the accuracy of cost estimates, lack of proper scope definition creates a potential for change or growth in scope during construction. Cowie (1987) observes that the majority of cost growth during the construction period derives from scope growth. Increases in scope, without extending a project delivery date, can only be executed by increasing the resource intensity over and above the planned level. Hetland (1994) points out that this often leads to increased unit cost and hence increased total cost of a project. Design and project specific factors affect the cost estimate of a project and can include such other factors as vagueness in scope, design complexity, and project size (Akinci and Fischer 1998). Engineering designs have a high level of influence on project costs and sometimes unsatisfactory design performance can lead to cost overrun (Barrie and Paulson, 1992). Anderson and Tucker (1994) reported that their survey found that about one-third of architectural/engineering projects miss cost and schedule targets. Chang (2002) notes that, as reported by Smith (1996), there have been few instances where an engineering design is so complete that a project could be built to the exact specifications contained in the original design documents. Many construction problems are due to design defects and can be traced back to the design process (Bramble and Cipollini, 1998). 2.21.7 Scope change In their research study to quantify the impact that project changes have on engineering and construction project performance, Ibbs and Allen (1995) define change as any event which results in a modification of the original scope, execution time or cost of work. Because change may occur throughout all phases of a project, their research focuses on the quantitative impacts that change has on the detailed design and construction phase of projects. They found that project change has a large effect on the financial performance of a construction project. Thomas (1985) studied highway construction programs and reported on selected claims for project changes and cost/schedule overrun on these same projects. The


65

study concludes that project change has a direct effect on costs and schedules of construction projects, primarily cost/schedule overrun. Constructability in the highway industry has also gained considerable attention for adding costs to designed projects. Early research by O’Connor et al. (1991) identified that poor specifications can cause construction rework and delays. Their findings suggest that 22% of all constructability problems are related to ineffective communication of engineering information, plans, and specifications, especially inadequacies in project specifications. Anderson et al. (1999) also confirmed the issue of inadequacies in project specifications in their research on state highways in the Unites States. Chang (2002) reported that on four completed case study projects for environmental and engineering design services for roadway construction projects in California carried out on a cost-plus-fixed-fee basis, cost increases averaged 25%. While the reason for cost overrun can be obvious, the problem still remains that an estimate is a forecast of a cost to be incurred sometime in the future — the problem is that the future is not always predictable. Kayode (1979) reports that project cost overrun are caused by rising costs largely due to inflation, inadequate analysis and inadequate information. Orji (1988) is of the opinion that the causes include certain government fiscal/monetary policies, poor costing of projects, inflation within the economy and some practices of project participants, especially those involving government projects. A further reason advanced for the incidence of project cost overrun is attributed to costing methods (Akpan and Igwe, 2001). The quality of project design documentation has fallen alarmingly over the past 15 to 20 years (Tilley, et al., 2000). It has been reported that many client organisations routinely tender major projects on sketch plans of partially completed documentation to transfer design risk to the managing contractor (McLennan and Jorss, 2006). They also reported that the findings of the task force set up by the Queensland Division of Engineers Australia in 2004 included the following root causes:

• inadequate project briefs based on unrealistic expectations of time and cost • lack of integration along the supply chain linking parties, and between project phases • poor understanding and low skills in risk assessment and management • inadequate use of CAD/computer technology in the design process and in the

compilation of specifications. 2.22 Cost forecasting models A considerable amount of literature advocates that forecast project cost accuracy is very important to both the client (who can have better cost control over the project) and the contractor (who can have a better chance to bid the job with a reasonable profit margin). Many of the research works have concentrated on the accuracy in the construction cost forecasting. Ashworth and Skitmore (1983) suggest that the factors influencing project cost forecasting accuracy are:

• availability of design information


66

• building size • market condition • number of bidders.

The development of cost forecasting models is one of the techniques for assessing project costs, and hence, ultimately, the project’s cost at completion. The development of cost forecasting models is summarised by Raftery and Ng (1993) as:

• First Generation Models: Element cost planning in the UK and by bidding models in the US

• Second Generation Models: using regression analysis • Third Generation Models: probabilistic estimates, frequently based on Monte Carlo

techniques. Raftery (1994) also suggests that range estimating can be developed to show the form/shape of project cost distributions. In 1996, Skitmore indicated this might be done parametrically for tender price forecasts, by fitting one of the standard probability density functions to the cumulative frequency of the tender price forecast/actual values (Skitmore, 1996). Oberlender and Trost (2001) and Trost and Oberlender (2003) report on quantitative data they collected from completed construction projects in the process industry. Their data are analyzed using factor analysis and multivariate regression analysis. The resulting model, known as the estimate score procedure, allows a project team to score an estimate and then predict its accuracy based on the estimate score. They identify six main factors affecting estimate accuracy and, in order of significance, are:

1. basic process design 2. team experience and cost information 3. time allowed to prepare the estimate 4. site requirements 5. bidding climate 6. labour climate.

Weverbergh (2002) comments that market data without background information on costs, uncertainly and privileged knowledge only leads to very crude types of cost estimates, but is likely to be the best that can be provided under the circumstances. In the past, forecasting future highway construction costs has been achieved in basically three ways. Firstly, unit rates of construction such as dollars per lane kilometre by highway type have been used to estimate construction costs in the short term (Hartgen and Talvitie, 1995; Stevens, 1995). However, this method has generally been found to be unreliable, because site conditions such as topography, in-situ soil, land prices, environment and traffic loads differ sufficiently from location to location to make the use of average prices inaccurate for individual projects (Hartgen and Talvitie, 1995). Secondly, extrapolation of past trends, or time-series analysis, has been used to forecast future overall construction costs (Koppula, 1981; Hartgen et al., 1997). Typically in these analyses, construction costs are collapsed down into overall expressions of construction


67

indices. These types of models are usually only used for short-term forecasting because of their reliance on the notion that past conditions and standards are maintained into the future. Thirdly, models have been established that describe construction costs as a function of factors believed to influence construction costs. The relationship between construction costs and these factors is established from past records of construction costs. Typically, these models are used to estimate the cost of individual contracts only. These models, with their relational structure, are the only models expected to provide reliable long-term estimates (Wilmot and Cheng, 2003). Recent research has attempted to apply artificial intelligence to the prediction of completed project costs. These efforts start with the a priori assumption that each project is considered unique. Attempts have been made to develop extremely complex models that incorporate the many factors and their interactions that can affect the cost of a construction project. These efforts have yielded software that requires significant computing power, a sophisticated user, input of subjective information, and significant required input data. Ashley (1989) describes a general performance model for analysing individual construction projects that requires the analysis of many factors such as labour productivity, design, quality, procurement and the interaction between the factors. Leu et al. (2001) point out that during project implementation many environmental variables such as weather, space, congestion and productivity level dynamically affect activity duration, and hence cost. Leu et al. (2001) also assert that activities need to be executed in cost effective ways that are based on the principle of construction time/cost trade off. Several early mathematical and heuristic models have been generated to solve construction time-cost trade-off problems (Panagiotokopoulos, 1977). By measuring the cost of risk as an input into a project’s estimate, the success or otherwise of initial risk assessments may be seen as a variance from the project’s estimated cost at completion. It has been postulated that this can provide a measure of the successful outcomes of projects through defined key performance indicators. Central to any project improvement is the ability to measure aspects of the project outcomes, including the cost of risk, for benchmarking and performance measurement purposes. An early study by Charles and Andrew (1990) identifies factors that influence the construction change order rates causing cost overrun. These consist of:

• size of project • difference between the low bid and the estimate • type of construction • level of competition • quality of the contract documentation • interpersonal relations within the project • policies of the contractor.

It was discovered that a cost overrun rate of 1 to 11% is more likely to occur on larger projects than smaller ones. Contracts with an award amount less than the estimate are found to be more likely to have cost overrun rates above 5%.


68

2.23 Project cost contingency The cost performance of construction projects is a key criterion for project owners. Construction and development is fraught with difficulty and the basic principle of risk analysis is that an attempt should be made to at least identify these risky project items and attach some financial value to them. These amounts can then be added to a project budget as items of possible expenditure. The intention of this notion is that the project budget becomes a more realistic representation of the client’s likely outlay. Some of the project uncertainties will be eliminated or clarified as the planning of the project matures, however some uncertainties will be carried forward to project tender stage. The use of risk premium money is regarded as standard practice in construction. Despite the fact that most project construction owners transfer most risks to other parties in different forms, in many construction projects the owner adds a contingency allowance to the estimated cost in order to avoid project overrun arising from unexpected events. In terms of managing risk on a project, contingency can take many forms. It may be a time allowance in the program of work for delay such as wet weather, a cost allowance in the project cost estimate to account for the residual risk accepted by the project manager or a contingency process in case an event happens. Cost contingency is included within a budget to represent the total financial commitment for a project client and the quantum of such contingency is of critical importance to projects. There appears to be no standard definition of contingency. Patrascu (1988:115) observes that:

Contingency is probably the most misunderstood, misinterpreted and misapplied word in project execution. Contingency can and does mean different things to different people.

More recently, the Association for the Advancement of Cost Engineers (AACE 2000: 28) defines contingency as:

An amount of money or time (or other resources) added to the base estimated amount to achieve a specific confidence level, or to allow for changes that experience shows will likely be required.

In Project Management Institute (2000:199), it defines contingency as: The amount of money or time needed above the estimate to reduce the risk of overrun of project objectives to a level acceptable to the organisation.

The key attributes of the concept of project cost contingency are: • reserve — cost contingency is a reserve of money (AACE, 2000) • risk — the need and amount for contingency reflects the existence of risk in projects

(Thompson and Perry, 1992).

Contingency can be divided into two categories of risk — known unknowns and unknown unknowns (Project Management Institute, 2000; Hillson, 1999) and can be invoked for events within the defined project scope that are:

• unforeseen (Moselhi, 1997; Yeo, 1990) • unexpected (Mak et al., 1998)


69

• unidentified (Levine, 1995) • undefined (Clark and Lorenzoni, 1985; Thompson and Perry, 1992).

Traditionally cost estimates are treated as deterministic, having point estimates for each cost element that are based on their most likely value (i.e. a single value estimate based on the most likely values of the cost elements) (Mak et al., 1998). These single values may or may not accurately indicate the possible value of the estimate, and they certainly do not indicate the possible range of values an estimate may assume (Toakley, 1995). The cost performance of construction projects is a key success criterion for project sponsors. Construction projects are notorious for running over budget (Hester et al., 1991; Zeitoun and Oberlender, 1993). When cost estimates are being prepared for feasibility, planning and then design stages, the cost of risk is reflected by the inclusion of a contingency sum. The contingency sum, usually expressed as a percentage markup on the base estimate is used in an attempt to allow for the unexpected. One of the most commonly used methods for contingency allocation is the ‘classes of estimate’ method. Almost all major companies in every industry group develop their own criteria for the classification of cost estimates. As an example, Blok (1982) defines five classes of estimate as shown in Table 2.6.

Source: Blok, 1982

Table 2.6: Classes of project estimates

Project cost estimates are prepared at different stages over the life cycle of a project. The estimating techniques used and the associated probable error ranges depend on the availability of project information and engineering data. For instance, classes IV and V are basically the study estimates, whereas class III is the budget estimate prepared prior to budget approval and the final commitment of funds for project execution. Class I and II estimates are control estimates where the level of accuracy is high (Blok, 1982). The Department of Main Roads Queensland expects the following contingency allowances shown in Table 2.7 in their project estimates unless there are other reasons why they should be adjusted (Queensland Department of Main Roads, 2000).

halla



70

Source: Queensland Department of Main Roads, 2000

Table 2.7: Contingency ranges in highway estimates

2.24 Estimating methods for cost contingency The practice of presenting project cost estimates as a deterministic figure comprising a base estimate and the addition of a single contingency amount (usually as a percentage addition) has been adopted in the construction industry for a long time for budgeting purposes. Usual practice is for this amount to be a single lump sum with no attempt made to identify, describe, and value various categories and possible areas of uncertainty and risk. Cost contingency is included within a budget to represent the total financial commitment for the project owner. Therefore the estimation of cost contingency and its ultimate adequacy is of critical importance to project owners. Baccarini (2004) details numerous estimating methods available for project cost contingency as shown below:

• traditional percentage • method of moments • Monte Carlo simulation • factor rating • individual risks – expected value • range estimating • regression • artificial neural networks • fuzzy sets • controlled interval memory • influence diagrams • analytical hierarchy process.

When estimating, the most common method of allowing for uncertainty is the addition of a percentage contingency figure to the most likely estimate of the final cost of the known works. Although this contingency can be calculated in various ways as detailed previously, the most common way is to consider around 10% of the estimated project cost (Burger, 2003). Hartman (2000) argues that this is an unscientific approach and thus a reason why so many projects finish over budget. As Yeo (1990) also points out, the most common method of contingency allocation could be regarded as overly simplistic and heavily dependent on an estimator’s faith in their own experience.

halla



71

Quantification of contingency allowances for cost estimating items can also be achieved by applying the risk management processes detailed in AS/NZS 4360 (Standards, 1999). Historical events may be used as a guide; however estimators and project managers need to use their experience and professional judgement to weigh the competing factors to arrive at the most likely value. Where risks are significant and complex, a statistical evaluation such as the Monte Carlo method can be used. The objective of contingency allocation is to ensure that the estimated project cost is realistic and sufficient to contain any cost incurred by risks and uncertainties. This contingency is often calculated as an across-the-board percentage addition on the base estimate, typically derived from intuition, past experience and historical data. This approach is considered arbitrary, as Thompson and Perry (1992:121) observe:

All too often risk is either ignored or dealt with in an arbitrary way: simply adding a 10% contingency onto the estimated cost of a project is typical.

However, Thompson and Perry (1992) outline several weaknesses of using a contingency amount as follows:

• The percentage figure is most likely arbitrarily arrived at and not appropriate for the specific project

• There is a tendency to double count risk because some estimators are inclined to include contingencies in their best estimate

• A percentage addition still results in a single-figure prediction of estimated cost, implying a degree of certainty that is simply not justified

• The percentage added indicates the potential for detrimental or downside risk; it does not indicate any potential for cost reduction and may therefore hide poor management of the execution of the project

• Because the percentage allows for all risk in terms of a cost contingency, it tends to direct attention away from time, performance and quality risks

• It does not encourage creativity in estimating practice, allowing it to become routine and mundane, which can propagate oversights.

Eden et al. (2005) point out that it may be important to require different contingencies for different elements of a project. However, the establishment of a range of contingencies can require a considerable amount of work by estimators, so they simply add on a 10% contingency across the board for example in order to acknowledge the difficulty of pinning down project uncertainty. When Euro Tunnel (the private company that owns the tunnel under the English Channel) went public in 1987 to raise funds for the project, investors were told that building the tunnel would be relatively straightforward. Under Water (1989:37) reported that, in respect to cost escalation, the prospectus read:

Whilst the undertaking of a tunneling project of this nature necessarily involves certain construction risks, the techniques to be used are well proven . . . . The Directors, having consulted the Mâitre d’Oeuvre, believe that 10% would be a reasonable allowance for the possible impact of unforeseen circumstances on construction costs.

Cooper et al. (1985) points out that, in estimating the cost of a large hydroelectric development, the cost variability and uncertainty was acknowledged by incorporating a contingency allowance in the estimate of 10%. This was calculated as a proportion of the


72

total construction cost after subtracting engineering, management and owner’s costs. In this case, the contingency proportion reflects past experience industry practice and the feel of the cost estimating team. Touran (2003) proposed a probabilistic model for the calculation of project cost contingency to counter initial project cost estimates by considering the expected number of changes and the average cost of change. The model assumed a Poisson arrival pattern for change orders and independent random variables for various change orders. From these elements, Touran (2003) calculates the probability of cost overrun for a given contingency level. Touran (2003) also asserts that project owners, such as transportation agencies, who are usually engaged in specific types of construction projects can, by reviewing historical data of a specific transit agency, calculate rates of change, size and distribution of changes and prepare risk profiles or cumulative probability curves for various values of contingency. Touran (2003) also suggests that such outcomes can be used at the budgeting phase of new projects to ensure consideration is given to potential cost overrun after projects commence. Where there is some form of tender documentation provided for bidders, a portion of the contingency will usually be transferred to the provisional sums section in these documents. For construction projects that usually use a government’s fixed quantities contract, the magnitude of the final account variations that comprise additions and omissions can be compared with the contingencies included in estimates. This comparison can be used to assess the accuracy of the allowance made for the contingencies at the early estimate stage. With the traditional approach of informed guessing at contingencies, it can then be perceived that the size and the level of quality of projects will be affected by the contingency amount. Estimators tend to include an inflated buffer in the contingency estimate. Raftery (1994) identifies personal bias and differences in personal risk attitude. Kahnaman and Tversky (1972) refer to this as conservatism. The term conservatism originated from studies of human information processing in that individuals tended to revise their opinions in the light of new evidence to a lesser degree than would be expected of an optimal information processing system. This tendency for humans to be less extreme than optimal in their judgements has been termed conservatism in probabilistic information processing. Moreover, due to the effect of negative sanctions (i.e. imposing a penalty for an underestimate, where tender bids are above the pretender estimate but no reward/penalty for an overestimate), an over-exaggerated contingency is not uncommon in many project estimates. For public works projects, this leads to misallocation of resources as more than sufficient funds are locked up in projects. Musgrave and Musgrave (1984) reported that the mis-allocation of resources severely disturbs the important fiscal functions of public sector spending in the areas of:

1. provision of social goods 2. distribution of income and wealth 3. maintenance of a stable economy in the aspects of employment and price levels.

In the case of public expenditure, the task becomes one of distributing the available fund allocations among competing projects. Having chosen projects for inclusion, the budgeted capital expenditure is then committed for the duration of the program. Sometimes project teams can inflate the contingency allowances in an attempt to avoid the need to seek


73

additional funds if budgets become over spent. However, this inflation can become exaggerated if there is no heavy call on the contingency fund beyond what might reasonably have been expected for the project. Consequently, project budgets can be seriously under spent. The magnitude of under-spending can be so large that it is possible to identify facilities that were previously foregone but could have been included in a construction program in the first place. Explaining situations such as this can be just as embarrassing for clients as seeking additional funds because of budgets being inadequate to meet unforeseen costs. In some areas of the public sector, there is a tendency to remove contingency provisions in budget submission, as contingencies are often seen as fats (Yeo, 1990). Consequently there is no allowance to express the anticipation of risk or for the lack of confidence in project estimates. As well, the engineering and construction complexities of projects are often overshadowed by economic, societal and political challenges. In addition to these challenges, a number of observers suggest that project estimates are purposely misrepresented in quantum in an effort to secure project approval (Flyvbjerg et al., 2002). Contingency can have a major impact on project outcome for a project owner. If contingency is too high it might encourage poor cost management, cause the project to be uneconomical and so aborted. It may also lock up funds not available for other projects or activities (Flyvbjerg et al., 2002). On the other hand, if the contingency allocation is too low, then it may be too rigid and set an unrealistic financial environment, resulting in unsatisfactory performance outcomes (Dey et al., 1996). Under many companies risk management strategies, risks are reviewed at intervals throughout the life cycle of the project and assessments updated to reflect the current level of uncertainty surrounding the project (Flyvbjerg et al., 2002). Risks for which contingencies are provided early in a project may some later time be overcome by further investigation or design modifications. For example, a contingency allowance for rock in cuttings early in the project may be replaced by specific quantities and costs following geotechnical investigations to minimise the specific risk exposure. The amount of contingency is reassessed at project review points to reflect current knowledge and level of uncertainty of the project with a view to forecasting the most likely outcome (Dey et al., 1996). HM Treasury (1993) identifies two major categories of contingency that can be incorporated into construction projects:

• Design contingency – this allows for changes during the design process for such factors as incomplete scope definition and inaccuracy of estimating methods and data (Clark and Lorenzoni, 1985).

• Construction contingency – this is for changes during the construction process. Under a traditional procurement arrangement, the project owner engages others to produce the design before competitively selecting the construction contractor. Subsequently a contract is signed between the project owner and the contractor, which typically contains a variations clause to allow for changes and provide a mechanism for determining and valuing variations. Construction contingency exists to cater for these variations allowable under the contract between the project owner and contractor (Staugas, 1995).


74

Mak and Picken (2000) state that contingency can be compared with the total approved value of contract variations to assess the accuracy of the contingency. Baccarini (2004) analysed project cost data of 48 road construction projects from an Australian government road authority for the estimation of construction contingency. He reports that the organisation uses a traditional percentage approach for estimating contingency. The main findings of his analysis include:

• Construction contingency is on average 5% of award contract value, whilst variations were 10% of award contract value. This shows a shortfall in contingency of 4.6%

• The amount of estimated contingency is significantly inadequate to cater for the total value of contact variations, by an average shortfall of 47%

• There are no significant correlations between project variables and cost contingency that might be used to predict cost contingency.

2.25 Literature summary This chapter has presented some of the crucial findings in the existing theoretical and empirical literature on risks associated with the delivery of construction projects, particularly associated with highways and on project cost overrun. The literature supports the notion that accurate, early cost estimates for engineering and construction projects are extremely important to the sponsoring organisation. Accurate cost estimates are vital for business unit decisions that include strategies for asset development, potential project screening, and resource commitments for further project development. The literature survey has revealed several research studies on highway construction projects which attempt to predict the amount a construction contract might increase while taking into account various factors that could be used in such predictions. Research to date has generally revolved around the cost increase in contracts within projects. Several research studies have demonstrated that changes initiated during construction projects have a large effect on their financial performance. Research also demonstrated that estimating methodology and accuracy of cost estimates can be major reasons for cost increases. Research studies have also been conducted in order to predict the extent to which the cost of a construction contract might increase, taking into account various project prediction factors. Most research has involved considerable effort investigating the construction aspects of project delivery and the impact of that on the performance and other aspects of projects. Changes in construction contracts lead not only to increased costs, but also to contract delays which then affect project delivery. In the history of construction, the nature of construction risks has led to many cost overruns. The first step in clients managing risks causing cost overrun is their identification. In the area of highway project construction, the literature review has unearthed little empirical research that has determined client risks leading to cost overrun associated certain types of highway projects and their delivery methods. The literature review has identified many important stages during the delivery of projects where the management of construction risks are important. The literature review concludes that, although many of the risk techniques are effective for the particular types of projects they were applied to, the approaches generally treat projects as independent entities with little attempt to categorise projects into specific project sub-types from which detailed


75

analyses can be undertaken. In the past, highway cost estimating models have been established that describe construction risks as a function of factors believed to influence construction costs. Typically, the models established in this manner have been used to estimate the cost of individual contracts only, not project budgets. Empirical research is now required to assess whether certain highway projects properties and delivery methods indicate higher propensity to cost overrun. The research needs to be focused on the client, not the contractor, and with a particular focus on overrun relating to the decision-to-build baseline budget. The outcome from such an analysis would enable clients to be in a better position to take advantage of these findings when considering specific types of future highway construction. Data from different highway types should be studied to identify if there is any correlation between project cost overrun and the type of highway project constructed. Chapter 3 follows and describes the methodology adopted in the research to assess client cost overruns in highway projects.


76

CHAPTER 3 Research methodology

77

CHAPTER 3

Research methodology

3.1 Introduction Research methodology is the collection of methods to change or create new beliefs and knowledge. The term methodology means different things to different people and its meaning continues to evolve (McCuen, 1996). Research methodology deals with the methods for creating knowledge about the world and the interpretation of this knowledge in light of the ontological and epistemological positions (Reich, 1994). Ontology deals with the nature of the things we know about the world or the nature of the world, while epistemology deals with the relation between humans and their knowledge. Typical epistemological questions are:

• What can we know? • How do we know? • What is truth? • Is there a priori knowledge, and if so, of what?

The following section provides a general description of the research strategy adopted for this thesis, as well as justification of the methodology. 3.2 Research strategy The term research refers to the development a new body of knowledge. Scientific research refers to the systematic, controlled, rigorous, empirical and critical investigation of a hypothetical proposition about a presumed relation in order to find the solution to a problem or discover and interpret new knowledge. McCuen (1996) describes scientific research as being the investigation of phenomena via practices consistent with the method of science. Scientific investigation and the verification of beliefs about real world phenomena involve empirical research based on the belief that all knowledge originates in experience (Stone, 1978). Empirical research deals with facts that have objective reality and is the generic process followed in the research presented in this thesis. Engineering and construction management research are seen as systematic processes of discovering, acquiring, and using new knowledge to solve problems of theory or practice. The steps followed in an empirical study of a phenomenon are:

1. Observation: an informed and critical questioning of an existing phenomenon leading to the problem statement and the research question

2. Hypothesis: a formal expression of a preconceived factual relationship which provides a tentative explanation or solution to the problem

3. Experimentation: the design of the study leading to a systematic and controlled testing of the hypothesis

4. Induction: a generalisation of the experimental results that leads to a conclusion about the formal statement of the theory.


78

As well, there are a number of different approaches to empirical research as shown in Figure 3.1.

Source: Stone, 1978 Figure 3.1: Empirical research strategies

A common misconception in research is that the various research strategies should be arranged hierarchically in the following manner (Yin, 2003):

• case studies are appropriate for the exploratory phase of an investigation • surveys are appropriate for descriptive phase • experiments are the only way of developing explanations for casual inquires.

However, this hierarchical view of research strategies is incorrect in that experiments with an explanatory motive have always existed. Yin (2003) also points out that each research strategy is not distinguished by this hierarchy, but by conditions such as the:

• type of research question posed • extent of control that an investigator has over actual behaviour events • degree of focus on contemporary as opposed to historical events.

The first and most important condition for differentiating among the various research strategies is the identification of the type of research question being asked. The second condition is the current state of knowledge of the variables involved in the research (Bennett, 1991). In Chapter 1, the following two research questions were formulated:


2. How does the amount of highway cost overrun in such highway projects correlate with their project types, size, delivery processes and client project risks when historical project data is analysed?

halla



79

Table 3.1 displays the conditions for various research strategies. The what and how research questions are likely to favour the use of archival analysis, history or case study research strategies

Source: Yin, 2003

Table 3.1: Situations for differing research strategies

Historical research is concerned with historical events or approach to contemporary events or problems. As well, historical research can be utilised to help solve a current problem through an examination of what has happened in the past (Bennett, 1991). The three basic steps in the historical method are the collection of data, the analysis and criticism of the data and finally its presentation. Bennett (1991) points out that when the problem under investigation is of more recent historical origin, then data and facts can be available but may not necessarily be collected in the form needed in order to describe and understand the problem. There are two ways to look at the historical approach to a research problem:

• Data may be collected to describe the field at a particular point in time (cross-sectional study), or

• The development of the problem may be described over a period of time (referred to as a longitudinal historical study).

On the other hand, although a survey is a strategy that allows a researcher to collect data directly from sources in a systematic fashion, there are some important disadvantages (Stone, 1978). These include:

• decreased willingness or refusal of people to respond to survey probes, because of suspicion, fear and other form of resistance

• most surveys are one-offs and so their capacity to generate data with which to test causal connections among variables is limited

• the sample survey can be an extremely expensive research strategy because of administrative and personal costs

• standardised response formats of many sample surveys may force respondents to subscribe to statements they don’t fully endorse

• surveys that are used to collect data may have low response rates.

halla



80

This research adopts the historical analysis approach as the required methodology as it provides an insight into the current research problem through the examination of what had happened in the past using analysis, analogy and trend extrapolation of historic data. 3.3 The research procedure The research procedure for this thesis includes for the following:

• clear understanding of the problem being studied • definition of the research objectives • description and justification of the point of departure • identification of fundamental research issues • definition of all the elements and components that comprise the investigation • a clear methodology to perform the investigation.

The approach entails researching construction delivery practices to identify risk occurrences as well as risk constraints and processes to minimise client risk exposure during project delivery that lead to cost overrun. It has also provided a means for the development of a consensus of risk factors based on expert opinions and trend exploration and model development of cost overrun based on historical project data and project attributes. The main activities of this investigation have been presented in Figure 3.2 which provides a detailed explanation of the research process based on the research plan.

Figure 3.2: Main research activities

Literature review

Establish study data. Build databases for

data collection.

Determine cost overrun factors.

Determine highway project types.

Project

construction risks

Factor analysis of cost overrun

data for highways

Develop principal cost

overrun factors in project types

Prob

lem

iden

tific

atio

n

Project attributes. Modelling of cost overrun factors using multivariate regression analysis.

Conclusions Recommendation Further research options

Data / information/

knowledge/ experience

Results


81

3.4 Research steps The five stages involved in the conduct of the research are outlined in Table 3.2 below. Stage Research procedure 1 Literature research to determine research focus.

2 Establish data sources of highway construction projects.

3

a) Determine cost overrun factors from historic project data

b) Use factor analysis (principal component analysis) and factor rotation on cost overrun factors to consolidate data.

4 Use nominal group technique (NGT) to elicit, review and prioritise principal cost overrun risk groupings and highway project types.

5 Undertake data analysis and statistical modelling using multivariate linear regression analysis. Establish correlations between client risks causing cost overrun, project attributes and project programmed cost.

Table 3.2: Research procedures

The following sections explain the justification and conduct of the individual methodology stages adopted in the research. 3.5 Stage 1: Review literature The aim of the literature review was to examine previous research and identify the gaps in current knowledge. This stage also assisted in determining the context of the research study and help position this work relative to previous works. It also assisted in the conceptualisation of the research areas sufficiently to develop the main focus of the research, influence the research design and generate specific hypotheses to be tested. This first stage focused on a thorough review and evaluation of the research literature relating to the research topic. This included topics such as:

• nature of project risk and uncertainty • organisation risk culture • project risk management • project procurement practices in managing construction risk • improving project delivery methods so as to manage risk • pre-qualification linkages to contract default • bid evaluation and contract selection practices • project budget estimating procedures • final project cost forecasting • role of contingency in managing cost overrun • expert elicitation of project risks.


82

Different media modes, such as project contract documents, electronic databases, direct/telephone interviews, as well as internet search engines were used. The literature review also focused on determining risk cost factors from previous research studies that influence cost changes in construction projects. Also, in this first stage, unstructured interviews were conducted to initially collect ideas rather than collect data. The primary objective of these interviews was for them to serve as fact-finding missions, and their largely unstructured format aided in the identification of the important research concepts. Discussions and meetings were held with project clients, construction industry project managers, estimators and construction management academics. 3.6 Stage 2: Establish data source of highway construction projects Semi-structured interviews of construction industry representatives identified project data sources for construction cost overrun. This process also provided some opportunity to evaluate individual practitioner’s tacit knowledge, based on their project management experience. The research stage required a sample of highway infrastructure projects that was appropriate for this area of research. The data sample was required to be large enough to allow statistical analyses of cost overrun factors and project costs. Data on actual costs in transportation infrastructure projects were relatively difficult to come by. One reason is that such data is time consuming to generate. For private projects, such data are often classified as commercial-in-confidence and thus kept from the hands of competitors. Unfortunately, this also tends to keep private project data from the hands of researchers. For both public and private projects, data on actual costs have been held back by project owners because more often than not, actual costs reveal substantial cost escalation that was often considered an embarrassment to project owners. For public sector projects, funding and accounting procedures often made it difficult to keep track of the multiple and complex changes that occur in total project costs over time. For large public projects, reconstructing their actual total costs would typically entail long and difficult archival work and complex accounting. The study for this research into cost overrun into various types of highway construction projects focused on a state highway authority that manages a network of highways and bridges network within the state of Queensland, Australia. The authority is the Queensland Department of Main Roads (QDMR) and it is responsible for the 34,000 kilometres of the state’s highway network, representing 20% of Queensland’s total road network of 174,000 kilometres. It also manages 2740 bridges and 20,000 major culverts. This highway network represents the state’s largest single physical asset, with a replacement value in 2005 of A$26.6 billion. Queensland’s highway network traverses a wide variety of geographic features that make its highway network unique in Australia. These features include:

• freight/tourist passenger routes that traverse coastal topography requiring high capacity hydraulic designs to accommodate high flood immunity and speedy water runoff

• highway routes that traverse mountainous topography requiring limits on climbing and descent design grades to accommodate heavy highway freight movements

• high traffic density urban highway networks


83

• low volume rural main road network interconnecting low rainfall semi-arid regional centres

• high volume national highway and main road networks that are located in coastal tropical climates with extremely high seasonal rainfalls

• medium volume national highway freight corridor that is located predominately in low rainfall semi-arid and arid rural regions but with expansive soils and unseasonal extreme weather conditions necessitating specialised pavement designs

• limited availability of high quality road-making materials in most rural areas, necessitating long distance haulage of road making materials

• regional transport regulations that allow volume loading of multiple articulated cattle road trains requiring a network of high speed, high strength, all-weather and wide highway pavements.

The research was validly limited to the highway network of the state of Queensland. No other highway network in Australia that has this combination of unique and diversified geographic and functional constraints and limitations are managed by the one highway organisation. The research focused on highway infrastructure projects from QDMR that contained data on substantial project cost overruns.The highway project construction data was collected from the published Roads Implementation Program Yearbooks of the Queensland Department of Main Roads (QDMR) over the period from 1995 to 2003 with a portfolio of projects in the sample worth approximately A$1 billion. The data were available as published documents. The project construction cost data was selected on the basis of data availability and all projects that were known to have information on substantial cost overrun were considered for inclusion. The construction projects covered 78 different local authority geographic areas of Queensland out of a total of 125. They represent state highway works in 65% of the administrative areas of Queensland over the stated time period. All projects subsequently analysed were those delivered by the traditional design-bid-build method. The use of project data using this delivery mechanism was justified as there is considerable support worldwide for delivery of infrastructure projects, such as highways, using the traditional method. In a global perspective, the delivery of capital infrastructure investment projects varies in practice from country to country, however, highway infrastructure project delivery in such countries as Australia, Canada, Great Britain, New Zealand, Sweden and the US all use the traditional model of design-bid-build in the great majority of cases (Pakkala, 2002). It is important to note that the project programmed cost was defined as budgeted project construction cost at the time of the decision to build the project. This was consistent with Flyvbjerg et al. (2002) who pointed out that the decision-to-build estimate method focused on decision making and hence on the accuracy of the information available to decision makers. It was this cost estimate at the time of making the decision to build that was of primary interest in the research. This decision-to-build value was also the international standard for measuring the inaccuracy of cost estimates (National Audit Office and Department of Transport, 1992; Nijkamp and Ubbels, 1999). For the purpose of the research, the total project cost estimate included the estimated costs of all component activities from the initiation of the project design to construction finalisation.


84

These included the costs of:

• conducting investigations and developing the design • detailing the design • acquiring land • altering public utility plant • construction • project administration and handover.

The sampling criterion was that of data availability. The highway project construction data available was only available in a consistent format from 1995 and so did not allow an assessment of cost overrun prior to 1995. While the project sample was not perfect, it was considered by the researcher to be the best obtainable, given the limited availability of useful data in the public domain in this field of research. A data integrity process also required to ensure that the data presented was true and factual representations of the highway organisation's historic data. since that data was contained within documents which represented public statements of the organisations planning, construction and maintenance activities of the state government, then it was considered that the facts contained within these documents were true and factual. Each document was authorised by both the state government Minister and the Director General of the state highway organisation. 3.6.1 Indexing of historical road project prices Indicators of price movement of construction outputs are a valuable tool in economic analysis and construction industry output price indexes have been developed to enable the measurement of changes over time in the price of new construction outputs of building roads and bridges. A price index is different to a cost index in that it does not provide an indication of the actual cost of constructing and maintaining roads. Its main purpose is to provide a way of deriving constant prices for inputs to road expenditure and maintenance. The input components to the index consist of labour, materials and plant. The prices that have the most influence on the index are those for the provision of labour, fuel, bitumen, quarry products and plant hire. Among these inputs labour, bitumen and fuel have had the largest average increase over the life of the index .These industry output price indexes are primarily used as deflators in the calculation of volume estimates of capital expenditure in the Australian National Accounts. Construction output indexes are also used for contract adjustment and asset revaluation. Two forms of indices have been specifically available from the Australian Bureau of Statistics (ABS). These are:

1. Price Index of the Output of the Road and Bridge Construction Industry 2. The Road Input Cost Index.

Price Index of the Output of the Road and Bridge Construction Industry: This index was first produced in an experimental form in the March quarter 2000, and first published in the September 2002 issue of Producer Price Indexes, Australia (ABS catalogue no. 6427.0). The Road Input Cost Index (RICI): Price indexes for highway projects in Queensland have been indexed since 1984 and up to 30 June 2004 by the application of a calculated index. The RICI was used in this research to index up the historical highway costs because it provided the only continuous highway focused price indices for the financial years 1995/1996 through to 2002/2003 for which the historic project data were available. The


85

Road and Bridge Construction Price Index was not adopted as the index was only time-series back calculated to an index reference period of 1998–99 = 100. 3.7 Stage 3: (a) Determine cost overrun factors from historic project data. The first step in stage three the research required the determination of highway work types and project cost overrun factors. The available highway data contained individual descriptions of all the work types as well as the reasons for individual projects having exceeded the client’s programmed budget. These work types and reasons for cost overrun were recorded in Excel spreadsheets by the researcher. Where common work types or cost overrun factors occurred across projects, single work types and cost factors were recorded to cover incidences. All unique work types and reasons for cost overrun were recorded individually. The research process used the experience in highway construction and the professional judgement of the researcher to determine the listings of work types and reasons for cost overrun. The focus of this analysis was based on the client’s exposure to project cost overrun, not that of contractors delivering the projects. The client focus required that a number of considerations that were identified in the literature research had to be taken into account when reporting the cost overrun factors. These included:

• the use of design-bid-build contracts that could lead to higher client exposure to design risks

• pre-qualification of contractors that has the potential to limit client risk exposure to contract default

• contract payment types that focused on schedule of rates and bill of quantities • contract clauses that were designed to reduce the client’s exposure to certain

construction risks • tender evaluation techniques • contract provisions that limit the client's exposure to adverse physical and latent

conditions and wet weather events. 3.8 Stage 3: (b) Factor analysis using principal component

analysis and factor rotation on cost overrun factors to consolidate data

The second step in stage three of the research required the development of consolidated groupings of high level project risk factors contributing to cost overrun in highway construction projects. The most relevant classical risk research on qualitative aspects of risk is that of Slovic et al. (1980) in which they utilised factor analysis (principal component analysis) to reduce many qualitative risk characteristics or attributes to a much smaller number of higher order factors. In discussing this original research, Gregory and Mendelson (1993) noted that the original factor analysis conducted by Slovic, Fischhoff and Lichenstein has been granted classic status in the field of risk perception. 3.8.1 Factoring methods There are different methods of extracting the factors from a set of data. The method chosen matters more when the sample is small, the variables are few, and/or the communality


86

estimates of the variables differ. By far the most common form of factor analysis is Principal components analysis (PCA). PCA seeks a linear combination of variables such that the maximum variance is extracted from the variables. It then removes this variance and seeks a second linear combination which explains the maximum proportion of the remaining variance, and so on. This is called the principal axis method and results in orthogonal (uncorrelated) factors. PCA analyses total (common and unique) variance. The PCA technique of factor analysis was considered appropriate because of the limited a priori knowledge available about the number of different cluster relationships that could be expected for the data sample. The literature also indicated that PCA provided a deterministic method to group elements into meaningful subdivisions in order to overcome multicollinearity problems in the project data. As well, it was a statistical procedure that could uncover relationships among many variables and in the context of this research the variables were the specific cost overrun reasons in highway construction projects. In the factor analysis technique, correlations and interactions among variables are summarised into a small number of underlying factors. The method aimed at identifying key variables or groups of variables that controlled the cost overrun system under study. In adopting PCA, there were four decisions that needed consideration as part of the factor analysis methodology. These were:

1. method of factor extraction 2. sample size 3. type of factor rotation 4. number of reduced factors (components) to be adopted.

The next sections look at these four requirements. 3.8.2 Method of factor extraction There are different methods of extracting the factors from a set of data. The method chosen matters more when the sample is small, the variables are few, and/or the communality estimates of the variables differ. By far the most common form of factor analysis is Principal components analysis (PCA). PCA seeks a linear combination of variables such that the maximum variance is extracted from the variables. It then removes this variance and seeks a second linear combination which explains the maximum proportion of the remaining variance, and so on. This is called the principal axis method and results in orthogonal (uncorrelated) factors. PCA analyses total (common and unique) variance. The PCA technique is appropriate where a limited a priori knowledge is available concerning the number of different cluster relationships that could be expected for the data sample (Hair et al., 1998). PCA also provides a deterministic method to group elements into meaningful subdivisions in order to overcome multicollinearity problems in the project data. 3.8.3 Sample size for factor analysis Guidelines for the minimum sample size needed to conduct factor analysis suggested a minimum sample size of 100 to 200 observations (Guadagnoli and Velicer, 1988). Some researchers have suggested the ratio of sample size to number of variables as a criterion, with recommendations ranging from 2:1 through to 20:1. PCA requires a large sample size. It is based on the correlation of the variables involved, and correlations usually need a large


87

sample size before they stabilise. Tabachnick and Fidell (2001: 588) have advised the following regarding sample size: 50 observations is very poor, 100 is poor, 200 is fair, 300 is good, 500 is very good and 1000 or more is excellent. As a rule of thumb, a bare minimum of 10 observations per variable is desirable to avoid computational difficulties. 3.8.4 Type of factor rotation employed In order to facilitate the interpretation of factors, factor analysis requires the rotation of axes. The rotation procedure does not affect the goodness-of-fit of the factor solutions but serves to make the output more understandable. Three rotation techniques are in general use: varimax, equimax and quartermax. Of these, the most popular is Kaiser's varimax algorithm, which is known to provide the best parsimonious analytical solution (Harman, 1967). This minimises the number of variables with high loadings on factors, thus causing the factor loadings of each variable to be more clearly differentiated. 3.8.5 Number of factors in analysis An important decision is the determination of the number of factors to be extracted and several guidelines are available for this. One of the most common is the minimum eigenvalue criterion. Essentially this method involved taking the principal components of all the variables, ranking their eigenvalues from highest to lowest, then the number of eigenvalue greater than one was selected as the criteria for the number of factors included in the analysis. The scree plot of eigenvalues against the number of factors was also used as part of this process. The plot was used to show the point at which the eigenvalues began to level off and this plot was used as a cut-off point to support the adoption of the desired number of factors (Velicer and Jackson, 1990). 3.8.6 Characteristics of samples in the factor analysis Several pre-tests are available to measure the sample characteristics necessary for successful factor analysis. One is the Kaiser Meyer Olkins test (KMO) for sampling adequacy. KMO values vary from 0 to 1.0 and values closer to 1 are better. An overall KMO should be 0.60 or higher to develop successful factor analysis (Hutcheson and Sofroniou, 1999). Another test is the Bartlett Test of Sphericity, which checks if the sample was randomly drawn from a population in which the correlation matrix was an identity matrix. This uses the determinant of the correlation matrix to tests the null hypothesis that the correlation matrix is an identity matrix using a chi-square approximation (Bartlett, 1947) and is particularly relevant when dealing with a relatively small sample of data (<100) and with a relatively large number of variables (>10). The Bartlett test sets up a chi-square approximation to determine whether the developed correlation matrix is an identical matrix in the analysis. Bartlett’s sphericity test is particularly relevant when dealing with a relatively small sample of data (<100) and with a relatively large number of variables (>10). A minimum value of 700 was adopted for the Bartlett test (Hutcheson and Sofroniou, 1999). A further test is to examine the Anti-Image correlation matrix. The diagonals on the matrix should have an overall Measure of Sampling Adequacy (MSA) of 0.5 or above (Hair et al., 1998). Individual variables can be considered for elimination from the analysis if they are


88

low on this measure. 3.9 Stage 4: Use nominal group technique (NGT) to elicit, review and prioritize cost overrun risk groupings and highway project types Stage four of the research methodology involved the elicitation of the grouping of cost overrun factors from experts. Elicitation using experts has been a formal process of obtaining information or answers to specific questions where the information was highly subjective. The process has been used by quite a number of disciplines (Ayyub and McCue, 1997). Koskinen et al. (2002) explain tacit knowledge as expressing itself in human actions in the form of evaluations, attitudes, points of view, commitments and motivation. The expert elicitation was adopted in order to obtain a comparison between the factors derived from the factor analysis technique and those obtained from carrying out an expert elicitation process. From previous interviews by the researcher, it was important to take note of interviewees’ concerns about time management. In the rapidly paced world of construction management, it was found that an expert elicitation method needed to be efficient because experts in construction management were continually confronted with insufficient time (Laufer, 1996). The selection of the elicitation technique was therefore determined to a large extent by the time that experts were prepared to allocate to the research project. A number of techniques were available that provided the opportunity to evaluate tacit knowledge based on the experience of individual practitioners. The three expert elicitation techniques considered for the research project were:

1. focus group 2. Delphi technique 3. nominal group technique.

3.9.1 Focus group Focus group research had long been prominent in marketing studies (Morgan, 1997), in part because market researchers sought to tap emotional and unconscious motivations not amenable to the structured questions of conventional survey research. It was reported that the interaction among focus group participants brought out a range of perspectives through the language that was used by the members. The reactions of each person spark ideas in others, and one person may fill in a gap left by others. The difference between focus group research and group interviewing was important in considering the appropriate technique. In group interviewing, a standard survey instrument is administered to respondents simultaneously. In contrast, there is no standard instrument in focus group studies, only a topic being explored through the exchange of group discussion. Survey research required a priori theory or at least a list of subtopics as a guide for selection of items to be included in the survey instrument. In focus group research, there was no a priori theory. 3.9.2 Delphi technique Linstone and Turoff (1975) identified the Delphi technique as one form of anonymously eliciting the opinions of experts concerning events and the reasoning behind the opinions. In


89

the process, experts are asked to revise their opinions in the light of the information contained in the feedback. This sequence of questionnaire and revision is repeated until no further significant opinion changes are expected. Sackman (1972) identified important shortcomings in the Delphi which were important in considering the form of elicitation that was adopted in this research project. These shortcomings were characterised as:

• information and questions provided to experts needed to be carefully reviewed to ensure objectivity

• difficulty in summarising and presenting a common evaluation scale to a group that could be interpreted uniformly by the experts

• benefits of experts participating in active dialogue was missed • difficult and time consuming in exploring disagreements between experts.

3.9.3 Nominal group technique Originally developed as an organisational planning technique by Delbecq, Van de Ven and Gustafson in 1971, the nominal group technique is a consensus planning tool that helps prioritise issues (Delbecq et al., 1986). In the nominal group technique, participants are brought together for a discussion session led by a moderator. The nominal group technique has been used as an alternative to the focus group and the Delphi techniques. It presents more structure than the focus group, but still takes advantage of the synergy created by group participants. As its name suggests, the nominal group technique is only nominally a group, since the rankings are provided on an individual basis. The nominal group technique involves a process similar to the Delphi method (Dalkey, 1968) with the objective of the technique being the exploration of ideas for decisions from a team of experts (Adler and Ziglio, 1996). The prime difference between NGT and the Delphi technique was that communication does take place between participating individuals within the NGT method. As well, NGT separated out the process of independent ideas generation, structured feedback, evaluation and aggregation of opinions. It increases individual participation. Detailed research by Gustafson et al (1973) showed that NGT was superior to Delphi. Advantages of NGT were that:

• voting was anonymous • there were opportunities for equal participation of group members • distractions (communication noise) inherent in other group methods were minimised.

Delbecq et al. (1986) summarises the NGT decision making process used as:

1. silent generation of ideas 2. round-robin feedback from group members to record ideas 3. discussion of each recorded item for clarification and evaluation 4. individual voting on priority of items with the group decision being mathematically

derived through rank-ordering or rating.

The use of face-to-face meetings to reach agreement sometimes causes problems because: • a senior member of the group (e.g. a boss or person with a dominant personality)

could sway opinions in a manner inconsistent with the information presented • people could be unwilling to change opinions when stated publicly • people could grandstand or posture by sticking to beliefs that may not have been

appropriate, and so show they were actively engaged in the process.


90

All these issues are needed to be managed properly in an elicitation process (Lifson, 1972). The Nominal group technique was seen as providing the most appropriate method for eliciting expert opinion as it had to potential to demand less valuable time from the experts and still provide the appropriate focus and outcome sought. 3.9.4 Selection of experts for NGT process Possible candidates to the nominal group panel were considered from within several sources:

• professional organisations • project management consultants • contracting organisations • client organisations.

All potential group members were contacted via telephone to determine their willingness to participate in the research and whether they met the following criteria for selection:

• over ten years of contract management experience in highway construction projects • had acted in the role of the client representative in at least three infrastructure

construction projects • willing to participate in the entire process • willing to share ideas regarding construction/contract risk assessment and analysis

techniques as part of the research. A questionnaire was used to obtain relevant information about individuals that profiled potential members. The quality expert elicitation process involves at least three types of participants:

1. specialist 2. analyst, and 3. generalist (Keeney and von Winterfeldt, 1991).

1. Specialist: The specialist should be at the forefront of knowledge in their field, and be recognised as leaders by their peers. They should have the command of knowledge and flexibility of thought to apply their expertise to the issue at hand. As well, they should be capable of translating their knowledge into judgements relevant to the problem. 2. Analyst: The analyst conducts the elicitation of the judgement of specialists. Analysts have knowledge and expertise in statistics, probability theory and decision analysis, as well as having experience with expert elicitation processes. Their task was seen as assisting the specialists to formulate the issues, to decompose them, to articulate their judgements, to check consistency and to help document the specialists’ reasoning. 3. Generalist: It was important to include the generalist in any elicitation in any complex technological problems where the application of the specialist knowledge to the overall problem is not obvious. Generalists are experts with broad knowledge about many or all of the issues under study. Generalists do not need to be at the forefront of their field, but they should excel at communicating with the specialists, especially in the translation of project needs into specialist language. Generalists facilitate the essential bridging between various issues.


91

3.9.5 Group composition Group composition and characteristics are determined by its size and member characteristics. When considering the size of the group, there were two conflicting tendencies to consider. An increase in the group size would be accompanied by an increase in diversity of skills, experience and knowledge. However, the increased group size could also result in a decrease in individual participation as the larger a group gets the more distorted the issues become in favour of those members with low thresholds of participation (Chapman, 1998). Also, as the size of the group increases, the intimacy of interaction decreases, with more members feeling threatened and inhibited from participation because of the increased impersonality of the situation. In addition, the size of group membership tends to be related to cohesiveness with the greater the size of the group, the lower the cohesiveness. Many studies have identified the conditions under which groups tend to become more cohesive. These include physical proximity, similarity of work, homogeneity or similarity of attitudes and values, ease of communication and limiting the size of the group to ideally below 12 (Chapman, 1998). Research in expert elicitation suggested that five specialists were usually sufficient to cover most of the expertise and breadth of opinion (Clemen and Winkler, 1985). As well, they suggested that there may be a need for two or three additional generalists and two or three analysts. For some issues, the diversity of opinions of the set of experts was important, as was the individuals’ expert credentials. 3.9.6 Ranking process A Likert scale was used in the NGT process to record the comfort level that workshop participants felt in regard to decisions made by the group. Likert scales and Likert-type scales (the latter also known as rating scales) are a type of scale that is commonly used to measure human constructs (Vojir, et al.) The direct application of the Likert scale has often been adopted in multi-criteria decision making models. The one disadvantage of this scale method is that it does not allow a fuzzy range of importance to fit through a weighting assessment. For ranking the importance of project risk factors to the potential for cost overrun, it was considered that a collation of the mean and standard deviation of all the participant scores provided for each grouping would not adequately determine an overall relative ranking as this would not reflect any relationship between the groupings (Chan and Kumaraswamy, 1997). Therefore the ranking was determined by calculating the Importance Index for each group, using the following formula: Importance Index = Where w was the weighting, ranging from 1 to 10, given to each factor; W was the highest weight, i.e. 10; f was the frequency of the response; and F was the total number of NGT workshop participants.

100 ∑ ( w f ) WF


92

3.9.7 NGT workshop evaluation The final step in the nominal group technique involved the workshop evaluation completed by the expert group. This sought feedback on the NGT process, the process data provided and the timing and composition of the workshop. 3.10 Stage 5: Undertake data analysis and statistical modelling This final stage of the research process involved the investigation into statistical models which can explain the correlation between the cause, effect and other relationships relating to cost overrun in highway construction projects. While the term model can be used in many ways (Emory, 1980), for this research it referred to the dynamic framework or schema that helps portray key concepts and propositions of the research phenomenon. A model may be highly conceptual or theoretical, developed at the start of a piece of research, and then tested through the process of data gathering, analysis and reasoning. On the other hand, a model may be the end product of research, with little by way of a conceptual framework available at the start of this research (Bennett, 1991). The research model was developed as the end product of the overall research into cost overrun of highway project costs. Results from analysis of models can often provide valuable contributions to knowledge bases used for decision making (Nilsen and Aven, 2003). However, the development of the model also implies the trading off of complexity in order to provide satisfactory representation of the system under study. The ultimate goal of the statistical modelling process was not just to produce a model but also provide one that was sufficiently credible and acceptable to decision makers. In addition, a robust statistical technique was needed that would cope with any undetected data outliers, otherwise the results of the model could have proved misleading. Within the general classification of a model, there have been a variety of model techniques that have been applied in the analysis of risk variables. Some are shown in Table 3.4 below.

Source: Bennett, 1991

Table 3.4: Model techniques

halla



93

The method of quantification and the analysis techniques that can be used are dependent on each other, and both are restricted by the information available. Some of the techniques noted, particularly the network-based solutions, require a large amount of information that is not always available. Monte Carlo analysis technique has often been used in simulation and sensitivity analysis to determine the significance of individual sources of project risk and other project factors such as cost overrun (Flanagan and Norman, 1993). However, given that a sensitivity analysis is univariate and the effects of project cost overruns are numerous and vary simultaneously, the use of Monte Carlo analysis in this research was not considered appropriate. Data mining was seen as a useful research tool for pre-processing structured information from available data and using statistical analysis to uncover patterns and relationships hidden in project related databases. It was reported that data mining compresses even more value out of repositories of information (Marco, 2000). Multivariate statistical analysis is a scientific inference used in analytical work where the problem of inferring from sets of performance measurements on a number of individuals of objects is constantly faced. The history of science confirms that such inferences can be successful, and can handle inferred reality as well as a means of reducing the number of variables. Researchers in sciences such as behavioral, biological or physical have long since abandoned sole reliance on classical analysis methods (El-Choum, 1994). The construction industry has been seen as a good example as each project is unique and therefore, separate treatment is needed to overcome major difficulties that cannot be noticed otherwise. With multivariate statistical techniques two functions can be derived — these correspond to the distinction between descriptive and inferential statistics. Descriptive statistics requires no assumptions whatever about the distributions from which observations have been sampled, while most common multivariate significance tests have been based on homogeneity and normality assumptions. Multivariate regression technique was seen as the most effective tool to manage multiple project variables in the development of a model to determine relationships between projects, project risks and project cost overrun. Multivariate regression has been the most common method of modelling construction costs in the past (Koppula, 1981; Blair et al., 1993; Elhag and Boussebaine, 1998). Multivariate regression is the estimation of the conditional expectation of a random variable given other random variables and involves providing model solutions which could be used as the basis for decision making (Mason et al., 1989; Neter et al., 1990). Preliminary data analysis showed, on examination, a degree of linear relationship between dependent and individual independent project variables. This indicated that multivariate regression was ideal for examining project variable relationships in this type of data (Lewis-Beck, 1993). Linear statistical models have been used for several reasons:

• they can easily be used by construction site engineers and management • visual data inspection shows linear trends in initial project cost data using scatter

diagrams. The multivariate statistical analysis method is designed for an assortment of descriptive and inferential techniques that can either predict or measure sets of variables as found in construction cost forecasting. Regression analysis can summarise data and qualify the nature


94

and strength of relationships among the variables. New values of dependent variables based on observed values can also be predicted by using regression analysis. The method was used to assesses the strength of the relationship between each of a set of explanatory variables (sometimes known as independent variables) and a single response (or dependent) variable. Although there were several alternative techniques available in regression, the research method adopted was the method of least squares. Before using regression analysis, it was necessary to understand assumptions and limitations of the processes. The following key factors were considered when the multivariate regression model was developed (Carver and Nash, 2005):

• problem formulation • adequate, high quality project data • selection of appropriate project variables.

Problem formulation: Studying the relationship of multiple project and cost overrun variables is especially troublesome because the desired knowledge is complex in nature and many different combinations of factors are needed to be examined. As well, it is impractical and difficult to make use of a large number of parameters in the model. However, with the complexities in construction, these parameters were amplified and it was necessary to identify the key factors before they were used in the model. The focus of this part of the research project was a decision-oriented model in the highway project construction environment which had a statistical approach and which used the relationship between qualitative project variables in order to define the correlation between project cost overrun and the number of variables associated with the projects being analysed. Adequate, high quality data: The project data consisted of two types — quantitative and qualitative. Quantitative data represented the quantity or amount of an element. As an example, the dependent variable of the proposed model (cost estimate percentage overrun) is a quantitative variable (Marco, 2000). In contrast, qualitative (or categorical) data, for example the project delivery method or the reasons for the cost overrun, require no quantitative interpretation and so are only classified into categories (Kantardzic, 2003). Selection of appropriate variables: The aim of this stage of the research was to identify if any project variables had a relationship to the accuracy of project cost contingency, for example project size or location. Any project variables that were found to have a relationship could then be used to predict more accurate project cost contingencies; or simply highlight to estimators that when these variables were present, then there was a need to pay particular attention to them in estimating. Variable relationships can take on many different forms, most of which can be extremely difficult to handle in a regression analysis. In many instances it may not be reasonable to assume a linear relationship between the dependent variable and the independent variables. This fact has often become obvious when constructing useful scatter diagrams of sample observations. For this thesis, it has been assumed that the variables are linearly related and the relationship between Y and each of the independent variables was linear. Multivariate regression analysis was used and applied to the set of historical data to investigate the development of a cost overrun model in terms of a regression coefficient for each explanatory variable. The multiple regression took the form of the following model:


95

Source: Lewis-Beck, 1993.

where Y was a dependent variable (i.e. % or project cost overrun), a0 was a constant indicating the intersect with Y axis, bn were partial regression coefficients, Xn were independent variables, and e was the error term. The standardised versions of the bn coefficients were the beta weights, and the ratio of the beta coefficients was the ratio of the relative predictive power of the independent variables under consideration. The purpose of the model was to predict the value of an observable quantity Y = ƒ(X) in terms of a set of quantities X = (X, X2, X3,….Xn.), and the functional relationship (ƒ) between Y and X. The function ƒ was typically established on the basis of a mixture of accepted intuitive assumptions developed from the researchers experience in highway construction regarding the data being analysed (Nilsen and Aven, 2003). The usefulness of the multiple regression models was judged by the value of the multiple correlation coefficients and by the examination of residuals. The performance of the model was tested using Pearson's correlation coefficients. Applying multivariate regression analysis to a set of data results in what are known as regression coefficients, one for each explanatory variable. These coefficients give the estimated change in the response variable associated with a unit change in the corresponding explanatory variable, conditional on the other explanatory variables remaining constant. The fit of a multivariate regression model can be judged in various ways, for example, calculation of the multiple correlation coefficient or by the examination of residuals. The reason is that the regression coefficients and their associated standard errors are estimated conditional on the other explanatory variables in the current model. Consequently, if a variable is removed from the model, the regression coefficients of the remaining variables (and their standard errors) will change when estimated from the data excluding this variable. As a result of this complication, other procedures have been developed for selecting a subset of explanatory variables, most associated with the response. 3.10.1 Regression analysis basis assumptions The basic assumptions/requirements that needed to be satisfied in the model analysis using least squares estimation to yield reliable estimates of a and b in the regression models, the following are to be satisfied:

• Normality: at each possible value of X , the random disturbances are normally distributed

• Zero mean: at each possible value of X, the mean of the random disturbances is zero • Homogeneity of variance: at each possible value of X, the variance of the random

disturbance is constant • Independence: at each possible value of X, the value of the variance is independent of

all other deviations. If these conditions are not satisfied then there is the risk that any inferences about tests for significance and confidence intervals will become misleading. Because random disturbances cannot be directly observed in a model as the location of a true regression line cannot be


96

known, then it was planned to examine the hypothesised relationship that is known as residuals or the differences between an estimated regression line and observed Y values. 3.10.2 Correlation analysis In order to identify any project variables that may be correlated with project cost overrun, a correlation analysis is undertaken. This technique examines the performance of the models and the relationship between variables. The correlation coefficient (R) is a measure of how the actual and predicted correlate to each other in terms of direction. Values for R range from 0 to 1. The closer the correlation value is to 1, the more correlated the actual, and predicted values are. Pearson's correlation (R) was used to examine the relationship between data that was collected on an interval or ratio scale (Norusis, 2005). Whilst there are several different criteria that can be used for developing the rank order of regression models in terms of goodness of fit, the most often used criteria was the R2 and adjusted R2 statistics. The R2 was adopted in the research as it allowed direct comparison of the best model identified (Neter et al., 1990). R2 (coeffiecient of multiple determination) is a statistical indicator usually applied to multiple regression analysis. It compares the accuracy of a regression model to the accuracy of a trivial benchmark model wherein the prediction is the average of all the example output values. 3.10.3 Statistical regression Statistical regression is a way of computing the method of least squares in stages. In stage one, the independent best correlated with the dependent is included in the equation. In the second stage, the remaining independent with the highest partial correlation with the dependent, controlling for the first independent, is entered. This process was repeated, at each stage for previously-entered independents, until the addition of a remaining independent does not increase R2 by a significant amount (or until all variables are entered). The methodology involved testing within the established multivariate regression analysis for sets of independent variables that explained a proportion of the variance in a dependent variable at a determined significant level (R2). This established the relative predictive importance of the independent variables by comparing beta weights. Hierarchical regression analysis was adopted that determined how much variance in a dependent highway project variable that was explained by one or a set of new independent highway project variables. Testing of the significance of the difference of R2 was adopted and carried out in order to determine if the addition or subtraction of targeted independent variables helped the model significantly. The use of residual analysis was important because most assumptions of multiple regressions cannot be tested explicitly but gross violations, such as outliers or extreme cases, can be detected. When excluded, this can yield a better set of results. The most commonly used multivariate analysis method is the stepwise selection method (Norusis, 2005). However all three of the following methods were adopted in selecting the included and excluded independent variables in the statistical regression analysis:

• forward selection • backward selection • stepwise selection.


97

Forward selection — This method starts with a model containing none of the explanatory variables. In the first step, the procedure considers variables one by one for inclusion and selects the variable that results in the largest increase in R2. In the second step, the procedure considered variables for inclusion in a model that only contained the variable selected in the first step. In each subsequent step, the variable with the largest increase in R2 is selected, until, according to an F-test, further additions are judged not to improve the model. Backward selection — This method starts with a model containing all the variables and eliminated variables one by one. At each step the variable chosen for exclusion was that leading to the smallest decrease in R2. The procedure was repeated until, according to an F-test, further exclusions did not represent a deterioration of the model. Stepwise selection — This method involved the combination of the previous two approaches. It starts with no variables in the model. Then variables are added, as with the forward selection method. In addition, after each inclusion step, a backward elimination process is carried out to remove variables that are no longer judged to improve the model. Caution in this process is needed as the stepwise procedure is claimed to be controversial because it includes independent variables based on statistical criteria and not theoretical ones (Bryman and Cramer, 1999). Automatic variable selection procedures are exploratory tools and the results from a multivariate regression model selected by a stepwise procedure should be interpreted with caution. Different automatic variable selection procedures can lead to different variable subsets since the importance of variables is evaluated relative to the variables included in the model in the previous step of the procedure. Care was required in the automatic procedures for selecting subsets of variables (Agresti, 1996). 3.10.4 Sample size of data The regression analysis required the adoption of an adequate sample size of project data for analysis (Tabachnick and Fidell, 2001)

• For testing b coefficients, the sample size adopted was N >= 104 + m where m = number of independent variables

• For stepwise regression, N >= 40m was adopted since the stepwise method would easily train to noise and not be generalised in a smaller dataset

• There needed to be at least 10 to 20 times as many samples of data as there were variables otherwise the estimate of the regression was very unstable and unlikely to be replicable

• For the significance test of R2, the sample size adopted was N >= 50 + 8m where m = number of independent variables.

3.10.5 p-value In the multivariate regression analysis, the statistical model required the reporting of appropriate F-tests and t-tests. For each test, the p-value was reported. The p-value derived depended on a given sample and provided a measure of the strength of the results of tests, in contrast to a simple reject or do not reject criteria. The p-value is a measure of how much evidence existed against a null hypothesis, with the smaller the p-value, the more evidence of the null. The p-value is combined with the


98

significance level to make decisions on a given test of hypothesis. In such a case, if the p-value is less than some threshold (usually 0.05, sometimes a bit larger e.g. 0.1, or a bit smaller e.g. 0.01) then the null hypothesis is rejected. If the null hypothesis is true and the chance of random variation is the only reason for sample differences, then the p-value provides a quantitative measure of the strength of the model. Traditionally, p-values less than 0.01 are considered highly significant and less than 0.05 significant. A larger p-value means that a deviation can be due to chance. If the p-value is measured less than some threshold (usually 0.05, sometimes a bit larger like 0.1 or a bit smaller like .01) then the null hypothesis is rejected. Typical interpretation values for p are shown in the following Table 3.5.

Source: Flyvbjerg et al., 2002

Table 3.5: Interpretation of p-values

When a p-value is associated with a set of data, it is a measure of the probability that the data could have arisen as a random sample from some population described by the statistical model. A p-value is a measure of how much evidence you have against the null hypothesis (H0). The smaller the p-value, the more evidence you have. It is possible to combine the p-value with the significance level to make a decision on a given test of hypothesis. The distribution of p-values under null hypothesis H0 is uniform, and thus does not depend on a particular form of the statistical test. In a statistical hypothesis test, the p-value is the probability of observing a test statistic at least as extreme as the value actually observed, assuming that the null hypothesis is true. The value of p is defined with respect to a distribution. Therefore, we could call it model-distributional hypothesis rather than the null hypothesis. This means that if the null had been true, the p value is the probability against the null in that case. The p-value is determined by the observed value. 3.10.6 t-test The t-test is used to test that the regression coefficient is zero and to assess the significance of individual b coefficients. All variables not significant at the 0.05 level or better are removed from the equation. The t-test assumes randomly sampled data for the significance tests and the t-test is not used for dummy variables.

halla



99

3.10.7 Other considerations A major disadvantage of using the '% cost over' as the dependent variable is that it can provide a wider range of data outliers in the regression analysis. One approach in reducing this potential problem may be to divide the projects into different delivery types and then recalculating the '% over-cost' variable according to each project delivery type. This may require more data that are presently not available to the research project. As well, one of the assumptions in using regression analysis is that the independent variables have a constant variance. If this assumption is violated then the '% over-cost' model will be required to be transformed in order to satisfy the assumption. Multivariate linear regression assumes the relationship between variables to be linear. In practice this assumption can virtually never be confirmed. Multivariate regression procedures are not greatly affected by minor deviations from the assumption of linearity and hence no transformation of variables or explicitly allowing for nonlinear components was considered in the analysis. Multivariate regression should not be used for predictions outside the range of the explanatory variables, for example beyond highway projects or the geographic areas of Queensland. Predictions at levels of unobserved variables which are not comparable to the observed research data can result in very misleading predictions (Harrell, 2000). 3.11 Ethical considerations The use and publication of confidential of commercial-in-confidence project data can be of concern to organisations that have provided the information. This can also limit broader publication of research findings. Because the data compiled for this research were derived from publicly sourced documents, there are no ethical considerations required in publishing the data and its subsequent analysis in this thesis. 3.12 Conclusion This methodology describes the process of investigating and assessing the correlations between project risks, project types and cost overruns on highway projects procured within a public highway agency. Aspects of the methodology adopted included: reviewing literature on project risk and project cost overrun, determining and establishing a source of historic project data, recognising project risk factors, determining highway project types and undertaking statistical modelling to establish correlations between project cost overrun elements and project attributes. These research methods were applied to research data consisting of public records of highway construction projects, their budgeted estimates and final costs at completion on projects which had exceeded budget by more than 10 % between 1995 and 2003. These public records were published by the QDMR. The description of the highway project data collected for the research, the analysis techniques using the methodology and the statistics are described in the following Chapter 4.


100

CHAPTER 4 Data collection, analysis techniques and statistics

101

CHAPTER 4

Data collection, analysis techniques and statistics

4.1 Preamble The research methodology has specified five stages for approaching the research questions. Previous Chapter 2 has focused on Stage 1 of the methodology, namely the literature review This chapter focuses on stages 2 through 5 of the research questions. To address the first research question of: What client risks are present during the delivery of highway construction projects in Queensland, Australia that lead to significant project cost overruns? Stages 2, 3 and 4 of the research process have been developed as follows:

• Stage 2: Establish data sources of highway construction projects • Stage 3 (a): Determine cost overrun factors from historic project data • Stage 3 (b): Use factor analysis (principal component analysis) and factor rotation on

cost overrun factors to consolidate data • Stage 4: Use nominal group technique (NGT) to elicit, review and prioritise principal

cost overrun risk groupings and highway project types.

To address the research question of: How does the amount of highway cost overrun in such highway projects correlate with their project types, size, delivery processes and client project risks when historical project data are analysed? Stage 5 was developed to undertake data analysis and statistical modelling using multivariate linear regression analysis and to establish correlations between client risks causing cost overrun, project attributes and project programmed costs. 4.2 Stage 2: Establish data sources of highway construction projects

All road construction project information was collected from the published Roads Implementation Program documents of the Queensland Department of Main Roads (QDMR) over the financial years from 1995–96 to 2002–03. As well, further related contract data for this research were selectively available from within the project databases of QDMR. Table 4.1 summarises the projects that were available for the analyses.

Year project completed Number of projects 1995–96 13 1996–97 24 1997–98 31 1998–99 39 1999–00 36 2000–01 39 2001–02 21 2002–03 28

Total for all years 231 Table 4.1: Number of projects analysed


102

It was ascertained that the data presented was true and factual representations of the highway organisations historic data. since that data was contained within documents which represented public statements of the organisations planning, construction and maintenance activities of the state government and each document was authorised by both the state government Minister and the Director General of the state highway organisation. A sample of that data is given in Table 4.2 below. This sample data referred to highway construction projects completed in the 2002–2003 financial year.

Project number Location

Description of works

Method of

delivery

Programmed cost $m

Actual cost $m %

Mount Lindesay Highway (Brisbane – Beaudesert Road)

Construct interchange

Open tender

8.649 9.835 13.71

13/25A/48

Cost overrun reason: increase due to contract cost higher than originally estimated and change of project scope to include raised central median

Table 4.2: Sample of data of completed highway

As shown in Table 4.2 above, the various columns contained important project data. These data are described in the following sections. 4.2.1 Project number and location The first column of Table 4.2 contains the ‘Project number’. This provides a systematised identifier that includes the geographic Local Authority in which the project was constructed, the name of the road on which it was built, and a sequential number that this project is the x th project constructed on that particular road. It provides the following information about project 13/25A/48:

• Local Authority 13 (Beaudesert Shire Council) • Road Name 25A Mount Lindsay Highway (Brisbane – Beaudesert

Road) • Sequential Number The 48th project ever constructed on the Mount

Lindesay Highway between Brisbane and Beaudesert.

4.2.2 Description of works The third column of Table 4.2 contains the ‘Description of works’ and describes the type of highway project constructed for the particular project. This description also indicates whether the project was a highway or bridge-related project. A refined listing of works descriptions was produced by combining all duplicate work type descriptions and by consolidating like construction processes into representative processes. Table 4.3 below shows the final 17 work types that were developed from the full listing of work descriptions for use in further analysis.


103

Highway project work types 1 Construct interchange 2 Realignment of two lanes 3 Rehabilitate then widen pavement 4 Widen shoulders and sealing 5 Asphalt resurfacing 6 Replace bridge approaches 7 Construct to sealed two-lane standard8 Widen four to six lanes 9 Replace bridge(s)

10 Pavement overlay (strengthening) 11 Duplicate two to four lanes 12 Construct to sealed standard 13 Widen and overlay 14 Pavement overlay (>75 mm) 15 Realign four lanes 16 Miscellaneous works 17 Widen bridge

Table 4.3: Refined listing of highway project work types

4.2.3 Method of project delivery Column four of Table 4.2 shows the ‘Method of delivery’. All projects analysed used the traditional procurement method of having separate design and construction steps. At the time of this research, QDMR supported three viable and performing project delivery sectors across Queensland— the private contracting sector, local governments and RoadTek — which was a constituted government-owned-enterprise operating under the National Competition Policy and Trade Practices Act 1974 which required evidence of procurement best-value-for-money through competitive bids or comparisons for sole invitee work. Five types of delivery methods were recorded in the project data that have been variously employed on projects to satisfy the client’s project delivery policy. The following delivery methods were recorded:

1. Open tender: This is a contract carried out by a pre-qualified private or government-owned enterprise highway contractor in an open tender environment. The project uses the ‘schedule of rates’ contract format

2. Agreed Price Internal Contract (APIC): This represents a negotiated internal contract that forms a formal agreement between the client highway organisation and its internal workforce, but before that internal workforce was constituted as a government-owned-enterprise. The agreement is on the basis that the construction price is a negotiated price and is based on an agreed schedule of rates for the project construction. (This form of delivery was superseded by method 5 described below during 2001.)

3. Agreed Price Performance Contract (APPC): This type of project is an agreement between the client highway organisation and a particular Local Authority and forms a ‘performance agreement’ for the delivery of a project at a negotiated contract price


104

4. The project is located in the Local Authority’s geographic area and uses the Local Authority’s own workforce and its sub-contractors

5. Sole invitee Local Authority: This is a formal agreement entered into between the client highway organisation and a particular Local Authority. The agreement is on the basis that the construction of the project is carried out by the nominated Local Authority in order to provide continuity to that client’s workforce. (This form of delivery was superseded by method 3 described above in 2001.)

6. Sole invitee RoadTech: This is a project delivery method where a formal agreement is entered into between the client highway organisation and its internal workforce. The contract price is a negotiated price based on agreed schedule of unit rates for the project construction items. It is carried out by the client’s own workforce in order to provide continuity to their own workforce.

In the highway data analysed, there were 140 projects categorised as open tender and this constituted 60.6% of the total projects analysed. Forty six (19.6%) APIC contracts were identified and 23 (10%) were APPC contracts. The number of sole invitee Local Authority projects and sole invitee RoadTech projects were 8 (3.5%) and 14 (6.0%) respectively. Table 4.4 shows the composition of the methods of delivery for the projects analysed.

Method of delivery Number of projects Percentage of projects Open tender 140 60.6% APIC 46 19.9% APPC 23 10% Sole invitee local authority 8 3.5% Sole invitee RoadTech 14 6.0%

Table 4.4: Composition of project delivery methods

The APIC project delivery method and the Sole invitee RoadTech delivery methods became essentially the same over the duration of the research data period as the client organisation used their workforce to deliver projects on a negotiated price basis. These two recorded delivery methods were combined for the purpose of future analysis. As well, the APPC project delivery method and the Sole invitee local authority delivery method were also considered essentially similar for the purpose of the research. The client organisation contracted particular local authority organisations to deliver the highway projects using their workforce on a negotiated price basis. Therefore these two recorded delivery methods were combined for the purpose of future analysis. A code was attached to each of the three revised project delivery methods to allow for future analysis. These are shown in Table 4.5.

Delivery description Revised delivery description Projects represented(revised)

Open tender Open tender 60.6% APIC Sole invitee RoadTek

Negotiated price – internal workforce 25.9%

APPC Sole invitee local government

Negotiated price – external workforce 13.5%

Table 4.5 Project delivery code


105

Column three of Table 4.5 shows the combined revised percentages of projects representing open tender and the negotiated price for internal and external workforce project delivery methods. 4.2.4 Programmed cost The programmed cost shown in Column five of Table 4.2 was the client-allocated budget to deliver the project in millions of A$. This was the project budget at the ‘decision-to-build’ stage of the project. This budget was derived from the traditional engineering analysis of the design plans and drawings and the estimate was typically decomposed into line items representing the major activities and acquisition costs of the project. These included:


Project cost variability and project uncertainty was included in each programmed cost by the incorporation of a contingency allowance in the budget. This was calculated as a percentage on cost to the estimate totals of the six cost elements above. The policy of the client organisation was that the same level of project contingency of 10% was attributed across all project budgets over the analysis period. Table 4.6 shows the minimum, maximum, average and standard deviations of the project programmed costs for the analysis period.

Year project

completed

Number of projects

Minimum programmed

cost $m

Maximum programmed

cost $m

Average programmed

cost $m

Standard deviation of

programmed cost

1995–96 13 1.05 6.40 2.37 1.52 1996–97 24 0.88 37.00 4.46 7.93 1997–98 31 1.00 93.30 12.31 25.05 1998–99 39 1.06 41.50 4.09 6.46 1999–00 36 1.00 110.95 10.29 24.25 2000–01 39 1.02 154.66 12.00 32.29 2001–02 21 1.00 31.64 4.58 7.59 2002–03 28 1.00 40.24 6.91 10.10 All years 231 0.88 154.66 7.81 19.67

Table 4.6: Programmed costs of projects

4.2.5 Actual cost The actual cost shown in Column six of Table 4.2 was the project’s reported information about the actual outcome of the project as incurred by the client to deliver the project in millions of A$. The reported actual cost included the total value of change orders etc. in addition to the original programmed cost. It included the actual final costs of the component costs included in the project’s programmed cost. Table 4.7 shows the range of actual projects costs over the analysis timeframe.


106

Year project

completed

Number of projects

Minimum actual $m

Maximum actual

cost $m

Average actual cost $m

Standard deviation of actual cost

1995–96 13 1.38 8.93 3.11 2.12 1996–97 24 1.46 41.66 5.68 9.16 1997–98 31 1.14 121.83 15.17 31.06 1998–99 39 1.21 52.00 5.23 8.15 1999–00 36 1.33 144.59 13.10 32.08 2000–01 39 1.21 171.80 14.42 39.97 2001–02 21 1.24 39.97 5.69 9.43 2002–03 28 1.28 46.89 8.39 12.25 All Years 231 1.28 171.80 9.69 24.17

Table 4.7: Actual costs of projects

4.2.6 Percentage (%) The % cost overrun shown in Column seven of Table 4.2 was determined as the difference between the client’s actual project cost and their programmed cost, expressed as a percentage of the programmed cost for each project. Table 4.8 shows the range of percentages of cost overrun in the projects analysed.

Year project

completed

Number of

projects Minimum % Maximum % Average %

Standard deviation of

% 1995–96 13 12.4 50.0 30.30 11.1 1996–97 24 11.9 187.20 40.94 39.13 1997–98 31 7.00 113.20 27.62 23.13 1998–99 39 10.00 113.31 30.45 25.01 1999–00 36 10.77 95.10 27.51 17.11 2000–01 39 10.49 91.82 26.56 16.70 2001–02 21 10.31 65.54 25.53 15.48 2002–03 28 10.88 60.45 24.58 11.93 All years 231 7.00 187.16 28.88 21.96

Table 4.8: Percentage of cost overrun for analysis periods

4.2.7 Indexing of project costs to 2002–03 out-turn prices In order to evaluate the size of highway projects by means of the individual project expenditure over the full analysis period, all the reported project expenditures were indexed up to 2003 project prices. This process involved the application of price indices to the client project costs for the years 1997 through to 2002. Price indexes for highway projects in Queensland have been available since 1984 and in intervening years up to 30 June 2004 by the application of the Road Input Cost Index (RICI) as detailed in Chapter 3. Price increases since 1999–00 included the impact of the introduction of the Australian government’s 10% Goods and Services Tax (GST). It should be noted that the RICI was an input price adjuster index, rather than a cost of construction index. (A price index is different from a cost index in that it does not provide an indication


107

of the actual cost of constructing roads — rather, its main purpose is to provide a way of deriving constant prices for inputs to road expenditure.). Appendix A contains the RICI indices that have been compiled form the Australian Bureau of Statistics for the period 1984 to 2004. Table 4.9 details the RICI indices adopted over the analysis period. Column 3 lists the percentage factor used to factor up the historical project cost information. These factors ranged from 3.4% to 16.3% and were applied to projects programmed and actual costs for the corresponding financial years in which the projects were constructed.

Year RICI index Factor for 2002–03 prices 1995–96 97.33 16.3% 1996–97 98.44 15.0% 1997–98 100.00 13.2% 1998–99 102.15 10.9% 1999–00 104.29 8.6% 2000–01 106.60 6.2% 2001–02 109.49 3.4% 2002–03 113.23 —

Table 4.9: RICI indices and factors applied to project costs

All the historic project cost data of the client’s programmed costs and actual costs were adjusted to 2002–03 financial year figures by using the % factors in Table 4.9 above. Appendix B contains a full listing of adjusted programmed and adjusted actual project costs that were used in subsequent data analyses. 4.3 Stage 3 (a): Determine cost overrun factors from

historic project data This step in the research required the determination of project cost overrun factors from historic data. The focus of this analysis was based on the client’s exposure to project cost overrun, not that of contractors delivering the projects. A client focus required a number of considerations identified in the literature to be taken into account when reporting the cost overrun factors. These included:



• contract payment types that focused on schedule of rates and bill of quantities • contract clauses that were designed to reduce the clients’ exposure to certain

construction risks • tender evaluation techniques • contract provisions that limit the clients’ exposure to adverse physical and latent

conditions and wet weather events.


108

The available highway data contained individual descriptions of all the reasons for individual projects stated by the client as having caused the client’s programmed budget for the project to be exceeded by 10% or more. A sample of this data was shown under Columns two to seven of Table 4.2. It was considered that the reasons documented were true and factual representations of the historic project data. Since that data was contained within documents which represented public statements of the organisation's planning, construction and maintenance activities of the state government, then it was considered that the individual reasons for projects overrunning in costs contained within these documents were true and factual as each document was authorised for publication by both the state government Minister and the Director General of the state highway organisation. These reasons were transferred from the public documents and recorded in an Excel spreadsheet by the researcher for further analysis. Where common cost overrun factors occurred across projects, single cost factors were recorded to cover incidences. All unique reasons were recorded individually. This step in the research process drew on the experience in highway construction and the professional judgement of the researcher to extract a consistent group of reasons for the project overruns. Where there were common cost overrun factors identified across a number of projects, then a common cost factor was recorded to cover all incidences of those common factors. The research identified 37 factors from the highway data analysed. The final list of cost overrun variables, their symbols and the number of times they occurred across projects are shown in Table 4.10.

Reason for cost overrun Code Incidences across projects

Project acceleration requirement A 5 Constructability difficulty costs C 10 Constructability – under traffic CT 17 Design/project scope change D 95 Design scope change – drainage DD 33 Design scope change – environmental issues DE 19 Design scope change – design error DF 2 Design scope change – pavement materials/depth DM 23 Design preload requirement DPL 1 Design change to subgrade DSG 1 Design scope change – safety audit requirement DSA 4 Extras unspecified EU 6 Government initiative – employment continuity G 1 Government initiative – contribution by developer GCD 3 Government initiative – contribution by local government GCLG 1 Government initiative – contribution by rail GCR 1 Cultural heritage issues H 4 Latent condition – requires design change LD 4 Latent condition – rock encountered LR 7 Latent condition – additional stabilising LSG 3 Latent condition – removal and replacement of unsuitable material LUS 21 Material cost increase – asphalt MA 1


109

Material cost increase – bitumen price MB 1 Material cost increase – earthworks ME 3 Material cost increase – pavement materials MP 11 Material cost increase – client supplied components/materials MPS 4 Material/process quality issue MQ 2 Contract failure – new contract establishment costs N 1 Remote location costs O 7 Project administration cost increase P 8 Quantity increased measure Q 31 Resumption/accommodation works R 10 Services relocation costs S 12 Specification change SC 7 Contract tender price higher than original estimate TH 35 Contract tender price increase due to inflation TCI 1 Wet weather effects/rework WW 8

Table 4.10: Project cost overrun factors derived from historic highway data

Column two of Table 4.10 shows the ‘Code’. This was an alphabetic code assigned to each of the ‘Reason for cost overrun’ entries. This was to allow further data analysis at a later date. There was no logic to the allocation of the alphabetic code letters, other than a very coarse alignment of the code letter(s) with the major descriptor of the reason. As an example, A = Project Acceleration Requirements; WW = Wet Weather effects/rework. 4.3.1 Exclusion of some initial historical project data For the purpose of the research, cost overrun variable ‘EU Extras unspecified’ occurred across six projects. These projects also had other identified variables also attributed to cost overrun in those specific projects. Review of the project data revealed that those six occurrences of ‘EU Extras unspecified’ were all attributed to one type of construction project, namely the upgrading from four lanes to eight lanes of highway. These projects made up the various stages of the Pacific Motorway project that linked Brisbane to the Gold Coast in the south-east corner of Queensland. An interview with a senior motorway project staff member advised that more detailed cost overrun details for the specific projects were not available as research materials as this information now formed parts of various contract finalisation arrangements under specific deeds of agreements between the client and the specific motorway constructors (Creedy, 2004). Therefore, those six motorway projects were excluded from further data analysis of cost overrun reasons. 4.4 Stage 3 (b): Use factor analysis (principal component analysis)

and factor rotation on cost overrun factors to consolidate data Guidelines for the minimum sample size and ratio limits for the number of variables for factor analysis have been discussed in Chapter 3. For the data set in this project, the sample size was 231 and the number of factors was 28. This gave an acceptable ratio of 8.3 (Guadagnoli and Velicer, 1988). Factors which had produced an incidence of one (1) in Table 4.9 were excluded from the factor analysis because they each related to only one incidence. Nine such factors had an


110

incidence of one. The 37 identified cost overrun factors were reduced to 28 for the factor analysis process. Those cost overrun factors were then formed into an index matrix using MS Excel, with the 231 recorded projects displayed down the y-axis. A scaling of zero (0) and one (1) was then established and the matrix completed for all the projects. A one (1) was recorded against the cost overrun factor for a project when a particular cost overrun factor was identified. A zero (0) was inserted in the data file when no other factor occurred in the project. Where a project had multiple cost overrun factors recorded against it, then multiple ones (1) were inserted in the matrix row. Table 4.11 shows a sample of the data matrix developed in Excel for uploading into SPSS Version 12 for the factor analysis.

Project number A C CPI CT D DN DD DE DF DM DPL DSG DSA EU 99/332/10 0 0 0 0 0 0 1 0 0 0 0 0 0 0 63/17D/302 0 0 0 0 0 0 0 0 0 0 0 0 1 0 119/32B/27 0 0 0 0 0 0 0 1 0 0 0 0 0 0 150/548/301 0 0 0 1 0 0 0 1 0 0 0 0 0 0 30/10N/88 0 0 0 0 0 0 0 0 0 0 0 0 0 0 128/43/304 1 0 0 0 0 0 0 0 0 0 0 0 0 0 25/40A/812 0 0 0 1 0 0 0 0 0 0 0 0 0 0

Table 4.11: Sample display of project and cost overrun matrix data used in analysis

4.4.1 Factor analysis test parameters The Kaiser Meyer Olkin (KMO) test that measured sampling adequacy gave a sampling adequacy of 0.460. This was much less than 0.6 that was sought in the Chapter 3 methodology (Kaiser, 1974; Hutcheson and Sofroniou, 1999). This low figure threw initial doubt on the adequacy of the planned factor analysis technique in overcoming multicollinearity in the data. A further test using the Bartlett test of sphericity gave an approximate chi-square of 482.183 and 378 degrees of freedom The associated significance level was small at p = 0.000 for all the factors as shown in Table 4.12.

Kaiser Meyer Olkin measure of sampling adequacy 0.460

Approx. chi-square 482.18 df 378 Bartlett test of sphericity

Sig. .000 Table 4.12: KMO and Bartlet tests

Both the KMO and Bartlett tests indicated that any factor analysis process carried out on the data would produce marginal results (the Bartlett Test was less than the adopted 700 even though the Bartlett sphericity test is particularly relevant to small samples of data <100 and not so relevant to the larger sample of around 230 in this application). After considering the marginal sample characteristic test results, the principal component/factor analysis method was still used as the preliminary method for reducing the large number of cost overrun factors down to a more significant number.


111

An initial analysis of the 28 factors ascertained the proportion of each variable’s variance that could be explained by principal components. Column two of Table 4.12 shows that initial communalities of the 28 cost overrun variables were 1.000 because, by definition, the full orthogonal principal component analysis showed all of the variances in the variables to be explained by all the factors. The extraction communality shown in Column three of Table 4.13 gave the percent of variance in a given variable Variables with high values were represented in the common factor space, while variables with low values were moderately represented.

Cost overrun variable Initial communality Extraction communality A 1.000 0.724 C 1.000 0.655

CT 1.000 0.690 D 1.000 0.746

DD 1.000 0.667 DE 1.000 0.621 DF 1.000 0.637 DM 1.000 0.458 DSA 1.000 0.631 EU 1.000 0.469

GCD 1.000 0.505 H 1.000 0.684

LD 1.000 0.559 LR 1.000 0.629

LSG 1.000 0.613 LUS 1.000 0.608 ME 1.000 0.519 MP 1.000 0.661

MPS 1.000 0.485 MQ 1.000 0.767 O 1.000 0.468 P 1.000 0.661 Q 1.000 0.664 R 1.000 0.556 S 1.000 0.542

SC 1.000 0.682 TH 1.000 0.714

WW 1.000 0.664 Extraction method: Principal component analysis

Table 4.13: Initial and extraction communalities for cost overrun variables

4.4.2 Principal component analysis The principal component analysis was undertaken with the view of reducing the number of initial cost overrun factors by the redistribution of the variance to the first components extracted in a correlation matrix. The method of eigenvalue decomposition was used. Table 4.14 shows the total variance derived from the principal component analysis before rotation.


112

Component Initial eigenvalues Extraction sums of squared

loadings

Total

% of variance

Cumulative % Total

% of variance

Cumulative %

1 1.894 6.76 6.76 1.894 6.76 6.76 2 1.718 6.13 12.89 1.718 6.13 12.89 3 1.601 5.71 18.61 1.601 5.71 18.61 4 1.467 5.23 23.85 1.467 5.23 23.85 5 1.407 5.02 28.88 1.407 5.02 28.88 6 1.342 4.79 33.67 1.342 4.79 33.67 7 1.318 4.70 38.38 1.318 4.70 38.38 8 1.191 4.25 42.63 1.191 4.25 42.63 9 1.134 4.05 46.68 1.134 4.05 46.68

10 1.096 3.91 50.60 1.096 3.91 50.60 11 1.060 3.78 54.38 1.060 3.78 54.38 12 1.042 3.72 58.11 1.042 3.72 58.11 13 1.008 3.59 61.70 1.008 3.59 61.70 14 .979 3.49 65.20 15 .943 3.36 68.57 16 .920 3.28 71.85 17 .886 3.16 75.02 18 .826 2.94 77.97 19 .798 2.85 80.82 20 .764 2.72 83.55 21 .760 2.71 86.26 22 .709 2.530 88.79 23 .636 2.27 91.06 24 .593 2.11 93.18 25 .572 2.04 95.22 26 .522 1.86 97.09 27 .446 1.59 98.68 28 .367 1.31 100.00

Extraction method: Principal component analysis

Table 4.14: Total variance explained – unrotated

Column one of Table 4.14 shows the 28 components analysed. There were as many components extracted during the principal components analysis as there were variables initially used in the analysis, namely 28. The total % variance explained by each cost overrun factor was listed in the 'Initial eigenvalue Column' of Table 4.14. The initial eigenvalues derived were from standardised variables. Column two shows the total eigenvalues in descending order of magnitude with component one accounting for the most variance (96.76%) in the analysis and hence the highest eigenvalue. Component two accounted for as much of the leftover variance as it could, and so on. Column three contained the % of variance that was accounted for by each principal component. Column four of Table 4.13 contained the cumulative percentage of variance accounted for by the current and all preceding principal components. The factors were listed in descending order


113

of variance explained. The third row showed a value of 18.61 which meant that the first three components together account for 18.61 % of the total variance. The three columns of the right half of Table 4.14 represented the extraction sums of squared loadings and reproduced the values given on the same row on the left side of the table. The number of rows reproduced on the right side of the table was determined by the number of principal components whose eigenvalues were 1.00 or greater. There were 13 principal factors that had eigenvalues greater than one (1.000). Column seven displayed the computed variance of each variable explained by the 13 factor model. Since the factors were uncorrelated, the total proportion of the variance explained was the sum of the variance proportions of each factor. The figures in Column seven show that variation was relatively even across almost all components and this indicated a low significance of the principal components in reducing the dimensionality of the original set of cost overrun variables. It showed that the first 13 components collectively accounted for only 61.7 % of the total variability, while the other 15 components together accounted for the remaining 38.3 %. 4.4.3 Scree plot A further test using a scree plot was carried out on the project data. As explained in the methodology in Chapter 3, the scree plot graphed the eigenvalue against the number of initial factors (component numbers) so as to support a particular eigenvalue cut-off level. The plot in Figure 4.1 shows the plotted values that have been derived from the first two columns of Table 4.14. From the 13th component point on, the slope of the line flattened slightly indicating that each successive component was accounting for smaller and smaller amounts of the total variance out to component 28. Components with an eigenvalue of less than 1 accounted for less variance than did the original variable which had a variance of 1. The scree plot indicated that a weak model, comprising of 13 component factors, was feasible and warranted further investigation.

Figure 4.1: Scree plot of cost overrun components

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28Component number

0.5

1.0

1.5

2.0

Eigenvalue


114

4.4.4 Factor rotation Factor rotation was performed based on the varimax method with 16 iterations. It was found that a rotated and ordered factor loading matrix established 13 component groups for the 28 cost overrun variables as supported by the scree plot. The shaded portions of Table 4.15 show clearly the 13 groupings of the 28 cost-overrun factors.


115

Component

1 2 3 4 5 6 7 8 9 10 11 12 13 LUS .717 -.067 .009 .048 -.130 -.010 -.063 .043 -.001 -.002 .079 .239 .028EU .638 .044 -.020 -.020 .150 .006 .060 -.034 -.110 -.061 .021 -.125 -

.024DM .375 -.213 -.099 .019 -.004 -.129 -.085 -.199 .186 .176 -.188 -.148 .275H .016 .758 .026 .000 .014 .012 .014 -.088 -.180 -.216 -.063 -.036 .123

ME -.005 .689 -.029 .035 -.014 -.006 -.011 .022 .155 .131 -.010 .024 -.007

O -.098 .509 -.042 -.038 -.083 -.122 -.020 .012 .250 .324 -.026 -.021 -.065

R -.056 -.070 .706 -.015 .113 -.039 -.008 .160 -.011 .022 -.053 .054 -.042

GCD -.065 -.061 .622 .071 -.208 .057 .025 -.085 .137 -.118 -.128 .024 .029S .101 .129 .622 .022 .145 -.080 -.062 -.137 -.194 .021 .132 -.115 .098

TH -.220 -.058 -.165 -.744 .003 -.122 -.022 .070 -.084 -.080 -.141 .160 .049D -.363 -.114 -.086 .606 -.073 -.180 -.058 -.102 -.391 -.128 -.032 -.068 .027

DE -.038 .237 -.141 .358 .332 .303 -.070 .076 .098 -.127 -.134 .249 -.310

SC .049 -.035 .016 .007 .815 -.045 -.033 -.074 -.017 .024 .017 -.048 .049MPS -.036 -.014 .237 -.256 .450 -.044 -.007 .345 -.105 .077 .055 -.094 -

.099LR .051 -.022 -.016 .046 -.046 .774 -.117 .005 -.012 -.015 -.090 .027 -

.015C -.162 -.066 -.042 -.022 -.028 .667 .295 -.054 -.070 .192 .152 -.112 .097

MQ -.056 .004 -.008 -.062 -.003 .072 .860 -.069 .008 -.048 .062 -.055 .032WW .504 -.031 -.059 .123 -.067 -.124 .565 .152 -.045 .045 -.139 .061 -

.033P .058 -.024 .019 -.170 .025 .020 .025 .787 .031 -.053 -.045 .023 -

.030LD -.083 -.025 -.114 .270 -.133 -.082 -.086 .555 -.058 -.061 -.027 -.171 .297MP -.065 .157 -.009 -.062 -.094 -.053 -.036 .003 .772 .028 .088 -.093 -

.039DSA -.087 -.127 -.078 .197 .421 -.026 .064 -.103 .511 -.182 -.158 .090 .207CT -.056 .061 -.059 -.052 .057 .168 -.095 -.108 -.030 .774 -.073 -.110 .086Q .132 .030 .022 .155 -.056 -.122 .264 .057 .006 .539 .073 .482 -

.042LSG -.148 -.053 -.056 .140 -.006 .067 .032 .069 -.006 .056 .734 .118 .006DD .282 -.041 -.019 -.085 .018 -.114 -.014 -.180 .067 -.144 .695 -.135 .070DF .019 -.021 -.012 -.168 -.045 -.010 -.081 -.085 -.062 -.070 .005 .756 .106A -.017 -.086 -.058 .036 -.052 -.065 -.031 -.079 -.008 -.041 -.061 -.122 -

.823

Extraction method: Principal component analysis Rotation method: Varimax with Kaiser normalisation

Table 4.15: Rotated and ordered component matrix


116

These 13 component factor groupings were expanded into their full descriptions in Table 4.16 below

Component group Code Description LUS Latent condition – removal and replacement of unsuitable

material EU Extras unspecified Component group 1

DM Design scope change – pavement materials/depth H Cultural heritage issues

ME Material cost increase – earthworks Component group 2 O Remote location costs R Resumption/accommodation costs

GCD Government initiative – contribution by developer Component group 3 S Services relocation costs

TH Contract tender price higher than original estimate D Design/project scope change Component group 4

DE Design scope change – environmental issues SC Specification change Component group 5

MPS Material cost increase – client supplied components/materials LR Latent condition – rock encountered Component group 6 C Constructability difficulty costs

MQ Material/process quality issue Component group 7 WW Wet weather effects/rework

P Project administration cost increase Component group 8 LD Latent condition – requires design change MP Material cost increase – pavement materials Component group 9

DSA Design scope change – safety audit requirement CT Constructability – under traffic Component group 10 Q Quantity increased measure

LSG Latent condition – additional stabilising Component group 11 DD Design scope change – drainage

Component group 12 DF Design scope change – design error Component group 13 A Project acceleration requirement Table 4.16: Principal factor groupings from rotated component matrix

A review of the 13 groupings lead to the conclusion that no common factor component names were developed in the principal component analysis that could be allocated to the individual component groups so as to allow consistency in purpose of the group of factors. There appeared to be no evidence of strong correlations within any of the 13 groupings, even after rotation. This was particularly evidenced by the diversity in the construction processes associated with each of the principal component groups. These findings, supported by the weak scree plot demonstrated the invalidity of the factor analysis process in this research. This finding was further supported by the weak sampling adequacy of the initial highway cost overrun data available as determined previously by both the KMO test and the Bartlett test of sphericity. The 13 principal component factor model derived in the factor analysis process could not be used in any subsequent model development. Instead a process of using expert elicitation


117

produced an acceptable number of reduced higher level component factors that could be used in analysis. The development of this elicitation process is explained in the following section. 4.5 Stage 4: Use nominal group technique (NGT) to elicit, review and

prioritise principal cost overrun risk groupings and highway project types

This step of the research methodology was planned to confirm the principal component factors from the factor analysis. Since the factor analysis was unsuccessful, this stage was redefined so as to be used for the elicitation of a final grouping and ranking of cost overrun factors. Nominal group technique (NGT) was used to obtain expert agreement on:

• groupings of highway cost overrun factors and apply a ranking of importance of each in terms of exposure to project cost overrun potential

• a group of generic highway project types and apply a ranking of importance of each to their exposure to cost overrun potential.

4.5.1 Identification and selection of experts Potential group members were initially contacted via telephone to determine their willingness to participate in the research. A questionnaire was then emailed to the potential participants to ascertain their suitability within the group. Members were identified and selected from within the highway organisation from which the cost overrun data was obtained. A copy of the pro-forma questionnaire is contained as Appendix C and the responses to the questionnaire have been summarised in Appendix D. Clemen and Winkler (1985) have recommended five specialists and an additional two or three generalists and two or three analysts comprise a group. For the purpose of the research, a generalist was assumed to be one who had a broad range of experience across all of the stated areas. A specialist was assumed to be a member having at least 10 years experience, predominantly in one of the identified disciplines of estimating, design, construction and management. The expert panel finally adopted included an analyst from a consulting organisation, two generalists and five specialists — those seven from the QDMR. Table 4.17 shows the final group composition of the nine participants. Group type Member

1 Member

2 Member

3 Member

4 Member

5 Member

6 Member

7 Member

8 Member

9 Analyst * Generalist * * Specialist * * * * * Researcher * Table 4.17: Expert group composition

The primary qualifications of the nine members all were in civil engineering. Secondary qualifications were also reported by seven panel members in their completed questionnaire and included:

• 1 x PhD in Engineering • 2 x Master of Engineering Science


118

• 1 x Bachelor of Economics • 3 x Business Management.

The reported spread of minimum, maximum and mean years of project-related experience of the group from the questionnaires are shown in Table 4.18. The mean years shown in Column four indicated a good balance of project management expertise across the group.

Experience area Minimum years Maximum years Mean years Estimating 5 14 10.7

Design 1 17 8.5 Construction 2 15 8.5 Management 3 15 12.2

Table 4.18: Experience profile of expert group membership

As recommended by Keeney and von Winterfeldt (1991), the group members were presented with an overview of the complete study and the issues in preliminary form and given an overview of the formal expert judgement process that they would be involved in. This was to ensure that the members understood what was requested of them and how it fitted into the project. As a way of providing preliminary training of the group in the elicitation process, each member was emailed an overview of the NGT process as well as background information on the project and the project data. A copy of Table 4.10 (Project cost overrun factors derived from highway data) was also provided as well as the detailed explanation of each highway cost overrun factor. Seven examples of high level risk groupings from previous research identified in the literature review were provided to each member in order to give them an idea of what was anticipated of the group. The workshop elicitation process covered six workshop items as follows: 4.5.2 Agenda Item 1: Research project background This involved a presentation to the eight members by the researcher on the background to the research project. 4.5.3 Agenda item 2: Workshop aims/desired outcomes and explanation of

NGT for workshop Group members were provided with details of the workshop aims and desired outcomes. As well, a more detailed explanation was provided to the members on the NGT technique and the importance of the process to the development of a cost overrun model. 4.5.4 Agenda Item 3: Principal cost overrun grouping exercise The aim of agenda item three was to obtain expert agreement on groupings of highway cost overrun factors. The members were again provided with a copy of Table 4.10 (Project cost overrun factors derived from highway data). The factor 'EU–extras unspecified' had been excluded from the list as previously discussed in Stage 2 of the research process.


119

The workshop considered each of these 36 factors and came up with consolidated groupings. The process adopted was as follows:

1. silent generation of grouping ideas in writing 2. round-robin feedback from group members to record each idea of groupings 3. discussion of each recorded member groupings for clarification and evaluation 4. individual voting on grouping options 5. agreement on groupings.

The following list of 10 groupings was agreed by the participants during the NGT process:

• design and scope change (change on project definition) • services relocation • deficient documentation (specifications and designs) • right-of-way costs • insufficient investigations and latent conditions • constructability • environment • contractor risks • price escalation • client project management costs.

The next step in Item 3 was to group the 36 cost overrun factors into the 10 groupings. A matrix grid of the risk factors and the groupings as shown in Table 4.19 was produced and handed out in the workshop

Analysed risk

factors

Des

ign

and

scop

e ch

ange

Serv

ices

rel

ocat

ion

Def

icie

nt

docu

men

tatio

n (S

pec/

desi

gn)

Rig

ht-o

f-w

ay c

osts

Insu

ffic

ient

in

vest

igat

ions

and

la

tent

con

ditio

ns

Con

stru

ctab

ility

Env

iron

men

t

Con

trac

tors

ris

ks

Pric

e es

cala

tion

Clie

nt p

roje

ct

man

agem

ent c

osts

A C CT D DD DE DF DM DPL DSG DSA G GCD GCLG GCR H LD LR


120

LSG LUS MA MB ME MP MPS MQ N O P Q R S SC TH TCI WW

Table 4.19: Cost overrun risk factor grouping worksheet

Members then went through the NGT process again and agreed on the matrix composition so that each of the 36 cost overrun factors mapped to one of the 10 groupings. A Likert scale of 1 for poor fit, 2 for an acceptable fit and 3 for an excellent fit of each factor to its grouping was agreed and then applied by the group. The final mapping and the corresponding agreed Likert scale number are shown in Table 4.20.

Analysed risk

factors

Des

ign

and

scop

e ch

ange

Serv

ices

rel

ocat

ion

Def

icie

nt

docu

men

tatio

n (S

pec

and

desi

gn)

Rig

ht-o

f-w

ay c

osts

In

suff

icie

nt

inve

stig

atio

ns a

nd

late

ntco

nditi

ons

Con

stru

ctab

ility

Env

iron

men

t

Con

trac

tor

risk

s

Pric

e es

cala

tion

Clie

nt p

roje

ct

man

agem

ent c

osts

A 3 C 3 CT 3 D 3 DD 3 DE 3 DF 3 DM 2 DPL 3 DSG 2 DSA 3 G 3 GCD 3 GCLG 3 GCR 3 H 3 LD 3 LR 3


121

LSG 3 LUS 3 MA 3 MB 3 ME 3 MP 3 MPS 3 MQ 3 N 3 O 1 P 3 Q 3 R 3 S 3 SC 3 TH 3 TCI 3 WW 3

Likert scale: 1 = a poor fit, 2 = an acceptable fit, 3 = an excellent fit

Table 4.20: Agreed mapping of cost overrun factors to groupings

4.5.5. Agenda Item 4: Ranking of risk groupings This ranking exercise required the workshop participants to provide numerical scores that expressed their individual opinions on the level of importance of each of the 10 principal cost overrun factors in terms of their cost impact to highway projects. A scale of 1 to 10 was adopted, where a score of one indicated the least impact, and 10 indicated the most impact. All scores of the eight participants were recorded and an overall ranking of each of the groupings was derived. A collation of the mean and standard deviation of all the participant scores that was provided for each grouping was considered to not adequately determine an overall relative ranking as such a method did not reflect any relationship between the groupings (Chan and Kumaraswamy, 1997). The cost overrun group rankings were determined by calculating the Importance index for each grouping, using the following formula: Importance index = Where w was the weighting, ranging from 1 to 10, given to each factor; W was the highest weight, i.e. 10; f was the frequency of the response; and F was the total number of respondents. Each rank in Table 4.21 presents the degree of importance assigned to the groupings.

100 ∑ ( w f ) WF


122

Cost overrun grouping

Exp

ert 1

Exp

ert 2

Exp

ert 3

Exp

ert 4

Exp

ert 5

Exp

ert 6

Exp

ert 7

Exp

ert 8

Impo

rtan

ce

inde

x %

RA

NK

Design and scope change 8 10 10 10 9 10 10 9 95 I

Insufficient investigations

and latent conditions

9 9 7 8 8 7 9 10 84 II

Deficient documentation

(Spec and design)

7 5 9 5 4 9 8 8 69 III

Client project management

costs 10 8 8 7 10 8 1 1 66 IV

Services relocation 4 6 3 8 4 1 6 6 48 V

Constructability 4 6 3 4 3 4 6 5 44 VI

Price escalation 2 4 5 3 7 6 3 3 41 VII

Right-of-way costs 6 3 6 6 6 1 2 2 40 VIII

Contractor risks 1 1 2 2 1 5 5 6 29 IX

Environment 3 2 1 1 2 3 4 4 25 X

Table 4.21: Ranked groupings derived from importance index

The following section describes the composition of the final groupings in their rank I (highest) to X (lowest) order of their cost impact on highway projects. Rank I: Design and scope change (HL/1)


123

Design and project scope change was identified as the most important factor by participants (a score of 95% in the importance index) and was ranked first. The group found that the potential for cost increases in the design or scope change projects was very real and their choice in ranking supported historical data previously analysed and provided in previous Table 4.10. Below are the percentages of analysed projects which fell into this first ranked grouping.

• Design/project scope change (23.6% of projects represented) • Design scope change resulting from drainage, environmental issues, pavement

materials/depth (8.2% of projects represented) • Design scope change as a result of carrying out a safety audit on project

(1.0% of projects represented) • Quantity increase (7.7% of projects represented) • Specification change (1.7% of projects represented).

Rank II: Insufficient investigations and latent conditions (HL/2) Insufficient investigations and latent conditions were identified as the second most important factor group by participants with a score of 84% in the importance index. The group reported that highway project construction incurred significant additional costs if there were insufficient project investigations which ultimately could lead to latent conditions. Contractor claims for latent conditions were usually passed on to the client under most contract provisions. Increases in measured quantities in schedule of rated contracts have contributed to project cost overruns and some of these quantity increases were as a result of adverse latent conditions flowing on to the client in Schedule-of-Rates contracts. The relevant percentages of projects represented in Table 4.10 have also been inserted in the brackets.

• Design preload requirement (0.2% of projects represented) • Design change to subgrade (0.2% of projects represented) • Latent condition – requiring design change (1.0% of projects represented) • Latent condition – rock encountered (1.7% of projects represented) • Latent condition – additional stabilising (0.7% of projects represented) • Latent condition – removal and replacement of unsuitable material (5.2% of projects

represented). Rank III: Deficient documentation (specification and design) (HL/3) Deficient documentation (specification and design) was ranked as the third most important factor by participants with score of 69% in the importance index. The group argued that highway project construction could incur significant additional costs if there were deficiencies in project documentation. Contractor claims for deficient documentation can expose the client to additional project costs. The relevant Table 4.10 percentage of project data has also been inserted.

• Design scope change – Design error (0.5% of projects represented). Rank IV: Client project management costs (HL/4) Client project management cost was ranked as the fourth most important factor by participants (a score of 66% in the importance index). The group argued that highway project construction could incur significant additional costs if there were deficiencies in project documentation. The relevant Table 4.10 percentages of project data have also been inserted.


124

• Project acceleration requirement (1.2% of projects represented) • Government initiative – employment continuity, contribution by developer,

contribution by local government, contribution by rail (1.3% of projects represented) • Material/process quality issue (0.5% of projects represented) • Contract failure – new contract establishment costs (0.2% of projects represented) • Project administration cost increase (2.0% of projects represented) • Contract tender price higher than original estimate (8.7% of projects represented).

Rank V: Services relocation (HL/5) Costs for services relocation was ranked as the fifth most important factor by participants with a score of 48% in the importance index. The group noted the reason for their ranking was that poor scoping and estimates for utility service relocations could result in significant client costs. The relevant Table 4.10 percentage of project data has also been inserted.

• Services relocation costs (3.0% of projects represented). Rank VI: Constructability (HL/6) Constructability of highway projects was ranked as the sixth most important factor by participants (a score of 44%) in the importance index. The group noted the poor input by highway construction engineers into the project planning and design steps could lead to cost overrun for the client. The relevant Table 4.10 percentages of project data have also been inserted.

• Constructability difficulty costs (2.5%) • Constructability – under traffic (4.2%).

Rank VII: Price escalation (HL/7) Price escalation of material components was ranked as the seventh most important factor by participants (a score of 41%) in the importance index. The group noted that, although there was usually adequate provision made in project documentation and budgeting, there were occasions on projects when price escalation outside the normally expected provisions may result in additional costs to the client. The relevant Table 4.10 percentages of project data have also been shown below.

• Material cost increase – asphalt, bitumen price, earthworks, and pavement materials (3.8%)

• Principal supplied components/materials (1.0%) • Contract tender price increase due to inflation (0.2%).

Rank VIII: Right-of-way costs (HL/8) Property acquisitions and adjustments to properties adjacent to highway projects were ranked as eighth by participants with a score of 40% in the importance index. Protracted resumption disputes and settlements were offered as areas of potential additional client costs. The relevant Table 4.10 percentage of project data has also been shown below.

• Resumption/accommodation works (2.5% of projects represented). Rank IX: Contractor risks (HL/9) Contractors risks was ranked ninth by participants (a score of 29%) in the importance index. The group noted that projects that were constructed in remote locations had the potential to incur costs because of the difficulties in obtaining and maintaining adequate project resources. In addition, un-seasonal or excessive wet weather sometimes have the potential to


125

lead to unbudgeted costs for the client where project provisions were silent in such instances. The relevant Table 4.10 percentages of project data have also been shown below.

• Remote location costs (1.7% of projects represented) • Wet weather effects/rework (2.0% of projects represented).

Rank X: Environment (HL/10) Environment was ranked last by participants with a score of 25% in the importance index. The group noted that cultural heritage issues had the potential to draw out planned project timeframes and scopes, and thus incur costs because of the difficulties in obtaining approvals and clearances. The relevant Table 4.10 percentage of project data has also been shown below

• Cultural heritage issues (1.0% of projects represented). 4.5.6. Agenda Item 5 of workshop: Development of highway project types The aim of this activity was to obtain expert agreement on a fully inclusive grouping of highway project types that could be used as a means of collating historic projects for further analysis. Members were provided with a copy of Table 4.3 that contained a broad listing of highway project works which had previously been compiled by the researcher from the initial historical project data. In considering the projects types in Table 4.3, the workshop members discussed the possibility of additional project types or the option of combining existing project types. The workshop members agreed that the list of projects could then be categorised into three broad highway project groupings of highway projects, bridge projects and a miscellaneous category. The NGT allowed for further round robin, workshop discussion and then a voting step that developed the final groupings. The final list of project types consisted of nine road types, two bridge types and one miscellaneous type. These are shown in Table 4.22.

Project construction type Reference

Highway project type R1 Construct to sealed standard (2 lanes) R2 Widen shoulders and sealing R3 Rehabilitate/Strengthen R4 Realignment of two lanes R5 Realignment of four lanes R6 Duplicate two into four lanes R7 Widen four into six/eight lanes R8 Asphalt resurfacing R9 Construct intersection/interchange

Bridge project type B1 Bridges and approaches B2 Widen bridges

Miscellaneous project type M1 Miscellaneous roadworks/traffic furniture and devices

Table 4.22: Final NGT highway project construction types


126

The group also agreed that it would be desirable to differentiate between certain project types where the complexity or location of some projects might require identification at a more specific level. The workshop members agreed that the following project aspects needed to be considered if appropriate historical data became available in the future. Those aspects were whether:

• a rehabilitation project included pavement widening or not • a realignment project incorporated some of the existing road and was built under

traffic (The group referred to this type of project as being constructed at a brownfield site.)

• the project was on a completely new alignment away from the existing highway that it had replaced, thus having none or only a small exposure to highway traffic (The group referred to this type of project as being constructed at a greenfield site.)

• an interchange/intersection or bridge and approaches were to be in an urban or rural environment.

Table 4.23 shows the desired extensions to the agreed project types suggested by the participants if appropriate project data were made available.

Reference Highway project type R1 Construct to sealed standard (2 lanes) R2 Widen shoulders and sealing R3 Rehabilitate/strengthen

R3(NW) Rehabilitate/strengthen (no widening) R3(WW) Rehabilitate/strengthen (with widening)

R4 Realignment of two lanes R4(G) Realignment of two lanes – greenfield site R4(B) Realignment of two lanes – brownfield site

R5 Realignment of four lanes R5(G) Realignment of four lanes – greenfield site R5(B) Realignment of four lanes – brownfield site

R6 Duplicate two into four lanes R6(G) Duplicate two into four lanes – greenfield site R6(B) Duplicate two into four lanes – brownfield site

R7 Widen four into six/eight lanes R7(G) Widen four into six/eight lanes – greenfield site R7(B) Widen four into six/eight lanes – brownfield site

R8 Asphalt resurfacing R9 Construct intersection/interchange

R9(U) Construct intersection/interchange – urban R9(R) Construct intersection/interchange – rural

Bridge project type B1 Bridges and approaches

B1(U) Bridges and approaches – urban B1(R) Bridges and approaches – rural

B2 Widen bridges Miscellaneous project type

M1 Miscellaneous roadworks/traffic furniture and devices Table 4.23: Desired workshop project construction types for future grouping


127

4.5.7 NGT Workshop Agenda Item 6: Workshop feedback and assessment As part of the NGT process, an evaluation of the overall workshop process was carried out. Workshop participants completed four questions about the workshop. A Likert scale was also provided for each question as shown in Table 4.24. As well, a fifth question was provided which gave the participants an opportunity to comment on the workshop in general. The following details the responses of the four questions in the order presented. Seven out of the eight participants completed the Workshop Evaluation document. Question 1: The workshop was well paced and easy to understand. Two participants said that workshop was well paced and easy to understand and five participants said they agreed (Table 4.24).

Participant Strongly agree Agree Disagree Strongly disagree 1 * 2 * 3 * 4 * 5 * 6 * 7 *

Table 4.24: Workshop evaluation for Question 1

Question 2: The information provide in the week prior to the workshop was useful in preparing for and also contributing to the workshop outcome. Two participants strongly agreed that information provided prior to the workshop was useful in preparing for and contributing to the workshop outcome and five participants said they agreed (Table 4.25).

Participant Strongly agree Agree Disagree Strongly disagree

1 * 2 * 3 * 4 * 5 * 6 * 7 *


Question 3: The use of the NGT gave me adequate opportunity to provide input into the workshop. Two participants also strongly agreed that the use of the NGT provided adequate opportunity to provide input into the workshop and five participants said they agreed (Table 4.26).


128

Participant Strongly agree Agree Disagree Strongly disagree

1 * 2 * 3 * 4 * 5 * 6 * 7 *


Question 4: The range and number of attendees was adequate for the workshop outcome Three participants strongly agreed that the range and number of attendees were adequate for the workshop outcome and four participants said they agreed (Table 4.27).

Participant Strongly agree Agree Disagree Strongly disagree 1 * 2 * 3 * 4 * 5 * 6 * 7 *


The following general comments were also provided by five of the participants: 1. Difficult issue to have a one-on-one relationship. Many causes are multi-

dimensional 2. Not certain it consistently sorted out the conceptual levels of causes 3. Good 4. Good 5. Worked well.

Overall, the NGT workshop worked well as supported by the response rankings of the workshop evaluation questions. The expert elicitation in the research adopted the NGT process. The process was straightforward and used the available time of the experts efficiently. Participants in the overall expert elicitation process were very supportive of the application of NGT in the difficult area of identifying client risk in the delivery of projects. There were two important outcomes that were derived during the NGT process. Firstly, 10 project risk groupings were developed that were ranked in importance by the group with the application of the importance index. Secondly, a generic set of 12 highway project types was developed. Both sets of outputs were acceptable for use in the proposed modelling of project cost overrun using multivariate regression analysis.


129

4.6 Application of NGT workshop findings to project data

The high-level (HL) risk groupings that were developed by the experts and mapped to the initial 36 cost overrun factors, as contained in previous Table 4.20, were then mapped back to the initial projects. This was accomplished with the aid of an Excel spreadsheet. Table 4.28 shows a small sample of the Excel spreadsheet that was produced to map the associations of (say) the low level cost overrun risks of D – Design/project scope change, DD – Design scope change for drainage and DE – Design scope change for environmental issues.

Project Number Risk D HL/1 Risk DD HL/1 Risk DE HL/1

47/16B/302 1 1 0 0 36/15A/301 &302 0 0 0 140/900/2 0 0 0 116/26B/31 1 1 0 0 148/17B/302 1 1 0 0 80/10A/772 1 1 1 1 0 150/10M/301 0 1 1 0 8/26A/302 0 0 0 94/36b/19 1 1 0 0 140/902/2 1 1 0 1 1 160/12B/5 0 0 0 30/6204/13 1 1 0 1 1 5/10K/19 1 1 0 1 1 55/99C/15 0 1 1 0 160/103/3 0 1 1 0 99/323/13 1 1 0 0 160/103/6 1 1 0 1 1 140/U18B/61 0 0 0 140/U18B/61 1 1 0 0 80/133/734 1 1 0 0 36/93E/706 1 1 0 0

Table 4.28: Sample of low level to high level risk group mapping to projects

For the purpose of analysis, 1 was inserted in the master data file for each low level risk and its associated HL risk grouping for each project. As well, a 0 was inserted in all low level risk columns when there was no association with a particular HL risk. This provided a visually complete 'picture' of the project mappings.


130

An analysis was carried out to ascertain which HL groupings of cost overrun factors had the most occurrences across the projects analysed. There were 42 HL risk combinations recorded across the project data. Table 4.29 shows the incidences of the HL groupings across the projects.

Row HL/ risk grouping Incidence

1 1 92 2 2 1 3 3 2 4 4 1 5 5 6 6 6 7 7 7 1 8 8 3 9 9 1

10 10 22 11 1.2 5 12 1.3 5 13 1.4 4 14 1.5 19 15 1.6 5 16 1.7 1 17 1.8 1 18 1.9 2 19 2.4 2 20 3.8 2 21 5.6 3 22 5.8 1 23 1.10 10 24 1.2.5 1 25 1.2.6 1 26 1.2.7 1 27 1.3.5 1 28 1.5.6 3 29 1.5.8 3 30 1.6.8 1 31 3.10 1 32 5.10 1 33 6.10 2 34 7.8.9 1 35 9.10 3 36 1.3.6.9 1 37 1.8.10 2 38 2.4.10 1 39 2.9.10 1 40 4.5.10 1 41 1.4.9.10 1 42 1.6.8.10 1

Table 4.29: Incidences across projects for the HL/ risk groups


131

Column one of Table 4.29 shows the HL/1. A decimal format has been used to record when more than one HL risk grouping occurred in a particular project. Rows 1 to 10 of Column 2 of Table 4.29 display the incidences of single HL/1s. Whereas, Row 11 shows that there were five projects where both HL/1 and HL/2 occurred. HL/1 recorded the highest incidences with 92 projects. The next highest recorded 22 incidences recorded for HL/2. There were 19 recorded incidences where a combination of HL/1 and HL/5 occurred in projects. As a further explanation of Table 4.29 above, the last row shows that there was one project which had HL/1, HL/6, HL/ 8 and HL/10 risk groupings that collectively contributed to cost overrun in the one recorded project. It was identified that a significantly high number (92) of incidences were recorded for HL/1, when compared with the rest of the incidences recorded. The composition of HL/1 consisted of the following low-level risk factors that were determined in the previous expert elicitation process:

• Design/project scope change (23.6% of projects represented) • Design scope change resulting from drainage, environmental issues, pavement

materials/depth (8.2% of projects represented) • Design scope change as a result of carrying out a safety audit on project (1.0% of

projects represented) • Quantity increase (7.7% of projects represented) • Specification change (1.7% of projects represented).

There had been 31 incidences of quantity increase previously recorded against projects as shown in previous Table 4.10. As this amount was comprised of 34% of the HL/1 grouping, it was decided to extract the quantity increase incidences from the HL/1 risk group and create an eleventh grouping of Quantity increase (HL/Q) for incorporation in the model analysis. 4.7 Step 4: Undertake data analysis and statistical modelling

The aim of this step was to analyse of historical project data based on statistical theories and concepts that identified direct correlations between particular highway construction project types and project cost overrun. 4.7.1 Model – dependent variable The dependent variable adopted in the model was the continuous variable % cost overrun and was the difference between the client’s actual project cost and their programmed cost, expressed as a percentage of the programmed cost for each project. 4.7.2 Model – prediction variables The initial analysis of the highway cost data has indicated that substantial cost overrun factors needed to be considered when looking at the reasons for project cost overrun. The approach identified project variables that had relationships with projects reporting high cost overrun. The following prediction variables were selected for model investigation and were based on the availability of historical project data:


132

1. Highway geographic project type 1. Highway project construction type 2. Highway project delivery type 3. Indexed highway project programmed cost 4. Highway project high level risk grouping.

Each of these five variables is explained in the following sections. 4.7.3 Geographic project type The reason for including the geographic project type in the proposed model was because there appeared to exist a strong relationship between the remoteness of a project from established workforces and from proven materials and component manufactures that could lead to increases in project costs above those estimated. Drew and Skitmore (1992) identified the density of population and the extent of geographic area as important factors for competitive bidding in building projects. It was therefore postulated that the rural geographic type of highway projects had a higher potential to overrun budgeted costs. 4.7.4 Geographic data and model coding An analysis was carried out on the project data to split projects down into the geographic area in which the project was constructed. This split was on the basis of a project being built in an urban or rural environment. The criterion adopted was whether a project was constructed in a city or a shire area. The adopted split was based on the Local Government Regulation s. 5. This provided the following declaration of classes of local government (LA) areas (Queensland Government, 1993):

• Local government areas are classified as shires unless they meet the criteria to be declared a City or Town and are so declared. They are generally rural areas with significant rural uses or large tracts of undeveloped land in its natural state.

• The criteria for City status require the area to be a regional centre with a total population of at least 25,000, and a population in the urban centre of at least 15,000 at a population density of at least 150 persons for each square kilometre in that urban centre.

There were 60 'rural' local authority areas identified in the data and 13 'urban' areas. The splits into rural/urban are shown in Table 4.30 and were based on the previous shire/city definitions.

LA number LA name Geographic type5 Burdekin Shire Council Rural 8 Banana Shire Council Rural 9 Barcaldine Shire Council Rural

10 Mount Isa City Council Rural 12 Bauhinia Shire Council Rural 13 Beaudesert Shire Council Rural


133

17 Bendemere Shire Council Rural 19 Boulia Shire Council Rural 20 Broadsound Shire Council Rural 22 Bungil Shire Council Rural 23 Burke Shire Council Rural 24 Hervey Bay City Council Urban 25 Caboolture Shire Council Rural 27 Calliope Shire Council Rural 30 Cardwell Shire Council Rural 33 Chinchilla Shire Council Rural 34 Redland Shire Council Rural 35 Clifton Shire Council Rural 36 Cloncurry Shire Council Rural 37 Cook Shire Council Rural 40 Crows Nest Shire Council Rural 41 Croydon Shire Council Rural 42 Dalrymple Shire Council Rural 44 Diamantina Shire Council Rural 45 Douglas Shire Council Rural 47 Duaringa Shire Council Rural 48 Eacham Shire Council Rural 50 Emerald Shire Council Rural 55 Flinders Shire Council Rural 58 Boonah Shire Council Rural 59 Herberton Shire Council Rural 60 Burnett Shire Council Rural 63 Inglewood Shire Council Rural 64 Isis Shire Council Rural 66 Johnstone Shire Council Rural 70 Logan City Council Urban 72 Kilkivan Shire Council Rural 73 Kingaroy Shire Council Rural 76 Caloundra City Council Urban 79 McKinlay Shire Council Rural 80 Maroochy Shire Council Rural 83 Miriam Vale Shire Council Rural 88 Murweh Shire Council Rural 89 Nanango Shire Council Rural 90 Nebo Shire Council Rural 92 Noosa Shire Council Rural 94 Paroo Shire Council Rural 95 Peak Downs Shire Council Rural 97 Pine Rivers Shire Council Rural 99 Pittsworth Shire Council Rural

100 Whitsunday Shire Council Rural 104 Rosalie Shire Council Rural 107 Sarina Shire Council Rural 113 Tara Shire Council Rural 114 Gatton Shire Council Rural 116 Taroom Shire Council Rural


134

119 Atherton Shire Council Rural 120 Mackay City Council Urban 122 Waggamba Shire Council Rural 125 Bowen Shire Council Rural 128 Cooloola Shire Council Rural 130 Wondai Shire Council Rural 133 Mareeba Shire Council Rural 134 Richmond Shire Council Rural 140 Brisbane City Council Urban 141 Bundaberg City Council Urban 146 Maryborough City Council Urban 148 Ipswich City Council Urban 149 Toowoomba City Council Urban 150 Townsville City Council Urban 158 Cairns City Council Urban 160 Gold Coast City Council Urban 161 Gladstone City Council Urban

Table 4.30: Split of rural and urban geographic types

For the purpose of analysis, 1 was inserted as a categorical variable in the master data file for all projects constructed in a rural environment and a dummy value of' 0 for when the project was not a rural project. Conversely, a 1 was inserted when a project was classified as urban, and a dummy 0 for the null case. A sample of this data coding for the geographic area is shown in Table 4.31.

Project number Rural projects Urban projects

37/91A/20 1 0 160/203/3 0 1 76/153/3 0 1 45/655/18 1 0 5/10K/809 1 0 148/3042/2 0 1 140/U15/44 0 1 140/U16/819 & 820 0 1 37/90C/36 1 0 25/126/23 1 0 70/108/300 0 1 120/856/3 0 1 48/21A802 1 0 66/814/5 1 0 48/6404/3 1 0 34/1102/17 1 0 92/140/11 1 0

Table 4.31: Data coding for geographic types


135

4.7.5 Highway project construction type reference number The type of project construction in the proposed model was included because it was thought that a strong relationship existed between the degree of difficulty in scoping, designing and constructing highway projects which had multiple lanes and high strength pavements. It was therefore postulated that highway projects involving duplication to multiple lanes had a higher potential to overrun budgeted costs. Conversely, it could have been postulated that bridge projects would have less cost overrun potential because it was easier to design and construct typically generic highway bridge types. 4.7.6 Construction type data and model coding Twelve individual highway project construction types were developed as part of the expert elicitation process. They were detailed in Table 4.22. For the purpose of further data analysis individual reference numbers were coded to each of the 12 project construction types in Table 4.32.

Code Construction project type Reference numberR1 Construct to sealed standard (2 lanes) Project type 1 R2 Widen shoulders and sealing Project type 2 R3 Rehabilitate/Strengthen Project type 3 R4 Realignment of two lanes Project type 4 R5 Realignment of four lanes Project type 5 R6 Duplicate two into four lanes Project type 6 R7 Widen four into six/eight lanes Project type 7 R8 Asphalt resurfacing Project type 8 R9 Construct intersection/interchange Project type 9 B1 Bridges and approaches Project type 10 B2 Widen bridges Project type 11 M1 Miscellaneous roadworks/traffic furniture and devices Project type 12

Table 4.32: Construction project type reference number

For the purpose of analysis, 1 was inserted as a categorical variable in the master data file for all projects constructed in a project type 1 and a dummy value of' 0 for when the project was not a project type 1. As well, 1s were inserted when a projects were successively project types 2 to 12 inclusive, with the dummy value of' 0 inserted for when the projects were null for the particular type.


136

Project number Project type 1

Project type 2

Project type 3

Project type 4

Project type 5

Project type 6

37/91A/20 0 0 0 0 0 0 160/203/3 0 0 0 0 0 1 76/153/3 0 0 0 0 0 0 45/655/18 0 0 0 0 0 0 5/10K/809 0 0 0 0 0 0 148/3042/2 1 0 0 0 0 0 140/U15/44 0 0 0 0 0 0 140/U16/819 0 0 1 0 0 0 37/90C/36 1 0 0 0 0 0 25/126/23 0 0 0 0 0 1 70/108/300 0 1 0 0 0 0 120/856/3 0 0 0 0 0 0 48/21A802 0 0 1 0 0 0 66/814/5 0 1 0 0 0 0 48/6404/3 0 0 0 1 0 0 34/1102/17 0 0 0 0 0 0 92/140/11 0 0 0 0 0 0

Table 4.33: Sample data coding for only project types 1 to 6

4.7.7 Project delivery types The reason for including the type of project delivery of the highway construction in the proposed model was because there may be a strong relationship between the competitiveness generated in an open tender environment that should produce lower than client estimate bids as opposed to a negotiated price contract. As well, sometimes in high risk projects, clients use of their own internal workforce can be the best option for assuming risks that may eventuate and hence be managed such that all risk flows back to the client. Conversely, it could be postulated that open tender contracts have the potential to generate excessive change order variations brought about by the initial competitive contract price environment. 4.7.8 Project delivery data and model coding Three project delivery types were initially developed in Table 4.5 as open tender, negotiated price – internal workforce and negotiated price – external workforce. The delivery codes are shown in Table 4.34.

Revised delivery description Delivery code Open tender Delivery code 1

Negotiated price – internal workforce Delivery code 2

Negotiated price – external workforce Delivery code 3

Table 4.34: Project delivery codes


137

For the purpose of analysis, 1 was inserted as a categorical variable in the master data file for delivery code 1 and a dummy value of' 0 for when that delivery code did not apply to a project. A similar treatment of 1s and 0s was applied for delivery code 2 and delivery code 3. A sample of this data coding for the project delivery codes is shown in Table 4.35.

Project number Delivery code 1 Delivery code 2 Delivery code 3 37/91A/20 1 0 0 160/203/3 1 0 0 76/153/3 1 0 0 45/655/18 1 0 0 5/10K/809 0 1 0 148/3042/2 1 0 0 140/U15/44 1 0 0 140/U16/819 1 0 0 37/90C/36 0 1 0 25/126/23 1 0 0 70/108/300 1 0 0 120/856/3 1 0 0 48/21A802 1 0 0 66/814/5 1 0 0 48/6404/3 1 0 0 34/1102/17 0 1 0

Table 4.35: Sample data coding for project delivery codes

4.7.9 Indexed highway project programmed cost continuous variable Generally there is a correlation between the cost of a project and the size of the project. In this research it was adopted that, if projects costs are indexed to a common year, then the project cost can be used as a surrogate for project size. The reason for including the indexed highway programmed cost in the proposed model was because it was thought that there was a strong relationship between the size of a project and the percentage a project might overrun. For highway projects, the greatest risk lays below ground level due to the relatively greater physical footprint of the project, and the larger the footprint, then the larger the risk cost should be. Williams (2003) reported results that suggested that large highway projects tended to have much higher relative cost increases than small projects. Hence it was postulated that larger projects overrun budget more so than smaller projects when indexed to a common year. The minimum project size was limited to A$1m. For the purpose of the research analysis, the programmed cost data in A$m from financial years 1995–96 through to 2001–02 were adjusted by way of a cost index (refer Table 4.9) up to 2002–03 out-turn year was the continuous variable used. A sample of this continuous variable data is shown in Table 4.36.


138

Project number Indexed program cost $m

37/91A/20 4.73 160/203/3 1.36 76/153/3 2.55 45/655/18 1.90 5/10K/809 1.30 148/3042/2 1.81 140/U15/44 2.30 140/U16/819 & 820 1.80 37/90C/36 1.77 25/126/23 1.28 70/108/300 1.30 120/856/3 3.81 48/21A802 1.13 66/814/5 2.06 48/6404/3 8.69 34/1102/17 2.00 92/140/11 1.70

Table 4.36: Sample of indexed programmed cost $m continuous variables

4.7.10 Project high level risks The reason for including client risks associated with highway project construction in the model was that there was very strong research and historical project data evidence that suggested strong linkage to between the realisation of unplanned or under-budgeted project risks and cost overrun of constructed projects. Smith (1995a) has confirmed that inappropriate risk management can also lead to higher costs for the client. As well, El-Choum (1994) identified project cost overrun parameters for public infrastructure projects. Risks associated with varying types of highway construction projects were considered to have possible effects on the differences observed in cost overrun. Possible correlations may have existed between the prevalence of certain risks with certain levels of cost overrun. It was postulated that particular groupings of historical risks which clients have been exposed to in highway project delivery bear a relationship to the size of project cost overruns.It was further postulated that risks that were ranked most important in terms of impact on project cost overrun by a team of experts would produce higher cost overruns in the model. 4.7.11 Project high level risk data and model coding Ten high level risk groupings were initially developed using the NGT process. A further analysis demonstrated the need to have extracted quantity increase from HL/1 into its own HL/Q. Table 4.37 shows the 11 groupings used in the model


139

High level risk description Code

Design and scope change HL/1 Insufficient investigations and latent conditions HL/2 Deficient documentation (specification and design) HL/3 Client project management costs HL/4 Services relocation HL/5 Constructability HL/6 Price escalation HL/7 Right of way costs HL/8 Contractor risks HL/9 Environment HL/10 Quantity HL/Q

Table 4.37: High level risk groupings codes

For the purpose of analysis, a 1 was inserted as a categorical variable in the master data file against projects which had a HL/1 associated with them and a dummy value of' 0 for when the high level risk – design and scope change did not apply to the projects. A similar treatment of 1s and 0s was applied for HL/2 through to HL/10 and HL/Q. A sample of this data coding for the project delivery codes is shown in Table 4.38.

Project number HL/1 HL/2 HL/3 HL/4 HL/5 HL/6 HL/7 HL/8 HL/9 HL/10 HL/Q

37/91A/20 1 0 0 0 1 0 0 0 0 0 0 160/203/3 1 0 0 0 0 0 0 0 0 0 0 76/153/3 1 0 0 0 0 0 0 0 0 0 0 45/655/18 1 0 0 0 0 0 0 0 0 0 0 5/10K/809 1 0 0 0 1 0 0 0 0 0 0 148/3042/2 1 0 0 0 0 0 0 0 0 0 0 140/U15/44 1 0 0 0 0 0 0 0 0 0 0 140/U16/819 0 0 0 0 0 0 0 0 0 1 0 37/90C/36 1 0 0 0 0 0 0 0 0 0 0 25/126/23 1 1 0 0 0 0 0 0 0 0 0 70/108/300 1 0 0 0 0 0 0 0 0 0 0 120/856/3 0 0 0 0 0 0 1 0 0 0 0 48/21A802 1 0 0 0 0 0 0 0 0 0 0 66/814/5 1 0 0 0 0 0 0 0 0 1 0 48/6404/3 1 0 0 0 0 0 0 0 0 0 0 34/1102/17 0 0 0 0 0 0 0 0 0 1 0 92/140/11 1 0 0 0 0 0 0 0 0 0 0

Table 4.38 Sample of high level risk grouping codes against projects

4.8 Regression modelling 4.8.1 Outlying data values As a preliminary step in the analysis process, the 231 project cases identified in Section 4.3 were analysed for random disturbance. For the purpose of specifically identifying any


140

project outliers, a linear regression analysis was carried out using the dependent variable as 'project cost overrun $m' and the predictor variable as 'indexed programmed cost $m'. A value of two standard deviations from the initial estimated line was set to measure the variability of the random disturbance. It was postulated that about 95% of observed points should have fallen within the adopted two standard errors, assuming normality. This analysis generated a table of project cases with residuals more than the adopted value. This was helpful in identifying potential outlying data values. The following Table 4.39 details the casewise diagnostics that were produced from the analysis of the two nominated variables.

Case number Std. residual Cost overrun $m Predicted value Residual

12 –8.703 17.1 34.911 –17.7705 57 –3.124 12.0 18.344 –6.3783

138 2.164 34.7 30.230 4.4197 168 3.643 28.5 21.111 7.4384 196 9.451 42.4 23.108 19.2991

Dependent variable: cost overrun $m Table 4.39: Case-wise diagnostics

As can be seen in Table 4.39, there are five cases identified with standardised residuals exceeding 2 in absolute value. An analysis of the data behind the case numbers identified in the diagnostics revealed the following projects in Table 4.40 as being potential data outliers

Case number

Year of project

Corresponding project number Project description

12 2000–2001 160/12A/8.561 * Pacific Highway (Pacific Motorway) Logan Motorway – Stapylton

57 1997–1998 160/12A/8.562 Pacific Motorway (Logan – Nerang)

138 2000–2001 160/12A/8.564 # Pacific Highway (Pacific Motorway) Oxenford – Gaven

168 1997–1998 160/12A/8.561 # Pacific Motorway (Logan – Nerang)

196 1999–2000 160/12A/8.564 * Pacific Motorway (Logan – Nerang) Oxenford – Gaven

Table 4.40: Project outlier details

The projects * and # were reported on in successive years. It was noted that all five cases above related to the Pacific Motorway project that had been constructed during the period 1997–2001 and all had been classified as R7 (Widen four lanes into six/eight lanes). Since the standardised residuals of these five cases exceeded 2 in absolute terms, then it was considered appropriate to exclude these five cases from any future model analysis. The sample size was subsequently reduced from 231 highway projects down to 226. A further analysis of the remaining project data for all R7 projects showed that only three projects remained in this category. It was then decided to remove those remaining three R7 projects. This step removed all project type 7 from all further model analysis and provided 223 projects for further analysis.


141

4.8.2 Multivariate regression analysis The statistical tool of multivariate regression was used to analyse the correlation between the following project variables that were simultaneously associated with the dependent variable of cost overrun percentage:

1. Highway geographic project type (urban/rural projects) 2. Highway project construction type (project types 1 to 12) 3. Highway project delivery type (delivery code 1, 2 or 3) 4. Indexed highway project programmed cost (indexed prog. cost $m) 5. Highway project high level risk grouping (HL/1 to 10 and HL/Q).

Due to wide spectrum of highway projects, delivery methods and client risks that caused cost overrun, many regression models were possible. According to 5 variable listings above, there were 2 geographic location types, 12 project types, 3 project delivery methods, and 11 high level risk groupings that were hypothesised as influencing the amount of project cost overrun for the projects analysed. The null hypothesis adopted for Pearson's correlation test was that there was no correlation between the size of cost overrun of projects (and their geographic location, construction type, delivery process, project size, and client project risks). Model testing was applied to confirm or otherwise that the given independent (prediction) variables in the model did not correlate against the dependent variable of project cost overrun with any significant level of accuracy. For the purpose of research, the statistical models were considered linear normal models (i.e. regression analysis with the appropriate f -tests and t-tests). For each test, the p-value was reported as a measure for rareness if identity of groups was assumed. A p-value less than 0.01 was considered highly significant and less than 0.05 significant. Whereas a larger p-value meant that the deviation could be due to chance. Three stages of statistical regression were computed using the method of least squares

• The following three methods were used to include and exclude independent variables in the analysis:


In stage one, the independent best correlated with the dependent was included in the equation. In the second stage, the remaining independent with the highest partial correlation with the dependent, controlling for the first independent, was entered. This process was repeated at each stage for previously-entered independents, until the addition of a remaining independent did not increase R2 by a significant amount or until all variables were entered. The next step was to inspect the residuals as surrogates for random disturbances and to make further judgements on the validity of the data and the linear regression model. The following sections detail the three selection modes applied to the project data for this purpose.


142

4.8.3 Forward selection mode for multivariate regression analysis Forward selection — this method started with a model that contained none of the explanatory variables. In the first step, the procedure considered variables one by one for inclusion and selected the variable that results in the largest increase in R2. In the second step, the procedure considered variables for inclusion in the model that only contained the variable selected in the first step. In each subsequent step, the variable with the largest increase in R2 was selected, until, according to an f -test, further additions were judged to not improve the model. The forward selection was next carried out on the data with each of the independent variables entered separately. Table 4.41 shows the independent variables entered and removed during the forward selection method. The dependent variable entered was % over cost.

Model Variables entered Variables removed Method

1 Indexed prog cost $m Nil Forward (Criterion: Probability-of-f-to-enter <= 0.050)

2 Project type 12 Nil Forward (Criterion: Probability-of-f-to-enter <= 0.050)

3 HL/1 Nil Forward (Criterion: Probability-of-f-to-enter <= 0.050)

Table 4.41: Variables entered/removed in forward selection mode

As the forward selection mode had been used, there were no variables removed. Only those variables which had a probability of f <= 0.050 were entered. Three models were produced in the forward selection analysis. The regression model summary for the forward selection mode is shown in Table 4.42. R represents the correlation between the linear regression prediction and the actual % over cost. The R values for models 1 and 2 were <0.2 and thus had negligible correlation. Model 3 had an R value of 0.245 which indicated a low correlation that was not very significant. The R2 values 0.019, 0.037 and 0.060 for models 1, 2 and 3 were statistically very poor. Column 4 shows the coefficient of multiple determination as adjusted R2 and was the adjustment applied to R2 to account for the number of variables in the model and the sample size used). The adjusted R2 for models 1, 2 and 3 were 0.015 (1.5%), 0.028 (2.8%) and 0.047 (4.7%) respectively. This meant that by moving to model three, there was an increase of only 3.2% in the total variation in the % over cost dependent variable.


143

Model R R2 Adjusted R2 Std. error of the estimate

1 0.139(a) 0.019 0.015 22.0986 2 0.192(b) 0.037 0.028 21.9501 3 0.245(c) 0.060 0.047 21.7318

Table 4.42: Forward selection mode summary using dependent variable of % over cost

The following predictor variables were used in the models: a. Model 1: Indexed prog. cost $m b. Model 2: Indexed prog. cost $m, Project type 12 c. Indexed prog cost $m, Project type 12, HL/1.

Unstandardised and standardised regression coefficients, as well as t and significance values for each of the three models were derived. These are shown in Table 4.43. The beta standardised coefficients for each variable, as shown in Column five of Table 4.43, gave the relative importance of each variable and measured the change in the dependent variable % over cost in units of its standard deviation. It showed that for Model 3, the variable 'Project Type 12' (beta = 0.158) had a marginally greater impact on the dependent variable % over cost than HL/1 (beta = 0.155).

Unstandardised coefficients

Standardised coefficients

Model B Std.

error Beta t Sig.

(Constant) 31.048 1.773 17.509 0.000 1 Indexed prog.

Cost $m -0.414 0.198 -0.139 -2.086 0.038

(Constant) 30.148 1.818 16.584 0.000 Indexed prog. Cost $m -0.403 0.197 -0.135 -2.043 0.042 2

Project type 12 11.083 5.541 0.132 2.000 0.047 (Constant) 26.105 2.499 10.448 0.000 Indexed prog. Cost $m -0.436 0.196 -0.146 -2.226 0.027

Project type 12 13.208 5.561 0.158 2.375 0.018 3

HL/1 6.989 2.996 0.155 2.333 0.021

Dependent variable: % over cost $m Table 4.43: Coefficients for forward selection mode

The variables excluded from the forward selection mode, along with beta, t and significance (f) values are shown in Table 4.44. The significance values of excluded variables that are shown in Column five of Table 4.44 were all >0.05. The observed slope of the model was given by the t value in Column four and was found to be significantly greater than 0. The results of the forward mode analysis indicated that the overall f results showed moderate evidence that the three models were statistically significant.


144

Model Excluded variable

Beta t Sig.

Rural projects -0.101(a) -1.449 0.149 Urban projects 0.101(a) 1.449 0.149 Project type 1 -0.041(a) -0.612 0.541 Project type 2 -0.073(a) -1.098 0.273 Project type 3 -0.096(a) -1.415 0.158 Project type 4 -0.008(a) -0.116 0.907 Project type 5 -0.038(a) -0.573 0.567 Project type 6 0.000(a) -0.006 0.995 Project type 8 0.028(a) 0.424 0.672 Project type 9 0.104(a) 1.560 0.120 Project type 10 0.006(a) 0.097 0.923 Project type 11 0.038(a) 0.575 0.566 Project type 12 0.132(a) 2.000 0.047 Delivery code 1 0.058(a) 0.828 0.408 Delivery code 2 0.024(a) 0.351 0.726 Delivery code 3 -0.106(a) -1.579 0.116 HL/1 0.129(a) 1.950 0.052 HL/2 -0.016(a) -0.235 0.814 HL/3 -0.051(a) -0.762 0.447 HL/4 -0.107(a) -1.615 0.108 HL/5 -0.006(a) -0.083 0.934 HL/6 0.009(a) 0.128 0.899 HL/7 0.053(a) 0.794 0.428 HL/8 -0.024(a) -0.354 0.723 HL/9 -0.055(a) -0.823 0.411 HL/10 -0.062(a) -0.934 0.351

1

HL/ Q -0.090(a) -1.358 0.176 Rural Projects -0.068(b) -0.939 0.349 Urban Projects 0.068(b) 0.939 0.349 Project type 1 -0.026(b) -0.390 0.697 Project type 2 -0.062(b) -0.926 0.355 Project type 3 -0.075(b) -1.104 0.271 Project type 4 0.002(b) 0.024 0.981 Project type 5 -0.034(b) -0.512 0.609 Project type 6 0.015(b) 0.215 0.830 Project type 8 0.034(b) 0.509 0.612 Project type 9 0.115(b) 1.740 0.083 Project type 10 0.021(b) 0.313 0.755 Project type 11 0.044(b) 0.661 0.509 Delivery code 1 0.073(b) 1.043 0.298 Delivery code 2 -0.004(b) -0.059 0.953 Delivery code 3 -0.091(b) -1.357 0.176 HL/1 0.155(b) 2.333 0.021 HL/2 -0.006(b) -0.095 0.924

2

HL/3 -0.062(b) -0.934 0.352


145

HL/4 -0.099(b) -1.501 0.135 HL/5 -0.003(b) -0.039 0.969 HL/6 -0.002(b) -0.028 0.978 HL/7 0.060(b) 0.899 0.370 HL/8 -0.039(b) -0.583 0.561 HL/9 -0.047(b) -0.701 0.484 HL/10 -0.065(b) -0.981 0.328

HL/ Q -0.117(b) -1.756 0.081 Rural Projects -0.065(c) -0.911 0.363 Urban Projects 0.065(c) 0.911 0.363 Project type 1 -0.023(c) -0.344 0.731 Project type 2 -0.066(c) -0.998 0.319 Project type 3 -0.073(c) -1.080 0.281 Project type 4 0.002(c) 0.034 0.973 Project type 5 -0.036(c) -0.548 0.584 Project type 6 0.022(c) 0.326 0.745 Project type 8 0.016(c) .247 0.805 Project type 9 0.113(c) 1.724 0.086 Project type 10 0.022(c) 0.332 0.740 Project type 11 0.048(c) 0.724 0.470 Delivery code 1 0.059(c) 0.846 0.398 Delivery code 2 0.011(c) 0.157 0.875 Delivery code 3 -0.090(c) -1.351 0.178 HL/2 0.005(c) 0.074 0.941 HL/3 -0.052(c) -0.790 0.430 HL/4 -0.086(c) -1.313 0.191 HL/5 0.003(c) 0.050 0.960 HL/6 0.031(c) 0.462 0.645 HL/7 0.084(c) 1.265 0.207 HL/8 -0.012(c) -0.183 0.855 HL/9 -0.023(c) -0.346 0.729 HL/10 -0.009(c) -0.127 0.899

3

HL/ Q -0.059(c) -0.788 0.432 Table 4.44: Excluded variables in forward selection mode

The following predictor variables were used in the derived models:

a. Model 1: Indexed prog. cost $m b. Model 2: Indexed prog. cost $m, Project type 12 c. Model 3: Indexed prog cost $m, Project type 12, HL/1.

4.8.4 Backwards selection mode for multivariate regression analysis Backward selection — This method started with a model containing all the variables and eliminated variables one by one. At each step the variable chosen for exclusion was that leading to the smallest decrease in R2. The procedure was repeated until, according to the f test, further exclusions did not represent a deterioration of the model. Only those variables which had a probability of f >= 0.100 were removed in the backwards selection process. A


146

total of 24 models was produced and produced R2 values ranging from 0.110 (11%) down to 0.073 (7.3%), which indicated very poor coefficient of variations for the models developed. The analysis output for the backwards selection mode was very large, and so it was not reproduced in this section. 4.8.5 Stepwise selection mode for multivariate regression analysis Stepwise selection — This third method involved the combination of the previous two approaches and started with no variables in the model. Variables were then added, as with the forward selection method. In addition, after each inclusion step, a backward elimination process was carried out to remove variables that were no longer judged to improve the model. Caution in this process was required as the stepwise procedure was claimed to be controversial because it included independent variables based on statistical criteria and not theoretical ones (Bryman and Cramer, 1999). f statistics with probabilities of 5% and 10% were employed for entry and removal criteria. The stepwise analysis produced three models. Table 4.45 shows the variables entered, with no variables being able to be removed under the above f criteria.

Model Variables entered

Variables removed Method

1 Indexed prog. cost $m Nil Stepwise (Criteria: Probability-of- f -to-enter <= .050,

Probability-of- f -to-remove >= .100).

2 Project type 12 Nil Stepwise (Criteria: Probability-of- f -to-enter <= .050, Probability-of- f -to-remove >= .100).

3 HL/1 Nil Stepwise (Criteria: Probability-of- f -to-enter <= .050, Probability-of- f -to-remove >= .100).

Table 4.45: Variables entered/removed in stepwise selection mode

4.9 Summary of variable selection modes for multivariate linear regression analysis

The R, R2, adjusted R2 and standard error of the estimate outputs from the forward, backwards and stepwise multivariate linear regression analysis were collated and are shown in Table 4.46 for the three selection modes

Method Model number R R2 Adjusted R2 Std. error of the estimate

Forward 1 0.139 0.019 0.015 22.0986 Forward 2 0.192 0.037 0.028 21.9501 Forward 3 0.245 0.060 0.047 21.7318 Backwards 1 0.333 0.111 -0.007 22.3395 Backwards 2 0.333 0.111 -0.002 22.2827 Backwards 3 0.333 0.111 0.003 22.2265


147

Backwards 4 0.333 0.111 0.008 22.1712 Backwards 5 0.333 0.111 0.013 22.1162 Backwards 6 0.333 0.111 0.018 22.0632 Backwards 7 0.333 0.111 0.023 22.0109 Backwards 8 0.332 0.110 0.027 21.9600 Backwards 9 0.332 0.110 0.032 21.9103 Backwards 10 0.331 0.109 0.035 21.8674 Backwards 11 0.329 0.108 0.039 21.8252 Backwards 12 0.327 0.107 0.042 21.7906 Backwards 13 0.325 0.106 0.045 21.7538 Backwards 14 0.323 0.105 0.049 21.7133 Backwards 15 0.321 0.103 0.052 21.6807 Backwards 16 0.317 0.100 0.054 21.6604 Backwards 17 0.314 0.099 0.056 21.6325 Backwards 18 0.311 0.096 0.058 21.6059 Backwards 19 0.307 0.094 0.060 21.5854 Backwards 20 0.300 0.090 0.061 21.5799 Backwards 21 0.293 0.086 0.060 21.5818 Backwards 22 0.284 0.081 0.059 21.5934 Backwards 23 0.270 0.073 0.056 21.6346 Stepwise 1 0.139 0.019 0.015 22.0986 Stepwise 2 0.192 0.037 0.028 21.9501 Stepwise 3 0.245 0.060 0.047 21.7318

Table 4.46 Summary of multivariate linear regression for three modes

The forward and stepwise analysis both produced three identical models, while the backwards mode produced 13 models. The R2 values ranged overall from 0.019 (1.9%) up to 0.111 (11%) and these values indicated a very poor coefficient of variation. As well, adjusted R2 values ranged overall from 0.002 to 0.061 which again indicated very weak models. The standard error of the regression estimate shown in Column 6. The values were consistently large and only showed a slight reduction in each of the models for the forward, backwards and stepwise modes. This fact, combined with the low estimates for R2 and adjusted R2, indicated that the models had not fitted the data very well. 4.10 Sensitivity testing of project data used in regression analyses Sensitivity testing was investigated for the stepwise regression models developed in order to ascertain whether additional outlier data still unduly influenced the models. The Project type 12 in model 2 showed prima facie, that these 17 miscellaneous projects were catch-alls for the undefined projects in the initial data categorisation. On review of the project data, the majority of the Project type 12 (Miscellaneous) was associated with the construction of sound attenuation structures to the boundary lines of urban highways or traffic infrastructure such as signalisation and signage. In particular, Project 140/U16/301 was categorised as Project type 12 (Miscellaneous roadworks/traffic furniture and devices)


148

project in the initial data compilation. A review of the project's specific data indicated that this project was described as a traffic management devices project on the Ipswich Highway (Cunningham Arterial). The open tender project included the installation of permanent separator concrete barriers, guardrails and fencing as well as temporary traffic control devices. The works were reported to have been carried out totally at night and under heavy traffic flows. The programmed cost in 2001 was $7.43m and the project experienced a cost overrun of 54.3%. Because of the unique nature of the project it was considered an outlier and removed from further regression analysis. In addition, project 160/12A/8.57-59 was categorised as a Type 12 (Miscellaneous roadworks/traffic furniture and devices) project in the initial data compilation. A review of the project's data indicated that this project was described as ancilliary works on the eight-lane Pacific Motorway (Pacific Highway). The open tender project involved the fabrication and installation of signage, noise amelioration, landscaping and roadworks. The programmed cost in 2001 was $28.2m and the project experienced a cost overrun of 27.7%. Because of the unique nature of the specific project and its location within the larger Pacific Motorway eight-lane project (R7 project type), the project was removed from further analysis. (All other R7 projects have already been removed from the regression analysis.) The data were re-analysed with the omission of the two outlier projects using stepwise mode and the analysis produced the following models.


Standardised coefficients Model Variable

B Std. error Beta t Sig.

(Constant) 27.257 1.325 20.572 0.000 1 Project type 9 10.143 4.924 0.138 2.060 0.041 (Constant) 28.952 1.563 18.522 0.000 Project type 9 10.031 4.891 0.136 2.051 0.041 2 Indexed prog. cost $m -0.340 0.169 -0.134 -2.009 0.046 (Constant) 26.123 2.096 12.463 0.000 Project type 9 9.769 4.859 0.133 2.011 0.046 Indexed prog. cost $m -0.366 0.169 -0.144 -2.170 0.031

3 HL/1 5.137 2.558 0.133 2.009 0.046

Dependent variable: % over cost $m

Table 4.47: Sensitivity testing of coefficients for secondary stepwise selection mode

As each of the revised models contained Project type 9, all these projects were investigated for data outliers. It was identified that Project 76/153/3 was an open tender at-grade intersection improvement project with an initial indexed programmed cost of $2.55m that was completed in 1999 and had a cost overrun of 110.9%. This case gave a predicted value of $40.09m and a residual of 70.1 which was outside two standard errors and it was considered appropriate to discard the project as an outlier. The data was re-analysed with the further omission of Project 76/153/3 using stepwise mode. The following single model shown in Table 4.48 was derived.


149


1 0.135 0.018 0.014 18.1997 Table 4.48: Sensitivity testing for stepwise selection mode summary

The following predictor variable was used in the model: Predictor: (Constant), Indexed prog. cost $m Dependent variable: % over cost Unstandardised and standardised regression coefficients, as well as t and significance values for the model are shown in Table 4.49. The beta standardised coefficient for the variable is shown in Column 5 of Table 4.49 and measured the change in the dependent variable % over cost in units of its standard deviation. It showed that for the model 1, the variable IndexedProgCost$m had a beta = -0.135.


Standardised coefficient Model

B Std. Error Beta t Sig.

(Constant) 29.249 1.472 19.870 0.0001 IndexedProgCost$m -0.328 0.164 -0.135 -2.007 0.046


Table 4.49: Coefficients for stepwise selection mode

The variables excluded from the stepwise selection mode on the revised data set, along with beta, t and significance (f) values are shown in Table 4.50. The significance values of excluded variables that are shown in Column five were all >0.05. The observed slope of the model was given by the t value in Column four and was found to be greater than 0. The results of this stepwise mode analysis indicated that the overall f results showed moderate evidence that the model was statistically significant.

Model Beta in t Sig.

Rural projects -0.046 -0.651 0.516 Urban projects 0.046 0.651 0.516 Project type 1 -0.021 -0.317 0.752 Project type 2 -0.063 -0.932 0.352 Project type 3 -0.067 -0.983 0.327 Project type 4 0.011 0.162 0.871 Project type 5 -0.039 -0.581 0.562 Project type 6 0.023 0.326 0.745 Project type 8 0.047 0.698 0.486 Project type 9 0.072 1.074 0.284 Project type 10 0.036 0.539 0.590 Project type 11 0.059 0.883 0.378 Project type 12 -0.027 -0.395 0.693 Delivery code 1 0.115 1.645 0.101

1

Delivery code 2 -0.046 -0.672 0.502


150

Delivery code 3 -0.094 -1.390 0.166HL/1 0.125 1.869 0.063HL/2 -0.001 -0.011 0.991HL/3 -0.044 -0.656 0.513HL/4 -0.114 -1.703 0.090HL/5 -0.025 -0.371 0.711HL/6 -0.098 -1.459 0.146HL/7 0.079 1.177 0.241HL/8 -0.011 -0.163 0.871HL/9 -0.051 -0.759 0.449HL/10 -0.037 -0.547 0.585

HL/Q -0.129 -1.941 0.054

Predictor in the Model: (Constant), Indexed prog. cost $m Dependent variable: % over cost $m

Table 4.50: Excluded variables in stepwise selection mode using revised data

The residual statistics for the stepwise model are detailed in Table 4.51 below and shows the standard deviation for the residuals at a relatively wide 18.1581 for the revised 220 projects.

Minimum Maximum Mean Std. deviation N Predicted value 13.561 28.921 27.617 2.4678 220 Residual -18.3199 85.6141 0.0000 18.1581 220 Std. predicted value -5.696 0.528 0.000 1.000 220 Std. residual -1.007 4.704 0.000 0.998 220

Dependent Variable: %Over Cost

Table 4.51: Residual statistics for stepwise selection mode for revised project data

The stepwise analysis for the revised project data produced the one model. f statistics with probability of 5% and 10% were employed for entry and removal criteria. Table 4.52 shows the variables entered, with no variables being removed under the above f criteria.

Model Variables entered

Variables removed Method

1 Indexed prog. cost $m Nil Stepwise (Criteria: Probability-of-f-to-enter <= .0050,

Probability-of-f-to-remove >= 0.100).

Dependent Variable: % Overcost Table 4.52: Variables entered/removed in stepwise selection mode

4.11 Testing the general assumptions of regression and residual analysis The preceding residual and significance results produced from the stepwise mode analysis on the revised 220 projects had indicated that the model developed using only IndexedProgCost$m as the predictor variable was statistically significant.


151

An inspection of the residuals as surrogates for random disturbances enabled further judgements on the validity of the data and the regression model. Graphical plots were used for testing the normality and homogeneity for model validation as they had the advantage over numerical methods of readily illustrating the relationship between models and data. 4.11.1 Normality of error testing A normality of error test on the regression model was carried out to test the robustness around the regression line. This was tested by plotting the normal probability of the sorted values of the residuals versus the associated theoretical values from the standard normal distribution. This was to check whether or not it was reasonable to assume that the random errors inherent in the process had been drawn from a normal distribution. The assumption adopted was that the variability of data around the regression line should have been constant for all values of X in the regression model. The normal probability plot was ascertained to see if the residuals were normally distributed by lying along a 45 degree upward sloping diagonal line. The null hypothesis was that the residuals were normal, and to the extent that the graph deviated moderately from the 45 degree pattern then the normality assumption needed to be questioned. Figure 4.2 shows the Normal probability plot of the revised project cases.

Figure 4.2: Normal probability plot of regression standardised residuals

The plot was inspected to see if the random errors were normally distributed with the plotted points lying close to a straight line. The distinct curvature deviations from the straight line indicated that the random errors were probably not normally distributed. As can be seen, the

1.00.80.60.40.20.0 Observed Cumulative Probability

1.0

0.8

0.6

0.4

0.2

0.0

Expe

cted

Cum

ulat

ive

Prob

abili

ty

Dependent Variable: % over cost


152

graph demonstrated poor conformance to a 45 degree diagonal line with some values plotting away from the straight line. The next section describes the test carried out for homegeneity of variance. 4.11.2 Standardised residuals testing The next graph produced was the scatterplot of the standardised residuals versus the estimated standardised (or fitted) values. This was to provide an insight into the assumption of equal variances, as well as the assumption that X and Y had a linear relationship in the regression equation. Figure 4.3 shows the scatterplot of the standardised residuals versus predicted values of the original 220 cases.

Figure 4.3: Scatterplot of the standardised residuals versus predicted values of 220 cases

The plot is decreasingly dispersed across left to right. This suggested that the variances were not constant along the regression line and indicated a violation of the homogeneity-of-variance assumption (homoskedasticity). This rendered the inference unreliable by not having a more randomly scattered plot in an even, horizontal band around the residual value of zero. One reason for the shape of the plot was that the project data used in the research was limited to the inclusion of projects => 10% cost overrun. As well, the lower project programmed cost was limited to => $1m. In further examining the plot, a number of points exceeded +2. Under the model assumption, 95% of the standardised residuals were expected to be within the range of -2 to +2. The bulking of the data to the right of the plot indicated that some form of data transformation may have been required so as to satisfy random disturbances. This is discussed in the following section.

1 0-1-2-3-4-5 -6 Regression standardised predicted value

5

4

3

2

1

0

-1

-2

Reg

ress

ion

stan

dard

ised

resi

dual

Dependent variable: % over cost


153

4.11.3 Data transformation investigation A histogram showing the frequency of the regression standardised residuals was next produced (Figure 4.4) to ascertain whether the shape was consistent with the assumption of normality.

Figure 4.4: Histogram of standardised residuals

The histogram showed a relatively heavy positive skew of the data. This indicated that data transformation was required to improve the model. Hutcheson and Sofroniou (1999) suggested taking the reciprocal of X where X is an observed value for heavy positive skewness. 4.11.4 Data transformation using reciprocal of X The indexed programmed cost transformed using the reciprocal of the observed values. Table 4.53 shows a sample from the 220 projects with transformed indexed programmed costs. Project number Indexed prog. cost $m Reciprocal indexed prog. cost $m % over cost

140/U88/3 47.79 0.02092 18.0 160/12A/8.566 46.02 0.02173 25.3

160/12B/5 42.55 0.02350 12.6 70/25A/13 40.42 0.02474 14.7

420 -2 Regression standardised residual

60

50

40

30

20

10

0

Freq

uenc

y

Mean =1.63E-16Std. Dev. =0.998

N =220

Dependent Variable: % over cost


154

150/10L/55 35.40 0.02825 32.5 70/25A/13 32.71 0.03057 25.7

160/12A/8.57-59 29.95 0.03339 27.7 160/12A/8.565 28.23 0.03543 23.9

Table 4.53: Sample of data using reciprocal transformation

4.11.5 Re-testing of general assumptions of residuals A re-inspection of the residuals as surrogates for random disturbances was carried out so as to make further judgements on the validity of the transformed data and an associated regression model. Graphical plots were again used for testing the normality and homogeneity for the revised model's validation. Figure 4.4 shows the revised Normal Probability Plot when using the transformed project data.

Figure 4.4: Normal probability plot with transformed variable

The scatter plot of the standardised residuals versus the estimated standardised transformed values is contained in Figure 4.5.

1.00.80.60.40.2 0.0 Observed cumulative probability

1.0

0.8

0.6

0.4

0.2

0.0

Expe

cted

cum

ulat

ive

prob

abili

ty

Normal P-P pot of regression standardised residual



155

Figure 4.5: Scatterplot of standardised residuals versus predicted of transformed data

It was observed that the plot was dispersed uniformly from left to right. By having a randomly scattered plot in an even, horizontal band around the residual value of zero, this rendered the inference of homogeneity reliable. There were still a number of data points which exceeded +2 for the standardised residuals. 4.12 Regression analysis using reciprocal indexed prog. cost $m The transformed data using reciprocal indexed prog. cost $m were re-analysed. The predictor variable used in the model was reciprocal of prog. cost $m and the dependent variable used was % over cost . The regression model summary is shown in Table 4.54. The R value for the model was <0.2 and thus indicated negligible correlation. The R2 value of 0.039 was statistically very poor. Column 4 shows the adjusted R2 at only 3.5%.


1 0.198 0.039 0.035 18.78958 Table 4.54: Model summary using reciprocal indexed prog. cost $m transformed data

Unstandardised and standardised regression coefficients, as well as t and significance values for the model are shown in Table 4.55. The beta standardised coefficient for the variable is shown in Column 5. This measured the change in the dependent variable % over cost in units of its standard deviation. It showed that the variable Reciprocal indexed prog. cost $m had a beta = 0.198 for the model.

2 10-1 -2 Regression standardised predicted value

5

4

3

2

1

0

-1

-2

Reg

ress

ion

stan

dard

ised

resi

dual



156

Unstandardised

coefficients Standardised coefficients T Sig.

Model Variable B Std. error Beta

1 (Constant) 21.751 2.467 8.816 0.000 Reciprocal of prog.cost$m 14.519 4.872 0.198 2.980 0.003


Table 4.55: Regression coefficients for transformed data model

The residual statistics for the model are detailed in Table 4.56 below and show the standard deviation for the residuals at a relatively wide 18.746 for the 220 projects analysed.

Minimum Maximum Mean Std. deviation N Predicted value 22.0551 36.2701 28.0604 3.78353 220Residual -22.11134 88.48981 0.00000 18.74663 220Std. predicted value -1.587 2.170 0.000 1.000 220Std. residual -1.177 4.710 0.000 0.998 220

Dependent Variable: %OverCost

Table 4.56: Residual statistics for transformed project data

4.13 Sensitivity testing using discrete rural and urban data sets Further univariate regression testing was carried out on separate rural and urban project data sets to ascertain if statistically stronger correlations could be developed by separating out the project data into two discrete datasets. There were 134 rural project cases and 86 urban project cases available for analysis and these agreed with the 220 cases previously derived after the identified outliers were removed. The predictor variable used in the analysis was reciprocal of prog. cost $m and the dependent variable used was % over cost . 4.13.1 Rural project data regression analysis The rural project data were analysed and the model summary was derived as shown in Table 4.57.


1 0.153 0.0299 0.016 18.0554 Table 4.57: Model summary using rural project data

The R value for the model was <0.2 and thus had negligible correlation. The R2 value of 0.016 was statistically very poor. Column four shows the adjusted R2 at only 1.6%.


157

Unstandardised and standardised regression coefficients, as well as t and significance values for the model are shown in Table 4.58. The beta standardised coefficient for the variable, as shown in Column five, measures the change in the dependent variable % over cost in units of its standard deviation. It showed that the variable 'Reciprocal indexed prog cost $m' had a beta = 0.153.


Standardised coefficient t Sig.

Model B Std. error Beta

1 (Constant) 22.158 3.427 6.467 0.000 Reciprocal of prog.cost$m 11.534 6.476 0.153 1.781 0.077


Table 4.58: Regression coefficients for rural data model

The residual statistics for the model are detailed in Table 4.59 below and show the standard deviation for the residuals at a relatively wide 17.9879 for the 134 projects analysed



Table 4.59: Residual statistics for rural project data

4.13.2 Testing of general assumptions of residuals for rural project data An inspection of the residuals as surrogates for random disturbances was carried out on the rural data so as to make further judgements on the validity of the transformed data and an associated regression model. Graphical plots were again used for testing the normality and homogeneity for the revised models validation as shown Figures 4.6 and 4.7.


158

Figure 4.6: Normal probability plot of rural project data

The scatterplot of the standardised residuals versus the estimated standardised transformed values is contained in Figure 4.7.

Figure 4.7: Scatterplot of standardised residuals versus predicted values for rural projects


1.0

0.8

0.6

0.4

0.2

0.0

Expe

cted

cum

ulat

ive

prob

abili

ty

Normal P-P plot of regression standardised residual


210-1 -2 Regression standardised predicted value

5

4

3

2

1

0

-1

-2

Reg

ress

ion

stan

dard

ised

resi

dual

Scatterplot



159

The plot was dispersed uniformly from left to right and rendered the inference of homogeneity reliable by having a randomly scattered plot in an even, horizontal band around the residual value of zero. A number of data points still exceeded +2 for the standardised residuals. 4.13.3 Urban project data regression analysis The 86 urban projects were analysed and the model summary was derived as shown in Table 4.60.


1 0.244 0.060 0.048 20.045 Table 4.60: Model summary using urban project data

The R value for the model was slightly >0.2 and thus had minimal correlation. The R2 value of 0.060 was statistically poor. Column four shows the adjusted R2 at only 1.6%. Unstandardised and standardised regression coefficients, as well as t and significance values for the model are shown in Table 4.61. The beta standardised coefficient for the variable is shown in Column five measured the change in the dependent variable % over cost in units of its standard deviation. It showed that the variable Reciprocal indexed prog. cost $m had a beta = 0.244.


Standardised coefficients Model

B Std. Error Beta t Sig.

(Constant) 21.920 3.617 6.059 0.0001 Reciprocal of prog.

cost $m 17.465 7.574 0.244 2.306 0.024


Table 4.61: Regression coefficients for urban data model

The residual statistics for the model are detailed in Table 4.62 below and show the standard deviation for the residuals at a relatively wide 19.923 for the 86 projects analysed



Table 4.62: Residual statistics for urban project data


160

4.13.4 Testing of general assumptions of residuals for urban project data An inspection for random disturbances of the residuals as surrogates permitted further judgement on the validity of the transformed data and an associated regression model. Graphical plots were again used for testing the normality and homogeneity for the revised model's validation. Figure 4.8 shows the revised Normal probability plot when using the urban project data.

Figure 4.8: Normal probability plot of urban project data

The scatterplot of the standardised residuals and the estimated standardised transformed values is contained in Figure 4.9.


1.0

0.8

0.6

0.4

0.2

0.0

Expe

cted

cum

ulat

ive

prob

abili

ty

Normal P-P plot of regression standardised residual



161

Figure 4.9: Scatterplot of standardised residuals versus predicted values of urban projects

Again it was observed that the predicted value plot was dispersed relatively uniformly from left to right and rendered the inference of homogeneity reliable by having a randomly scattered plot in an even, horizontal band around the residual value of zero but with a slight overflow of data points towards +2. A number of data points still exceeded +2 for the standardised residuals. 4.14 Sensitivity testing using open tender and negotiated price data sets Further univariate regression testing on separate open tender and negotiated price project data sets was then carried out to ascertain if a statistically stronger correlation would be developed within the separate project clusters. There were 131 open tender project cases and 89 negotiated price projects analysed. All relevant predictor variables were used with the dependent variable % over cost in the stepwise multivariate regression anlaysis on both individual data sets. The predictor variable used was reciprocal of prog. cost $m with the dependent variable again being % over cost. Both data set analyses were unremarkable in their statistical significance and again showed no increased correlation properties over the full data set of 220 projects.

2 10-1 Regression standardised predicted value

5.0

2.5

0.0

Reg

ress

ion

stan

dard

ised

resi

dual

Scatterplot



162

4.15 Regression analysis model summary This section has sought to demonstrate correlations between highway construction projects, their delivery methods and cost overrun by using multivariate regression analysis to develop robust models. The primary concern in developing such a model has been that of validation. This was an important step because of the highly variable nature of the project data used. The presence of the many considered variables here may create results contrary to what has been posed and what would normally have been expected. It was anticipated that the results of the research would contribute strongly in providing rational correlations between highway projects and their cost overrun. The validation process was an important step to see if any developed model best fitted the available data. All models were tested statistically to determine whether they confirmed a correlation between real project attributes and real project cost overruns. However, it was found that the regression models so developed perform poorly in representing correlations within the current project data. 4.15.1 Correlated model The regression analysis did demonstrate a weak correlation between the size of highway projects, as measured in indexed programmed cost and the size of cost overruns. The correlation evolved after data transformation was carried out to improve the model. For the regression formulae y = bx + c, the correlated model so devised had the following form, as extracted from Table 4.55.

% project cost overrun = 14.519 x reciprocal of indexed programmed cost $m.+ 21.751 In effect, this model has shown that as the size of a project increases in budgeted programmed cost, then the percentage cost overrun reduces in value. It further suggests that to counter the potential cost overrun, the over-and-above additional contingency percent that should be factored into all types of highway projects by the client to compensate for undefined (or under-defined) project risks can be varied depending on the project size. Such indicative over-and-above contingency percentages that have been derived from the model are shown in Table 4.63.

Project programmed cost Over-and-above percentage$1m 36.3% $5m 24.7%

$10m 23.2% $15m 22.7% $25m 22.3% $50m 22.0%

Table 4.63: Indicative over-and-above contingency percentages for project size


163

4.15.2 Summary of regression analysis

The statistical tool of multivariate regression was used to analyse the correlation between the following project variables that were simultaneously associated with the dependent variable of cost overrun percentage:

• Highway geographic project type (urban/rural projects) • Highway project construction type (project types 1 to 12) • Highway project delivery type (delivery code 1, 2 or 3) • Indexed highway project programmed cost (indexed prog. cost $m) • Highway project high level risk grouping (HL/1 to HL/10 and HL/Q).

A wide spectrum of highway projects, delivery methods and client risks were analysed using three separate modes of variable selection in the regression analysis. There were 2 geographic location types, 12 project types, 3 project delivery methods, and 11 high level risk groupings that were hypothesised to have a correlation to the amount project overrun in cost for the various projects analysed. The null hypothesis adopted for Pearson's correlation tests was that there was no correlation between the size of cost overrun of projects (and their geographic location, construction type, delivery process, project size, and client project risks. Model testing was applied to confirm or otherwise that the given independent (prediction) variables in the model did not correlate against the dependent variable of project cost overrun to any significant level of accuracy. Three modes of statistical regression were computed using:


All models were tested statistically and practically to determine whether they are efficient in predicting real project cost overrun results and based on the adopted statistical checks, all the regression models perform poorly in representing the project data. 4.16 Chapter summary The objectives of the research were to:

1. identify the project risk factors that lead to cost changes in highway construction projects

2. rank the different factors according to their incidence in historical project data 3. identify particular highway construction project types and groupings of cost overrun

factors 4. conduct an analysis of project data using statistical theories and concepts to identify

correlations between particular highway construction project types and project cost overrun.

It was hypothesised that there existed a defined relationship between the measure of the percentage of cost overrun in highway construction projects and:

• construction risks • types of highway construction projects • project delivery methods • project location


164

• project size. The correlation relationship of the multiple project variables was troublesome because the sought-after knowledge about cost overrun in highway projects was complex and had many different combinations of factors that had to be examined. The statistical modelling and analysis of the research data has found no strong correlations between project types, work types and project risk factors in producing cost overrun in highway construction projects. There was, however a weak model that showed that as the size of a project increased in budgeted programmed cost, then the percentage cost overrun reduced in value. Chapter 5 follows and discusses the research findings in fulfillment of the research objectives and documents its contribution to original research. It also suggests areas of further research.

CHAPTER 5 Discussion, recommendations and conclusions

165

CHAPTER 5

Discussion, recommendations and conclusions

This chapter discusses the research findings in fulfillment of the research objectives and documents its contribution to original research. It also suggests areas of further research in highway project management. 5.1 Discussion The overall objective of this research has been to identify the factors that influence significant project cost overruns for the client and to provide an analytical model that correlates project attributes to the level of their cost overruns and client project risks relating to decision-to-build budgets. The research methodology has been structured to seek out answers to the following research questions:


2. How does the amount of highway cost overrun in such highway projects correlate

with their project types, size, delivery processes and client project risks when historical project data are analysed?

In order to provide adequate answers to these questions, the following research stages were adopted:

1. the establishment of a data source for highway construction projects 2. the determination of project work types and cost overrun factors from historic case

study data 3. the utilisation of principal component analysis and factor rotation on cost overrun

factors in order to consolidate data 4. the use of the nominal group technique with highway construction experts to elicit

groupings of cost overrun factors and highway project types 5. the use of statistical analysis using multivariate linear regression analysis to

investigate correlations between cost overrun risk factors and project attributes by using historic project data.

The research investigations have revealed the following findings. 5.2 Highway project data The data has been collected from 231 highway projects published in the RIP documents of the Queensland Department of Main Roads over the financial years from 1995–96 to 2002–03. In the highway data analysed, there were 140 projects categorised as open tender contracts and this constituted 60.6% of the total projects analysed and 91 (39.4%) were sole invitee negotiated price contracts. The client-allocated budgets to deliver the projects included the following costs:


166


In order to carry out a valid analysis on the project data over the research period it was necessary to bring all relevant project cost data to a common out-turn financial year of 2002–03. The Road Input Cost Index (RICI) was adopted and applied to the research cost data. Table 5.1 details the RICI indices for the analysis period. The % adjustment in highway input costs has increased from 1.3% in 1995–96 up to 3.4% in 2001–02. The average increase over the seven-year analysis period was 2.4% per year and the index data shows a 16.3% increase in highway costs over the period.

Year Adjustment for 2002–03 out-turn price

Inter-year price adjustment

1995–96 16.3% 1.3% 1996–97 15.0% 1.8% 1997–98 13.2% 2.3% 1998–99 10.9% 2.3% 1999–00 8.6% 2.6% 2000–01 6.2% 2.8% 2001–02 3.4% 3.4% 2002–03 – Mean = 2.4%

Table 5.1: Project price adjustment percentages derived from RICI

Wilmot and Cheng (2003) carried out research to predict the cost escalation for future highway construction projects in the US. They used the Bureau of Economic Analysis of the Department of Commerce for predictions of future construction worker wages. The predicted worker wages were transformed into an index of construction labor cost with a value of 100 in 1987. Data on the cost of highway construction equipment and material for the period 1984–2015 were purchased from the commercial supplier of industrial data, Data Resources Incorporated and were transformed to an index with a value of 100 in 1987. Their model predicted that future overall highway construction costs would double in the period 1998–2015, representing a growth rate of approximately 4.2% per year, considerably higher than the current rate of general inflation of 2.5% in the US. 5.3 Cost overrun project risk factors The research identified 37 initial risk factors from the analysis of 231 highway projects which had reported significant cost overrun (i.e. in excess of 10% of the projects' initial cost). These were collated and Table 5.2 shows the top 12 factors in order of their magnitude of occurrence.


167

Cost overrun variable Occurrence % occurrence

1 Design/project scope change 95 31% 2 Contract tender price higher than original estimate 35 11% 3 Design scope change – drainage 33 10% 4 Quantity increased measure 31 10% 5 Design scope change – pavement materials/depth 23 7% 6 Latent condition – remove and replace

unsuitable material 21 7%

7 Design scope change – environmental issues 19 6% 8 Constructability – under traffic 17 5% 9 Services relocation costs 12 4% 10 Material cost increase – pavement materials 11 3% 11 Resumption/accommodation works 10 3% 12 Constructability difficulty costs 10 3% Table 5.2: Top twelve highway risk factors causing cost overrun

Detailed descriptions of significant cost overrun variables have been provided in Appendix E. 5.4 Factor analysis Factor analysis was initially adopted in the research methodology to statistically isolate the large number of project risks that had relationships to cost overrun in the highway project. The factor analysis reduced the number of independent cost overrun factor variables to 13. Guidelines had been reported for the minimum sample size and ratio limits for the number of variables for factor analysis and suggested a minimum sample size of 100 to 200 observations (Guadagnoli and Velicer, 1988) and the research data sample size of 231 satisfied this. Principal component analysis required a large sample size as it is based on the correlation of the variables involved. Sample testing found that the data sample used in the research was much less than that recommended by Kaiser (1974) and Hutcheson and Sofroniou (1999). As well, the Bartlett Test was less than recommended, even though Bartlett’s sphericity test was particularly relevant to small samples of data (<100) and not so relevant to larger samples as in the research case. The number of component variables reproduced in the analysis was determined by the number of principal components with eigenvalues of 1.00 or greater. Factor variation was relatively even across all components and this indicated low success in reducing the dimensionality of the original set of cost overrun variables using factor analysis. In order to facilitate a better interpretation of factors, the factor analysis required the rotation of axes that did not affect the goodness-of-fit of the factor solution. The varimax method of rotation, as developed by Kaiser, was adopted and provided the best parsimonious analytical solution. Computationally, the varimax rotation minimised the number of variables that had high loadings on the factors and caused the factor loadings of each variable to be marginally more clearly differentiated. A review of the 13 groupings led to the conclusion that no common factor components demonstrated consistency in purpose from the principal component analysis. There was no


168

evidence of strong correlations within any of the 13 groupings, even after rotation. A weak scree plot also indicated the derived model of 13 component factors was not a reliable representation of the total factors for use in the model analysis. 5.5 Expert elicitation findings Expert elicitation used in the research to develop a reduced combination of cost overrun risk factors.The expert elicitation used civil engineering highway experts. Nominal group technique (NGT) was used as it offered more structure than focus groups, but still took advantage of the synergy created by a group. NGT provided the most appropriate method for eliciting expert opinion as it demanded less valuable time from the industry experts and still provide the appropriate focus and outcome. NGT produced rank-ordered, weighted, semi-qualitative data on client risks and project types associated with significant cost overrun. The advantages of the NGT process were numerous. NGT provided a constructive, problem- solving approach that permitted equal participation by all the group members, and avoided the disproportionate influence by vocal individuals who are often present in group processes. It also reduced the pressure for individual members to conform to group opinion. In the feedback obtained during the process, participants appreciated the opportunity to give meaningful input. The NGT group composed of two generalists, five specialists, an analyst who took on the role of moderator and a researcher. This membership is supported by Clemen and Winkler (1985) who have suggested that five specialists are usually sufficient to cover most of the expertise and breadth of opinion but with an additional two or three generalists and two or three analysts for specific elicitation tasks. The spread of project-related experience of the members as shown in Table 5.3 demonstrated the good balance of project management expertise that existed across the group.

Experience area Minimum years Maximum years Mean years

Estimating 5 14 10.7 Design 1 17 8.5 Construction 2 15 8.5 Management 3 15 12.2

Table 5.3: Experience profile of expert group membership

One problem encountered during the elicitation process was the poor definition of some cost overrun factors provided to the group and the lack of specific project histories. To overcome this issue, the definitions of the factors causing the cost overrun were redefined as necessary. 5.6 High level project risks The research developed 10 importance ranked high level risk groupings (and their associated factors) that are present in the delivery of highway projects in Queensland. The research has associated these ten with significant project cost overruns. Highway project clients would benefit from these findings and thereby benfitting from achieving enhanced estimating accuracy in future highway projects.


169

Further research has also identified the project phases in which the risks can highly impact project cost. A Likert scale of L for low phase impact, M for a medium phase impact and H for a high phase impact has been used to identify the most susceptible project development areas. The final mapping is shown in Table 5.4. This supports the previous findings in Table 5.2 that the major propensity for significant cost overrun is predominantly located in the design phase of project development.

Risk group Cost overrun factors

Con

cept

Prel

imin

ary

Des

ign

Con

stru

ctio

n

Design/project scope change M H H L Design scope change resulting from drainage, environmental issues, pavement materials/depth

L M H M

Design scope change as a result of carrying out a safety audit on project L L H M

Quantity increase L L H M

Design and scope change (change in project definition)

Specification change L L M H Design preload requirement L M H L Design change to subgrade L L H M

Insufficient investigations and latent conditions

Latent condition – requiring design change – rock encountered – additional stabilising – removal/replacement of unsuitable material

L M H M

Deficient documentation Design scope change - design error L L H M Project acceleration requirement L L M H Government initiative – employment continuity developer contribution, local government/rail contribution,

L M H L

Material/process quality L L H H Contract failure – new contract establishment L L M H Project administration cost L L M H

Client project management costs

Tender price higher than client estimate L L H M

Services relocation Services relocation costs L M H

M

Constructability difficulty costs L M H H Constructability Constructability– under traffic L L M H

Material cost increase – asphalt, bitumen price, earthworks, pavement materials L L H H Price escalation

Principal supplied components/materials Contract tender price increase due to inflation L M H M

Right-of-way costs Resumption accommodation works L M H M Remote location costs L M H M Contractor risks

Wet weather effects/rework L L H M Environment Cultural heritage issues L M H M


170

L = Low impact on potential cost overrun M = Medium impact on potential cost overrun

H = High impact on potential cost overrun

Table 5.4: Ranked principal highway cost overrun risk groups

Further research findings have been developed in Table 5.5 to show risk sources capable of causing potential project cost overrun. Highway clients need to take these into account when they are developing new projects in order to reduce the possibility of their projects significantly exceeding budget. Rank Risk group Cost overrun factors Description of risk impact

1

Design and scope change in project definition

Design/project scope change resulting from drainage, environmental issues, pavement materials/depth Safety audit on project Quantity increase Specification change

Incomplete or missing design elements Potential increased cost, schedule delay and poor technical performance

2

Insufficient investigations and latent conditions

Design preload requirement Design change to subgrade Latent condition – requiring design change – rock encountered – additional stabilising – removal and replacement of unsuitable material

Undefined underground or hidden site conditions that can cause cost and schedule growth

3 Deficient documentation

Design scope change from design error

Incomplete or missing design elements that take into account construction aspects Uncertain design experience level Poor team cohesiveness or composition

4 Client project management

Project acceleration Government initiative – employment continuity Developer, local government, rail contributions Material/process quality Contract failure – new contract establishment Project administration Tender price higher than client estimate

Potential for poor quality and technical non-performance Uncertain design experience level Poor team cohesiveness or composition Estimates uncertain as they can be based on past projected costs

5 Services relocation

Relocation of utility services in road reserve

Approvals and environmental concerns for alignments can cause time delays and/or cost escalation

6 Constructability Constructability – Level of difficulty increases the potential


171

difficulty under traffic

for cost and schedule growth Potential for accidents and consequences of injuries resulting in costs

7 Price escalation

Material cost increase – asphalt, bitumen, earthworks, pavement materials Principal supplied components/materials Contract tender price increase due to inflation

Potential for material an labour price increases

8 Right-of-way Resumption accommodation works

Regulatory settlement approvals can cause project time delays or cost escalation

9 Contractor risks

Remote location costs Wet weather effects/rework

Labour strength and productivity uncertainties Potential for poor quality and technical non-performance Non-performance from vendors, sub-contractors or suppliers can cause impacts to cost and schedule Delay causing cost impact and technical non-performance from unseasonal weather patterns

10 Environment Cultural heritage issues

Regulatory approvals and mitigation processes of environmental concerns may cause time delays and/or cost escalation can cause project time delays or cost escalation

Table 5.5: Client risk sources during project

Twelve highway project types that collectively described all of the 231 highway projects in the data have been developed using the NGT process as shown in Table 5.6. Clients need to consider the standardized project types shown in order to be in a position to effectively analyse highway project data across organizations. The non-adoption of a standardized group of highway project types will only further perpetuate inefficiency in analyzing historical highway project data.

Project type Reference

Highway R1 Construct to sealed standard (2 lanes) R2 Widen shoulders and sealing R3 Rehabilitate/Strengthen R4 Realignment of two lanes R5 Realignment of four lanes R6 Duplicate two into four lanes R7 Widen four into six/eight lanes R8 Asphalt resurfacing R9 Construct intersection/interchange

Bridge B1 Bridges and approaches B2 Widen bridges


172

Miscellaneous M1 Miscellaneous roadworks/traffic furniture and devices

Table 5.6: Highway project construction types

The research also found it desirable to differentiate between certain project types where complexity or location need to be identified at a more specific level. The research found that the following project aspects needed to be considered in interrogating future details were readily available:

• rehabilitation projects include pavement widening or not • a realignment project incorporated some of the existing road and was built under

traffic — this type of project is referred to as being constructed at a brownfield site • the project was on a completely new alignment away from the existing highway that

it had replaced, thus having none or only a small exposure to highway traffic — this type of project is referred to as being constructed at a greenfield site

• an interchange/intersection or bridge and approaches were located in an urban or rural environment.

The determination of the highway project types aligned well with the literature research finding of Persad et al. (1995) who reported on a highway project classification developed by the Texas Department of Transport (TDoT) in 1986. It reported 14 types of highway projects as detailed in Table 5.7.

Source: Persad et al.(1995)

Table 5.7: 1986 Texas Department of Transport highway project classifications

5.7 Model development using multivariate regression analysis Multivariate regression has been the most common method of modelling construction costs in the past (Koppula, 1981; Blair et al., 1993; Elthag and Boussebaine, 1998; Williams, 2003) and it has been effectively used in this research to manage the multiple project variables and relationships between projects, project risks and project cost overrun.

halla



173

The dependent variable adopted in the model was the continuous variable percentage (%) cost overrun and has been defined as the difference between the client’s actual project cost and their programmed cost, expressed as a percentage of the programmed cost. The research has analysed the correlation between the following project variables:

1. Highway geographic project type (urban/rural projects) 2. Highway project construction type (project types 1 to 12) 3. Highway project delivery type (delivery code 1, 2 or 3) 4. Client project high level risk grouping (HL/1 to 10 and HL/Q) 5. Indexed highway project programmed cost (Indexed prog. cost $m).

Many regression models were possible due to the wide spectrum of highway projects, delivery methods and client project risks that were associated with cost overrun. Two geographic location types, 12 project types, three project delivery methods and 10 high level client risks were hypothesised as influencing the amount of cost overrun in highway project. The null hypothesis adopted was that there was no correlation between the size of cost overrun of location of projects, construction type, delivery process, project size or client risk factors. Three stages of statistical regression were computed using the method of least squares. For the purpose of research, the statistical models were considered linear normal models (i.e. regression analysis with the appropriate f -tests and t-tests). For each test, the p-value was reported as a measure for rareness if identity of groups was assumed. The forward selection, backward selection and stepwise selection regression methods were used in the analyses. The stepwise method delivered the most appropriate model after excluding outlier data and data transposition. Correlation analysis was undertaken to identify project variables that correlated with project cost overrun. The technique examined the performance of various models and the relationships between variables. Pearson's correlation coefficient (R) was used to examine the relationship between the data and for developing the rank order of regression models in terms of goodness of fit. The research used the coeffiecient of multiple determination – R2 and adjusted R2 statistics as they allowed direct comparison of the best model identified. A number of important analysis findings were revealed from the regression analyses as follows:

1. The research found no strong correlation between the geographic location of the highway projects and project cost overrun. This finding did not support Drew and Skitmore (1992) who identified that the density of population and the extent of geographic area were important factors for competitive tenders in building projects. The difference may have been because the research focused on highways, whereas Drew and Skitmore focused on building projects. As well, highway projects researched were all located in the one area, Queensland and not a broader geographic survey as was the case for Drew and Skitmore (1992).

2. The research found no strong correlation between highway project types and project

cost overrun. However, the research found that projects involved in constructing six or eight lanes from four lanes demonstrated excessive cost overruns when analysed for discordance. These were subsequently excluded as outliers so no data for six/eight


174

lane projects was incorporated in the final analysis. On a broader transportation research base of analysing rail, fixed-line and road projects, Flyvbjerg, et al (2002) found that the type of transportation project had a statistical effect on a project’s cost overrun. The difference in the research finding can be attributed to the much broader range of project types, other than just highway projects. As well, the Flyvbjerg research covered a number of differing countries.

3. The research found no strong correlation between the type of project delivery method

of highway projects and cost overrun. Projects that were delivered by open contract, as apposed to negotiated price, were no less susceptible to significant cost overrun.

4. It was postulated in the analyses that strong correlations existed between the groups of

client project risks and degree of cost overrun. The research found that no correlation between the client project risks and project cost overrun existed.

The research found correlation between indexed highway programmed cost and cost overrun after data translation. For the regression formulae y = bx + c, the correlated model had the following form:

% project cost overrun = 14.519 x reciprocal of indexed programmed cost $m.+ 21.751 This model indicates that, as the size of a project increases in budgeted programmed cost, so then does the size of the percentage cost overrun reduce. In contrast to this finding, research carried out in 1992 by the Transportation Research Board evaluated construction cost overruns on 468 transport projects completed for the Washington State Department of Transportation. Results of their analysis indicated that cost overruns, expressed as a percentage of the original contract amount, increased with the size of the project. This research finding has also been inconsistent with the research of Williams (2003) who analysed transportation projects in both the UK and the US. He found a (log.) linear relationship between contract size and cost overrun. The reasons for the inconsistency appeared to be the fact that Williams analysed only contract elements of projects and also the size of projects. Williams (2003) analysed projects which included contract values below the equivalent of A$1m in predominately highway rehabilitation projects. As well, he initially included dredging contracts in his data analysis in order to demonstrate linkages across project types in the US. In contrast, only highway construction and rehabilitation projects in Queensland that were >A$1m formed the basis for the data analysis. Both sets of projects were based on the design-bid-build delivery model. Presumably the differing broad findings are due to the unique geography of highway construction projects in Queensland, as opposed to that of the US. The research indicated that the percentage of project cost overrun is linked to the economy of scale of projects, such that smaller dollar projects can attract larger percentages of cost overruns, and larger dollar projects have the potential for smaller percentages of cost overruns. Smaller projects have a tendency towards a higher percentage increase in cost. The actual dollar magnitude of the cost overrun for the large project may still be greater, even though its


175

percentage change is less. For example, a 5% cost increase on a $1 million dollar project is $50, 000, whereas a 1% cost increase on a $100 million dollar project is $1,000,000. The research also lends itself to the assertion that additional contingency percentages can be derived from the model that can be applied to projects sizes when decisions have to be made on the size of individual projects. These proportional contingency percentages to projects' decision-to-build budgets of particular sizes are shown in Table 5.8.

Project programmed cost Over-and-above % $1m 36.3% $5m 24.7%

$10m 23.2% $15m 22.7% $25m 22.3% $50m 22.0%

Table 5.8: Over-and-above contingency percentages for varying project sizes

Statistical models assume error-free measurement, at least of the independent (predictor) variables and close attention was paid to the effects of noisy data (Helberg, 1996). Sensitivity testing was carried out on the research data in order to determine whether additional outlier data unduly influenced the models. Discordance tests were used to ascertain whether data observations were unreasonably extreme (Barnett and Lewis, 1984). The incidence of data outliers constituted less than 3% of the initial research data and this was considered only mildly contaminated with outliers. Rocke and Woodruff (1996) have pointed out that the detection of outliers can be more problematic if inconsistent data is found to be more than 25%. A check on the regression model was carried out in order to ascertain if a wider range of data outliers existed in the regression analysis. The approach undertook sensitivity testing on the data by dividing it into discrete data types. Regression re-testing was carried out on four separate data sets:

• rural projects • urban projects • open tender projects • negotiated price projects.

There was no discernable improvement in the correlation model produced from any of the four discrete datasets. 5.8 Implications for highway cost management 'body of knowledge' The presented research has used a sound technique of undertaking post-mortems of historic projects as a risk analysis technique in identifying significant cost overruns. Specifically this research has:

• combined risk assessment and expert elicitation techniques in investigating project management estimating and cost control issues when estimating highway projects

• provided a quantitative and qualitative project assessment that will help highway project decision makers define unforeseeable disturbances more reliably ahead of


176

time, so that corrective measures can be better taken into account in project design and estimating

• provided an in-depth post mortem analysis of client risks that have led to significant project cost overrun and thus has strengthened the understanding of why highway projects can overrun substantially above project decision-to-build budgets.

• identified client risk factors that are aimed at simply providing a better understanding of the risk parameters and management contingency requirements in project, thus validating better risk contingency weightings in budget estimates

• provided a standardised set of highway project types against which future highway project data can be collated and analysed

• provided importance ranked groupings of client project risks and descriptions of potential risk sources which clients need to take into account when developing new project budgets

• established NGT as a suitable method for risk elicitation and used it in the research project in a way that required minimal impact on the time of construction experts, but at the same time providing an acceptable outcome to a difficult elicitation requirement

• accounted for uncertainty when developing client project cost estimates by proposing a range of over and above % contingency to account for economy of scale in highway projects for varying sizes of highway projects.

The construction industry is unique in that it rarely builds something the same way, at the same place, or with the same set of people. Because construction professionals tend to describe risk in linguistic terms based on their experience and judgement, the research has aimed at providing the client with the background knowledge for representing project budget cost estimates in an ideal situation where all epistemic (lack of knowledge) uncertainty has been identified. As well, the construction industry is in need of project management tools that are straightforward and take advantage of the industry’s tendency to use engineering judgement to solve problems. The use of a sound elicitation risk assessment technique, such as in this research, provides a structured format to harness the experience and judgement of experts in assessing the financial impact of risks on projects in an efficient and time conscious manner. 5.9 Implications for cost estimating practices Most highway organisations have a relatively fixed annual budget for programmed construction and require that fairly accurate cost estimates before allocating individual project budgets. One could speculate that large cost under-estimation would be less likely in such situations. The research has detected no real trend in improvement in cost estimating over the seven years covered by the highway projects analysed. However, in the historic data analysis period adopted for this research, there was evidence that a good proportion of highway projects achieved their budget at project completion. This suggests that the project estimators associated with those projects have done a good job in terms of estimate accuracy. In this research, the project factors that have been analysed have not proven strong correlation to project overrun and so it can be implied that highway project estimators are, on the whole, making substantial allowances to cover project parameters that cause budget overrun.


177

While the research has been unable to supply any single and definitive answer to the predominant reasons for project cost overruns, the persistent underestimation of costs and the over-optimistic assumptions about performance of some project budget estimates raises questions about why client highway organisations have not been able to adjust their expectations over the years. Several possible remedial actions can be derived from the research. Firstly, the literature research has shown that contingency percentages of around 10% are commonly allocated to project estimates at the decision-to-build time to allow for unforeseen and unquantified client risks. The client organisation is reported to include a 10% management contingence to its decision-to-build budget estimates in the research period to take into account any underestimated client risks. The research now indicates that a higher order percentage in management contingency may be required for all highway projects (excluding six and eight-lane projects). This higher order contingent percentage is required to minimise the protential for projects to significantly exceed the client budgeted cost because of undefined, or under-defined, client risks. Therefore, an estimating procedure which includes revised levels of management reserve contingencies, ranging from 35% for projects up to $3m, down to 25% for larger projects, should be considered. This revised contingency would reduce the potential for projects to be reported as having incurred significant budget over-cost during delivery. Secondly, client estimating procedures for highway projects should take into account the potential cost impacts from the risk factors identified in this research. Failure to systematically evaluate the project risk potential of all of the risk factors could severely impact on the favourable outcomes of projects and on a client's overall highway construction program. Thirdly, the research found that design and scope changes are the highest contributing risk factor to project cost overrun in highway projects analysed. This finding is supported by Tilley et al. (2000) who found that the quality of project design and design documentation has fallen considerably over the past 15 to 20 years. McLennan and Jorss (2006) reported that poor design documentation includes the following root causes:

• inadequate project briefs based on unrealistic expectations of time and cost • lack of integration along the supply chain linking parties, and between project phases • poor understanding and low skills in risk assessment and management • inadequate use of CAD/computer technology in the design process and in the

compilation of specifications. To further manage cost overruns from design and scope changes the following qualitative measures need to be considered: (1) Design changes

• fix design standards for the life of the project as at the decision-to-build time • limit changes due to field conditions • limit any scope changes after the preliminary design has been accepted • undertake thorough checks on existing project conditions • ensure comprehensive and coordinated project design documentation • ensure better buildability/constructability through comprehensive site surveys • ensure better designer/client initiated scope change feedback processes


178

• ensure better feedback from previously constructed highway projects • ensure construction/maintenance engineers are part of the design team from the start

so as to eliminate most changes • initiate more preliminary field investigations during design phase • improve design quality • minimise changes after decision-to-build • improve independent design reviews • minimise unforeseen site conditions by ensuring detailed site surveys and

measurements. (2) Scope changes

• define the purpose of project and detailed scope to eliminate unnecessary changes • limit any scope change after preliminary design • review and sign-off of design scope by client • minimise scope change after the decision-to-build is made • ensure client involvement in scope development and preliminary design • ensure better communication of project objectives before execution • use feedback from previous projects.

The tight control of any scope creep by the client can limit variation works during construction and this is absolutely necessary for successful financial outcomes of projects. 5.10 Research limitations Studying the relationship of multiple variables is especially troublesome because the desired knowledge is complex in nature and many different combinations of factors need to be examined. There are a number of ways that statistical techniques can be misapplied to problems in the real world. These types of errors can lead to invalid or inaccurate results. The statistical model assumed error free measurement, at least of independent (predictor) variables. However, measurements are seldom perfect. Therefore, close attention is needed to be paid to the effects of measurement errors. This is especially important when dealing with noisy data or processes which are difficult to measure precisely (Helberg, 1996). Multivariate linear regression assumes the relationship between variables to be linear. In practice this assumption can virtually never be confirmed. Multivariate regression should not be used or predictions outside the range of the explanatory variables, for example beyond highway projects or the geographic areas of Queensland. Predictions at levels of unobserved variables which are not comparable to the observed research data can result in very misleading predictions (Harrell, 2000). This research was confined to the study of highway projects in the state of Queensland, Australia. Since construction costs can be specific to geographical/economic areas and time periods, caution is required when comparing the data and results to another situation. The research findings lend significance to a broad range of highway client organisations. However, it needs to be said that the historic data used in this research were founded on the following:


179

• projects were all of the design-bid-build project delivery method which may have lead to higher client exposure to design risks

• project contractors were all pre-qualified under a maximum capacity rating assessment and this pre-qualification process had the potential to limit client risk exposure to contract default

• contract payments were on the basis of schedule-of-rates and projects used bill of quantities in their contract elements

• specifically tailored contract provisions can have potential to limit the client highway organisation to the exposure of adverse physical and latent conditions events which sometimes occur in highway projects under certain circumstances.

5.11 Further research The model developed in this research could be refined by one or more of the following factors:

• adding one or more important project variables (e.g. estimator) to the model • using different type or market sector groupings such as toll roads • using a wider range of project sizes such as projects less than $1m to explore further

the preferred project size ranges at which projects are less susceptible to cost overrun • using other tests to examine violations of the regression assumptions and other

approaches to correct assumption violations • testing further data transformations of project variables • exploring other regression approaches to modelling project cost overrun in project

delivery. As well, the dramatic increase in the quantum percentage of project contingency across highway programmes by up to 35% may have certain negative effects on highway program deliveries which future research needs to undertake. In particular, both project managers and contractors involved in these types of project developments and construction will somehow manage to increase their payments — even within competitive procurement environments. Further research could be undertaken by testing the model by implementing two contingency allocation percentages on half the number of highway programme projects each, and the differences in budget cost reporting observed and reported on. Figure 5.1 shows this future research proposal that is designed to see if project managers try harder to keep payments down.


180

Highway construction projects

=>$1m budget

Open tenderhighway construction

projects

Negotiated pricehighway construction

contracts

Status quo10% contingency to

spread of projects


spread of projects

Over-and-abovecontingency % model to spread of projects


Highway construction projects

=>$1m budget

Open tenderhighway construction

projects

Negotiated pricehighway construction

contracts


spread of projects


spread of projects



Figure 5: 1: Future research proposal on project risk contingencies

The proposal could also be modified further by the inclusion of a blind placebo test to a small number of projects by the inclusion of unmodified contingency % within the modified contingency program. This mt ascertain if there is any a placebo effect that leads to further project cost savings. The linkage between the risk assessment and determining the appropriate probability distributions for various highway project types should be researched for a more analytical connection. Perhaps an Analytical Hierarchy Process (AHP) or another decision technique could be used. The level of effort required to produce this tighter linkage needs to be balanced against the potential increase in accuracy of the results. Project risk assessment is readily adaptable to fuzzy set theory where it is preformed in linguistic terms and then reduced to mathematical expressions. This may provide a convenient linkage between the evaluation of various highway risks and a numerical cost control technique for differing highway project types. This research effort has identified and quantified the risk drivers that affected the degree that projects significantly overrun in the highway infrastructure sector. Future research is needed to adequately identify and quantify the project cost overrun drivers as they relate to the construction of buildings and other infrastructure project types. Finally, there is a need for further studies into the storage and retrieval of historical project data. Historical data of finished projects have to be made more accessible. In view of the nature of construction projects with data being stored in different formats such as site diaries, calculation sheets, payment systems and even annotated drawings, the use of sophisticated information technology is very desirable. This further research into insufficient access to historical data will ultimately allow estimators to generate and deliver better project cost estimates.


181

5.12 Concluding remarks Project risks can be identified by a number of methods. The risk identification process used in this research has relied on documented experience gained during similar projects in the past. The accurate analysis the identification process is very important: in fact, it can be concluded that the main benefits of risk management come from the identification rather than the analysis stage. The primary basis of the research for identifying risks has been the use of available historical data, experience and insight. However, each construction project is unique, and similar risks may not recur on similar projects. Because there are no unerring procedures which may be consistently used to identify construction risks, the process relies heavily on the experience, interpretation and insight of key project personnel. The success of the research has rested on the importance of being able to assemble a group of suitably qualified people for the expert elicitation process undertaken during the research. The research into high cost overrun required a robust sample of highway infrastructure projects that was appropriate for this area of research. The data was required to be large enough to allow statistical analyses of cost overrun factors and project costs and research investigations found that data on actual costs in transportation infrastructure projects were relatively difficult to come by. One reason is that such data is time consuming to generate. The publication of confidential of commercial-in-confidence project data has been scarce as it can be of concern to organisations that have provided the information. The research utilised price movement indicators of construction outputs as a valuable tool in the analysis output price indexes so as to enable the measurement of changes over time in the price of new roads and bridges. RICI indices were used over the analysis period to factor up the historical project cost information. These factors ranged from 3.4% to 16.3% and were applied to the project programmed and actual costs for the corresponding financial years in which the project was delivered. The focus of the research analyses were based on client exposure to project cost overrun, not that of contractors delivering the projects. The literature research identified a number of considerations to be taken into account when adopting the cost overrun factors. These included:



• contract payment types that focused on schedule of rates and bill of quantities • contract clauses that were designed to reduce the client’s exposure to certain

construction risks • tender evaluation techniques • contract provisions that limit the client's exposure to adverse physical and latent

conditions and wet weather events. The risk identification process in this research has been beneficial as it focused the attention of the client on the detection and control strategies of risks. The research has provided an analytic procedure that allows the recognition of realistic cost overrun contingencies to projects and investigates the influences of risk factors on clients' risk handling decisions


182

The research has not looked at the whole project development life cycle but rather that part where major cost overrun occurs, namely from the time the decision-to-build has been made by the client and sound lessons have been drawn from the researcher’s post-mortems of the many highway projects considered. The analysis has produced important findings about the reasons that highway projects have overrun and has provided conclusive evidence of the most important risks that highway agencies need to focus their efforts on. Of particular concern is that of changes in project designs and scope changes during project development. The research process used the experience in highway construction and the professional judgement of the researcher to determine the listings of work types and reasons for cost overrun using the NGT process. From initial interviews by the researcher, it was important to take note of interviewees’ concerns about time management. In the rapidly paced world of construction management, it was reinforced that an expert elicitation method needed to be efficient because experts in construction management were continually confronted with insufficient time. The selection of the NGT elicitation technique was therefore determined to a large extent by the time that experts were prepared to allocate to the research project. The final stage of the research process has involved the investigation into statistical models which can explain the correlation between the cause, effect and other relationships relating to cost overrun in highway construction projects. While the term model can be used in many ways, however for this research it has referred to the dynamic framework or schema that helps portray key concepts and propositions of the research phenomenon. The regression analysis demonstrated a weak correlation between the size of highway projects, as measured in indexed programmed cost and the size of cost overruns. The correlation evolved after data transformation was carried out to improve the model. It can also be concluded from the research that the arbitrary application of a base contingency percentage figure, such as 10%, to accommodate project risk can lead to those projects reporting substantial budget overrun. The provision of more realistic contingency percentages across such projects will go a long way in providing for better reporting of highway project and programme results and associated key project performance indicators.Sensitivity testing was investigated for the stepwise regression models developed in order to ascertain whether additional outlier data still unduly influenced the models. The model developed has demonstrated that as the size of a project increases in budgeted programmed cost, then the percentage cost overrun reduces in value. It further suggests that to counter the potential cost overrun, the over-and-above additional indicative contingency percent that should be factored into all types of highway projects by the client to compensate for undefined (or under-defined) project risks can be varied depending on the project size To aid in the identification of future highway risks, this research has identified and compiled listings of highway project risks that have lead to cost overrun, as well as the incidence of each risk across the sample highway projects. The development of such listings should help to prevent projects risks from being overlooked by clients. The research has also illustrated how highway projects have overrun — and have done so beyond any reasonable expectations. The research has considered historical projects where the cost overrun was significantly beyond what might have ever been anticipated. The aim of the research was to contribute to the understanding of how highway projects go wrong, when they do, and in particular to draw some lessons from this exploration.

BIBLIOGRAPHY

183

BIBLIOGRAPHY

AACE. 2000. International Risk Management Dictionary. Journal of Cost Engineering., 42 (4). Abbasi, G. and H. Al-Mharmah. 2000. Project management by the public sector in a developing country. International Journal of Project Management., 18 (2): 105-09. Abrahamson, M. W. 1984. Risk management. International Construction Law Review, 1 (3): 241-264. Adameitz, M. 2003. Short Report on Risk Management. ttp://members.ozemail.com.au/~sqain/meeting_reports/Risk_report.htm (accessed 21 October 2003, 2003). Adler, M. and E. Ziglio. 1996. Grazing into the oracle, The Delphi method and its application to social policy and public health. London: Cromwell press. Agresti, A. 1996. Introduction to Categorical Data Analysis. New York: Wiley. Ahmed, S. M., R. Ahmed and D. E. Saram. 1999. Risk management trends in the Hong Kong construction industry: a comparison of contractor and owners perceptions. Journal of Engineering, Construction and Architectural Management 6/3:225-234., 6. Akinci, B. and M. Fischer. 1998. Factors affecting contractors' risk of cost overburden. Journal of Management in Engineering, 14 (1): 67-76. Akpan, E. O. P. 1987. Stochastic network scheduling system for optimum resource utilisation, University of Bradford, Bradford, England. Akpan, E. O. P. and O. Igwe. 2001. Methodology for determining price variation in project execution. Journal of Construction Engineering and Management, 127 (5): 367-73. Al-Bahar, J. F. and K. C. Crandall. 1990. Systematic risk management approach for construction projects. Journal of Construction Engineering and Management, 116 (3): 533-546. Albino, V. and A. C. Garavelli. 1998. A neural network application in sub-contractor rating in construction firms. International Journal of Project Management, 16 (1): 9-14. Alsagoff, S. A. and P. McDermott. 1994. Relational Contracting: A Prognosis for the UK Construction Industry? In Proceedings of CIB W92, Procurement Joint risk management and relational contracting, 11-19. Hong Kong.: University of Hong Kong. Al-Sobiei, O. S., D. Arditi and G. Polat. 2005. Managing Owner's Risk of Contractor Default. Journal of Construction and Engineering Management, 131 (9): 973-78 (accessed September 2005).


184

Anderson, S. D., D. Fischer and S. Rahman. 1999. Constructability issues for highway projects. Journal of Management Engineering, 15 (3): 60-68. Anderson, S. D. and R. L. Tucker. 1994. Improving project management of design. Journal of Management Engineering, 10 (4): 35-44. Ang, A. H.-S. and W. H. Tang. 1984. Probability Concepts in Engineering Planning and Design, Volume II Decision, Risk and Reliability. Vol. II - Decision, Risk and Reliability. New York, NY: John Wiley and Sons. Arditi, D. and F. Yasamis. 1997. Incentive/disincentive contracts: perceptions of owners and contractors. Journal of Construction Engineering and Management, 124 (5): 361-73. Arndt, R. 1999. Efficient risk allocation in privately funded infrastructure projects. PhD, Civil Engineering, University of Melbourne, Melbourne. ASCE. ed. 1989. Avoiding and Resolving Disputes in Underground Construction: Successful Contracting Practices and Guidelines. New York: American Society of Civil Engineers. Ashley, D. B. 1987. Knowledge-based approaches to construction project planning. In New Trends in Project Management, Proc. 11th International Expert Seminar, April 1987, 163-188. Zurich: International Project Management Association. Ashley, D. B. 1989. Project risk identification using inference subjective expert assessment and historical data. In Transactions of Internet International Expert Seminar, 9-28. Atlanta.: Atlanta Institute of Technology. Ashworth, A. 1994. Cost Studies of Building. London: Longman Scientific and Technical. Ashworth, A. and M. Skitmore. 1983. Accuracy in estimating. Chartered Institute of Building (Occasional Paper No. 27). Avots, I. 1984. Information systems for matrix organisations. In Matrix Management Systems Handbook, ed. D. I. Cleland (ed.). New York.: Van Nostrand Reinhold. Ayyub, B. M. and A. Haldar. 1984. Project Scheduling Using Fuzzy Set Concepts. Journal of Construction Engineering and Management, 110(2). Ayyub, B. M. and R. H. McCue. 1997. Probability, Statistics and Reliability for Engineers. Boca Raton, FL.: CRC Press. Ayyub, B. M. and R. Wilcox. 2000. A Risk-based Compliance Approval Process for Personal Flotation Devices. Potomac, MD.: BMA Engineering. Baccarini, D. 2004. Accuracy in estimating project cost construction contingencies - A statistical analysis. In COBRA 2004. Leeds Metropolitan University: RICS Foundation. Baker, S., D. Ponniah and S. Smith. 1999. Survey of risk management in major U.K. companies. Journal of Professional Issues in Engineering Education and Practice, 125 (3): 94-102.

BIBLIOGRAPHY

185

Banks, J., J. S. Carson and B. L. Nelson. 1996. Discrete - Event System Simulation 2nd edition. 2nd ed. Upper Saddle River, NJ.: Simon and Schuster. Bannister, D. and S. Fransella. 1989. Inquiring Man: The Psychology of Personal Constructs. 3rd edition ed. London: Routledge. Barnes, M. 1981. Project management by motivation. In Proc., PMI/ INTERNET 1981 Joint Symposium, 101-123. Boston. Barnett, V. and T. Lewis. 1984. Outliers in Statistical Data. 2nd Ed. ed. New York: John Wiley & Sons, Inc. Barrie, D. S. and B. C. Paulson. 1992. Professional Construction Management. New York: McGraw-Hill, Inc. Bathurst, E. P. and D. A. Butler. 1977. Building Cost Control Techniques and Economics. London: Heinemann. Begg, D., S. Fisher and R. Dornbusch. 2000. Economics. London: McGraw-Hill. Bennett, R. 1991. How is management research carried out? In The Management Research Handbook, eds. N. C. Smith and P. Danity, 85-103. London: Routledge. Bent, J. A. 1988. Project control: An introduction. In Project Management Handbook, 2nd ed., eds. C. D.I and W. R. King (eds.). New York: Van Nostrand Reinhold. Berny, J. 1989. A new distribution function for risk analysis. Journal of the Operational Research Society, 40: 1121-7. Blair, A. N. 1999. Risk Analysis of Cost and Schedule of Complex Engineering Systems. Doctoral Thesis, University of Maryland, College Park, MD. Blair, A. N., B. M. Ayyub and W. J. Bender. 2000. Fuzzy Stochastic Risk-Based Decision Analysis: MOB Case Study. Marine Structures Journal, Society of Naval and Marine Engineers, Sept. 2000. Blair, A. N., L. M. Lye and W. J. Campbell. 1993. Forecasting construction cost escalation. Canadian Journal of Civil Engineering, 20: 602-12. Blake, E. H. 1900. Extras and omissions in building contracts. The Surveyors' Institutuion transactions, XXXII: 525-72. Blok, F. G. 1982. Contingency: definition, classification and probability - Congress Paper B-3. In 7th International Cost Engineers Congress. London. Bradley, R., M, M. Powell, G and M. Soulsby, R. 1990. Quantifying variations in project-cost estimates. Journal of Management in Engineering, 6 (1): 99-106.


186

Bramble, B. B. and M. D. Cipollini. 1998. Resolution of disputes to avoid construction claims. In National Cooperative Highway Research Program Synthesis of Highway Practice: 214. Washington, D.C: Transportation Research Board, National Research Council. Bryman, A. and D. Cramer. 1999. Qualitative Data Analysis with SPSS Release 8 for Windows: A Guide for Social Scientists. London: Routledge. BSI. 1999. Guide to project management: in British Standard BS 6079. Vol. 2. London.: British Standards Institution. Burger, R. 2003. Contingency, qualifying the uncertainty. Journal of Cost Engineering., 45 (8): 12-17. Camerer, C. F. 2003. Behavioral game theory: experiments in strategic interaction. Princeton: Princeton University Press. Carelli, G. 1989. The Manus Opera House. Icaro, Varig Inflight Magazine, 17. Carmichael, D. G. 2000. Contracts and International Project Management. Rotterdam: A.A. Balkema Publishers. Carver, R. H. and J. G. Nash. 2005. Doing Data Analysis with SPSS Version 12. Ontario: Brooks/Cole Thompson Learning. Chan, D. W. M. and M. M. Kumaraswamy. 1997. A comparative study of causes of time overruns in Hong Kong construction projects. International Journal of Project Management, 15 (1): 55-63. Chang, A. S.-T. 2002. Reasons for cost and schedule increases for engineering design projects. Journal of Management in Engineering, January 2002: 29-36. Chapman, C. B. and S. Ward. 1997. Project Risk Management: Processes, Techniques, and Insights. Chichester: Wiley N.Y. Chapman, R. J. 1998. Effectiveness of working group risk identification and assessment techniques. International Journal of Project Management, 16 (6): 333-43. Charette, R. 1989. Software Engineering Risk Analysis and Management. New York: McGraw-Hill. Charles, T. J. and M. A. Andrew. 1990. Predictors of cost overrun rates. Journal of Construction Engineering and Management: ASCE., 116 (3): 569-79. Chee, T. S. and K. T. Yeo. 1995. Risk analysis of a build-operate transfer (BOT) power plant project. In IEEE Annual International Engineering Management Conference, 275-80. CII. 1989. Management of project risks and uncertainty; Publication 6-8. Cost/Schedule Controls Task Force. Austin, Texas: Construction Industry Institute.

BIBLIOGRAPHY

187

CII. 1996. The Partnering Process - Its Benefits, Implementation, and Measurement, . Austin, TX.: Construction Industry Institute,. Clark, F. D. and A. B. Lorenzoni. 1985. Applied Cost Engineering; 2nd Edition. New York: Marcel Dekker, Inc. Clemen, R. B. and R. L. Winkler. 1985. Limits in the precision and value of information from dependent sources. Operations Research, 33: 427-42. Clough, R. H. 1986. Construction Contracting. New York: John Wiley & Sons, Inc. Cohen, S. S. 1985. Operational research. London: Edward Arnold. Cooper, D. F., D. H. MacDonald and C. B. Chapman. 1985. Risk analysis of a construction cost estimate. International Journal of Project Management, 3 (3): 141-9. Cowie, G. 1987. Enough scope yields better estimates. Journal of Construction Engineering and Management, 28 (8): 21-27. Creedy, G. D. 2004. Discussions with A. McLennan on Pacific Motorway Contract Finalisations. Brisbane. Crowley, L. G. 1993. Risk Assessment of Competitive Procurement in Public Agencies. PhD, Texas A&M University, Texas. Crowley, L. G. and D. E. Hancher. 1995. Evaluation of competitive bids. Journal of Construction and Engineering Management, 121 (2): 238-45. Curole, M. A. 1995. Mars Risk Management Program. In 1995 Offshore Technology Conference Proceedings. Houston, TX. Curtis, B., S. Ward and C. B. Chapman. 1991. Roles, responsibilities and risks in management contracting: Construction Industry Research and Information Association (CIRIA) Special Publication 81. Cvethovich, G. and T. C. Earle. 1992. Environmental hazards and the public. Journal of Social Issues, 48 (4): 1-20. Dake, K. 1992. Myths of nature and the public. Journal of Social Issues, 48 (4): 21-38. Dalcher, D. 1993. The new project management mindset for the 21st century. In Proceedings of 1st British Project Management Colloquium. Henley-on-Thames, UK. Dalkey, N. C. 1968. The Delphi Method: An experimental study of group opinion. Santa Monica, California: The Rand Corporation. de Cano, A. and P. E. de le Cruz. 2002. Integrated Methodology for Project Risk Management. Journal of Construction and Engineering Management, 128 (6): 473-84.


188

de Neufville, R. D. and D. King. 1991. Risk and need-for-work premiums in contractor bidding. Journal of Construction Engineering and Management, American Society of Civil Engineers, 117 (4): 659-73. del Cano, A. 1992. Continuous project feasibility study and continuous project risk assessment. International Journal of Project Management, 10 (3): 165-70. Delbecq, A. L., A. H. Van de Ven and D. H. Gustafson. 1975. Group Techniques for Program Planning: A Guide to Nominal Group and Delphi Processes. Glenview, IL.: Scott, Foresman and Company. Delbecq, A. L., A. H. Van de Ven and D. H. Gustafson. 1986. Group Techniques for Program Planning: A Guide to Nominal Group and Delphi Processes. Wis.: Green Briar Press. Dey, P. K. 1999. Process re-engineering for effective implementation of projects. International Journal of Project Management, 17. (3): 147-59. Dey, P. K. 2001. Decision support system for risk management: a case study. Management Decisions, 39 (8): 634-49. Dey, P. K., M. T. Tabucanon and S. O. Ogunlana. 1996. Petroleum pipeline construction planning: a conceptual framework. International Journal of Project Management, 14 (4): 231-40. Dias, A., and P. G. Ioannou. 1995. A desirability model for the development of privately-promoted infrastructure projects.' UMCEE Report No. 95-09. Ann Arbor, Mich: Civil Engineering Dept., University of Michigan. Dozzi, S. P., S. M. AbouRizk and S. L. Schroeder. 1996. Utility theory model for bid markup decisions. Journal of Construction and Engineering Management, 122 (2): 119-24. Drew, D. S. and M. Skitmore. 1992. Competitiveness in bidding: a consultant's perspective. Journal of Construction Management and Economics, May: 227-47. Dyer, D. and J. H. Kagel. 1996. Bidding in common value auctions: how the commercial construction industry corrects for the winner's curse. Management Science Journal, 42 (10.): 1463-75. Eden, C., F. Ackerman and T. Williams. 2005. The amoebic growth of project costs. Project Management Journal, 36 (2): 15-27. Edum-Fotwe, F. T., A. Thorpe and R. McCaffer. 1999. Organisational relationships within the construction supply chain. In Proceedings of the Joint Triennial CIB Symposium of W55 and W65, 186-94. Cape Town: CIB. Edwards, L. 1995. Practical Risk Management in the Construction Industry, . London: Thomas Telford Publications.

BIBLIOGRAPHY

189

Egan, S. J. 1998. Rethinking Construction. The report of the Construction Task Force: Department of the Environment, Transport and the Regions. Eggleston, B. 1996. The New Engineering Contract - A Commentary. London: Blackwell Science. El-Choum, M. K. 1994. Identification and modelling of construction cost overruns parameters for public infrastructure projects using multivariate statistical methods. PhD, Stevens Institute of Technology, Castle Point, Hoboken, New Jersey. Elhag, T. M. S. and A. H. Boussebaine. 1998. Statistical analysis and cost models development: Research Report No. 2. Liverpool, U.K.: School of Architecture and Building Engineering, University of Liverpool. Elkington, P. and C. Smallman. 2002. Managing project risks: a case study from the utilities sector. International Journal of Project Management,, 20 (1): 49-57. Elsam, R. 1826. The Practical Builder's Perpetual Price Book. London: Kelly. Emhjellen, M., K. Emhjellen and P. Osmundsen. 2003. Cost estimation overruns in the North Sea. Project Management Journal, March: 23-31. Emory, C. W. 1980. Business Research Methods, 2nd Edition. Illinois: Irwin & Co.. Farnum, N. R. and L. W. Stanton. 1987. Some results concerning the estimation of beta distribution parameters in PERT. Journal of the Operational Research Society, 38 (3): 287-90. Fayek, A., D. M. Young and C. F. Duffield. 1998. Survey of tendering practices in the Australian construction industry. EMJ - Engineering Management Journal, 10 (4): 29-34. FDOT. 2003. Chapter 3: General management principles for transit capital projects. In Project and Construction Management Guidelines, Vol. 2003 Update. New York: US Department of Transportation. Fellows, R., D. Langford, R. Newcombe and S. Urry. 2002. Construction Management in Practice. 2nd ed. Oxford: Blackwell Science Ltd. Original edition, 1983. Fishburn, P. C. 1984. Foundations of risk measurement: Risk as probable loss. Management Science Journal, 30 (4): 396-406. Fisk, E. R. 1997. Construction Project Administration. 5th Edition ed. Englewood Cliffs, NJ: Prentice-Hall. Flanagan, R. and G. Norman. 1993. Risk Management and Construction. Oxford: Blackwell Scientific Press. Flyvbjerg, B., N. Bruzelius and W. Rothengatter. 2003. Megaprojects and Risk: An anatomy of ambition. Cambridge, UK: Cambridge University Press.


190

Flyvbjerg, B., M. S. Holm and S. Buhl. 2002. Underestimating Costs in Public Works Projects: Error or Lie? Journal of the American Planning Association, 68 (3): 279-95 (accessed 2002). Freudenburg, W. R. 1988. Perceived risk, real risk: social science and the art of probabilistic risk assessment. Science, 242: 44-49. Freudenburg, W. R. and S. K. Pastor. 1992. NIMBY's and LULU's: stalking the syndromes. Journal of Social Issues, 48 (4): 39-62. Friedman, L. 1956. A competitive bidding strategy. Journal of Operations Research, 1 (4): 104-12. Glower, W. J. 1990. The US perspective on procedures for subsurface ground conditions claims. International Construction Law Review, 7 (2): 155-86. Gransberg, d. and M. Ellicot. 1997. Best-value contracting criteria. Cost Engineering, 39 (6): 31-4. Gregory, R. 1989. Improving risk communication; questions of content and intent. In Prospects and problems in risk communication. Institute of Risk Research, ed. W. Leiss. Ontario: University of Waterloo Press. Gregory, R. and R. Mendelson. 1993. Perceived risk, dread, and benefits. Risk Analysis, 13 (3): 259-64. Guadagnoli, E. and W. F. Velicer. 1988. Relation of sample size to the stability of component patterns. Psychological Bulletin, 103 (2.): 265-75. Gustafson, D. H., R. M. Shukla, A. L. Delbecq and G. W. Walster. 1973. A comparative study of differences in subjective likliehood estimates made by individual, interacting groups. Delphi groups and nominal groups. Organisational Behavior Human Performance, 9: 280-91. Hackney, J. W. 1986. Contingency allowances and overrun probabilities: Paper B3. In Proceedings of the 9th International Cost Engineering Congress. Oslo, Norway. Hair, J. F., J. Anderson, R. L. Tatham and Black. 1998. Multivariate Data Analysis. 5th edition ed. Upper Saddle River, N.J: Prentice Hall, c1998.: Prentice Hall. Hair, J. F. and R. L. Tatham. 1995. Multivariate Data Analysis. 4th edition ed. New York: Macmillan. Hairston, D. E., P. DeLise and W. J. Skerpan. 1997. Delivered into providence. Civil Engineering (New York), 67 (2): 54-56. Halligan, D. W., W. T. Hester and H. R. Thomas. 1987. Managing unforeseen ground conditions. Journal of Construction Engineering and Management, 113 (2): 273-87.

BIBLIOGRAPHY

191

Halpin, D. W. 1985. Financial and Cost Concepts for Construction Management. New York, N.Y.: John Wiley and Sons. Halpin, M. G. 1998. Risk Management and Client Satisfaction, School of Construction Management and Property, Queensland University of Technology, Brisbane. Harman, H. H. 1967. Modern Factor Analysis. 2nd ed. Chicago: University of Chicago Press. Harrell, E. E. 2000. Regression Modelling Strategies with Applications to Linear Models, Logistic Regression and Survival Analysis. New York: Springer. Harris, F. C. and R. McCaffer. 1995. Modern Construction Management. London, UK: Blackwell Science Ltd. Hartgen, D. T., V. B. Bowman and M. W. Horner. 1997. Baseline market forecasts for state highway construction engineering, maintenance and administration programs 1997-2001: Transportation Publication Number 151. Charlotte, N.C: Centre for Interdisciplinary Transportation Studies, University of North Carolina. Hartgen, D. T. and A. P. Talvitie. 1995. Comparative performance of transportation systems: pitfalls and prospects. In World Conference on Transit Research. Sydney, Australia. Hartman, F., P. Snelgrove and R. Ashra. 1997. Effective wording to improve risk allocation in lump sum contracts. Journal of Construction Engineering and Management, 123 (4): 379-87. Hartman, F. T. 2000. Don't Park Your Brain Outside-a practical guide to improving shareholder value with SMART management: NC:PMI Publications. Hatush, Z. and M. Skitmore. 1998. Contractor selection using multicriteria utility theory: an additive model. Building and Environment Journal, 33 (105-115). Hegazy, T. and A. Ayed. 1998. Neural network model for parametric cost estimation of highway projects. Journal of Construction Engineering and Management, May/June 1998: 210-18. Hegazy, T. and O. Moselhi. 1995. Elements of cost estimation: a survey in Canada and the United States. Cost Engineering, 37 (5): 27-33. Helberg, C. 1996. Pitfalls of data analysis. Practical Assessment, Research and Evaluation, 5 (5). http://PAREonline.net/getvn.asp?v=5&n=5. Herbsman, Z. J. 1986. Model for forecasting highway construction cost:. In Transportation Research Record 1056. Washington, D.C.: Transportation Research Board. Hertz, D. B. 1964. Risk analysis in capital investment. Harvard Business review, 42 (1): 95-106.


192

Hester, W. T., J. A. Kuprenas and T. C. Chang. 1991. Construction Changes and Change Orders: Their Magnitude and Impacts. Berkeley: University of California. Hetland, P. W. 1994. Towards ultimate control of megaprojects in the North Sea. In Global Project Management Handbook, eds. D. I. Cleland and R. Gareis. New York: McGraw-Hill Book Co. Ltd. Hibbert, P., D. Merrifield and A. Taylor. 1990. Key Factors On Contractual Relationships. London: RICS. Hillebrandt, P. M. and J. Cannon. 1990. The Modern Construction Firm. London: Macmillan. Hillson, D. 1999. Developing effective risk responses. In PMI Annual Seminar and Symposium. Philadelphia: Project Management Institute. Hillson, D. 2002. Extending the risk process to manage opportunities. International Journal of Project Management, 20 (3): 225-40. Hinze, J. and K. Walsh. 1997. Shoring as a bid item. Managing Engineered Construction in Expanding Global Markets; ASCE Construction Congress Proceedings: 226-32. HM Treasury. 1993. CUP Guidance: No. 41 - Managing risk and contingency for works projects. London: HM Treasury Central Unit on Procurement. Ho, O. S. 2000. Enhancing construction technology through strategic partnering: a contractor's perspective. In Symposium on Quality Housing: Partnering Symposium 2000. Hong Kong. Holt, G. D., P. O. Olomolaiye and F. C. Harris. 1993. A conceptual alternative to current tendering practice. Building Research and Information., 21: 167-72. Holt, G. D., P. O. Olomolaiye and F. C. Harris. 1994. Factors influencing UK construction clients' choice of contractor. Building and Environment Journal, 29: 241-48. Holt, G. D., P. O. Olomolaiye and F. C. Harris. 1995. A review of contractor selection practice in the UK construction industry. Building and Environment Journal, 30 (.): 553-61. Holt, G. D., P. O. Olomolaiye and F. C. Harris. 1996. Applying cluster analysis to construction contractor classification. Building and Environment Journal, 31: 557-568. Holt(a), G. D., P. O. Olomolaiye and F. C. Harris. 1994. Evaluating pre-qualification criteria in contractor selection. Building and Environment Journal, 29: 437-448. Hughes, W. 1997. Construction management contracts: law and practice. Journal of Engineering, Construction and Architectural Management, 4 (1): 59-79. Hutcheson, G. and N. Sofroniou. 1999. The multivariate social scientist: Introductory statistics using generalised linear models. Thousand Oaks, CA: Sage Publications.

BIBLIOGRAPHY

193

Hutchings, D. M. and J. P. Christofferson. 2000. A study of management practices in small-volume home building companies. In ASC Proceedings of the 36th Annual Conference. Purdue University, Indiana. Ibbs, C. W. and W. E. Allen. 1995. Quantitative Impacts of Project Changes. Berkeley: University of California. Ibbs, W. C. and D. B. Ashley. 1987. Impact of various construction contract clauses. Journal of Construction Engineering and Management, 113 (3): 501-21. ICE. 1998. Institution of Civil Engineers: Risk analysis and management for projects (RAMP). London: Thomas Telford. In't veld, J. and J. Peeters. 1989. Keeping large projects under control: the importance of contract type selection. Journal of Project Management, 7 (3): 155-62. Iseley, T. and S. Gokhale. 2003. Equipment Productivity. South Carolina: CRC Press LLC. Jaafari, A. and A. Schub. 1990. Surviving failure: Lessons from field study. Journal of Construction and Engineering Management, 116 (1): 68-86. Jackson, J. 1989. Response to Gregory section. In Prospects and problems in risk communication. Institute of Risk Research, ed. W. Leiss (ed.). Ontario: University of Waterloo Press. Jergeas, G. F. and F. T. Hartman. 1994. Contractors protection against construction claims. AACE Transactions. Jones, D. S. 1996. Philosophies of risk allocation - the case for foreseability. International Construction Law Review, 13 (4): 570-85. Jones, D. S. 2000. Project Alliances. In Proceedings of Conference on 'Whose Risk? Managing Risk in Construction - Who Pays?' Hong Kong. Kahnaman, D. and A. Tversky. 1972. Subjective probability: A judgement of representativeness. Cognitive Psychology, 3: 430-54. Kaiser, H. F. 1958. The varimax criterion for analysis rotation in factor analysis. Psychometrika, 23: 187-200. Kangari, R. 1995. Risk management perceptions and trends of US construction. Journal of Construction Engineering Management, 121 (4): 422-29. Kangari, R. and L. S. Riggs. 1989. Construction risk assessment by linguistics. IEEE Transactions in Engineering Management, 36 (2): 126-31. Kantardzic, M. 2003. Data Mining : Concepts, Models, Methods and Algorithms. Piscataway, N.J.: IEEE Press.


194

Kaplan, S. 1991. The General Theory of Quantitative Risk Assessment. In Proceeding of the Fifth Conference - Risk-Based Decision Making in Water Resources. Reston, VA: ASCE. Kaplan, S. and B. J. Garrick. 1984. On the quantitative definition of risk. Risk Analysis., 1: 11-28. Kayode, M. O. 1979. The Art of Project Evaluation. Ibadan, Nigeria: University Press. Keeney, R., L and D. von Winterfeldt. 1991. Eliciting probabilities from experts in complex technical problems. IEEE Transactions in Engineering Management, 38 (3): 191-201. Khan, Z. 1998. Risk premiums associated with exculpatory clauses. Masters, University of Calgary, Canada, Alberta. Khosrowshahi, F. 1999. Neural network model for contractors' pre-qualification for local authority projects. Journal of Engineering, Construction and Architectural Management, 6 (3): 315-28. Kim, S. and D. Bajaj. 2000. Risk Management in Construction: An Approach for Contractors in South Korea. Cost Engineering, Association for the Advancement of Cost Engineering, 42 (1). Kirszner, L. G. and S. R. Mandell. 1992. The Holt Handbook. 3rd Ed. ed. New York.: Harcourt Brace Jovanovich,. Kohrs, R. H. and G. C. Welngarten. 1986. Measuring successful technical performance - A cost/schedule/technical control system, in: Measuring Success,. In Proceedings of the 18th Annual Seminar/Symposium of the Project Management Institute, 158-64. Montreal, Canada. Kometa, S. T., P. O. Olomolaiye and F. C. Harris. 1996. Review of client-generated risks to project consultants. International Journal of Project Management, 14 (5): 273-79. Koppula, S. D. 1981. Forecasting engineering costs: two case studies. Journal of Construction Division; ASCE, 107 (4): 733-43. Koskinen, K. U., P. Pihlanto. and H. Vanharanta. 2002. Tacit knowledge acquisition and sharing in a project work context. International Journal of Project Management, 21: 281-290. Kozek, J. and C. Hebberd. 1998. Contracts: share the risk. Journal of Construction Engineering and Management, 111 (2): 356-61. Kumamoto, H. and E. J. Henley. 1996. Probabilistic Risk Assessment and Management for Engineers and Scientists. 2nd Edition ed. New York, NY: IEEE Press. Kumaraswamy, M. M. 1997. Common categories and causes of construction claims. Construction Law Journal, 13 (1): 21-34. Kumaraswamy, M. M. and S. M. Dissanayaka. 1998. Linking procurement systems to project priorities. Building Research & Information, 26 (4): 223-38.

BIBLIOGRAPHY

195

Kumaraswamy, M. M. and J. D. Mathews. 2000. Improved sub-contractor selection employing partnering principles. Journal of Management in Engineering, 16 (3): 47-57. Kumaraswamy, M. M. and D. H. T. Walker. 1999. Multiple performance criteria for evaluating construction contractors. In CIB W92 Procurement Systems: A Guide to Best Practice in Construction, eds. S. Rowlinson and E. McDermott, 228-51. London: CIB. Lam, K. C., T. Ng, M. Skitmore and O. Cheung. 2002. Decision support system for contractor pre-qualification - artificial neural network model. Journal of Engineering, Construction and Architectural Management, 7 (3): 251-66. Langford, D. A., P. Kennedy, J. Conlin and N. McKenzie. 2003. Comparison of construction costs on motorway projects using measure and value and alternative tendering initiative contractual arrangements. Journal of Construction Management and Economics, 21 (December): 831-40. Lansdown, J. 1982. Expert Systems: Their Impact on the Construction Industry. London: RIBA. Latham, S. M. 1994. Constructing the Team - Final Report of the Government / Industry Review of Procurement and Contractual Arrangements in the UK Construction Industry. London. Laufer, A. 1996. Simultaneous Management: Managing Projects in a Dynamic Environment. New York, NY: American Management Association. Law, A. and W. D. Kelton. 1991. Simulation Modeling and Analysis. 2nd ed. New York: McGraw-Hill, Inc. Leavitt, D., S. Ennis and M. P. 1993. The cost escalation of rail projects: Using previous experience to re-evaluate the Calspeed estimates; Working Paper No. 567. Berkeley: Institute of Urban and Regional Development, University of California. Leu, S. S., A. T. Chen and C. H. Yang. 2001. A GA-based optimal model for construction time-cost trade-off. International Journal of Project Management, 19: 47-58. Levine, H. 1995. Risk management for dummies: managing schedule cost and technical risks and contingencies. PM Network, October: 30-32. Lewis-Beck, M. S. 1993. Applied regression: An introduction. In Regression Analysis, ed. M. S. Lewis-Beck. London: Sage Publications, Ltd. Linstone, H. A. and M. Turoff. 1975. The Delphi Method Techniques and Applications. Reading, MA.: Addison-Wesley. Loftus, J. 1999. Project Management of Multiple Projects and Contracts. London: Thomas Telford Ltd.


196

Macaulay, S. 1963. Non-contractual relations in business: a preliminary study. American Sociological Review, 28: 55-67. MacCrimmon, K. R. and D. A. Wehrung. 1986. Taking Risks: The Management of Uncertainty. New York: Free Press. Macneil, I. R. 1980. The New Social Contract: An Inquiry into Modern Contractual Relations. New Haven, NJ.: Yale University Press. Mahdi, I. M., M. J. Riley, S. M. Fereig and A. P. Alex. 2002. A multi-criteria approach to contractor selection. Journal of Engineering, Construction and Architectural Management, 9 (1): 29-37. Mak, S. and D. Picken. 2000. Using risk analysis to determine construction project contingencies. Journal of Construction Engineering and Management, 126 (2): 130-36. Mak, S., J. Wong and D. Picken. 1998. The effect of contingency allowances of using risk analysis in capital cost estimating: a Hong Kong case study. Journal of Construction Management and Economics, 16: 615-19. Marco, D. 2000. Building and Managing the Meta Data Repository: The Full Lifecycle Guide. New York: John Wiley. Marks, R. E. 2000. Efficient, competitive, and informed markets: Australian Corporations Law and Auctions. Sydney: University of New South Wales/University of Sydney. Mason, R. L., R. F. Gunst and J. l. Hess. 1989. Statistical Design and Analysis of Experiments - With Application to Engineering and Science. New York: John Wiley and Sons, Inc. Masterman, J. W. E. 1994. A study of the basis upon which clients in the construction industry choose their building procurement systems. PhD, Institute of Science and Technology, University of Manchester, Manchester, UK. Matthews, J. 1996. A project partnering approach to the main contractor sub-contractor relationship. PhD thesis, Civil Engineering, Loughborough University, Loughborough. McCoy, F. A. 1986. Measuring success: Establishing and maintaining a baseline. In Proceedings of the 18th Annual Seminar/symposium of the Project Management Institute,, 47-52. Montreal, Canada. McCuen, R. H. 1996. The Elements of Academic Research. New York: ASCE Press. McDermott, P. ed. 1999. Strategic and emergent issues in construction procurement. Edited by S. Rowlinson, E. McDermott and F. N. Spoon, CIB W92 "Procurement Systems: A Guide to Best Practice in Construction". London: E and F.N. Spon,. McGowan, P. H., R. Malcom, W. Horner, D. Jones and P. A. Thompson. 1992. Allocation and Evaluation of Risk in Construction Contracts, Occasional Paper No. 52. Ascot: Chartered Institute of Building.

BIBLIOGRAPHY

197

McKim, R. A. 1992. Risk Management Back to Basics. Cost Engineering, Association for the Advancement of Cost Engineering, 34(12). McLennan, A. and P. Jorss. 2006. Getting it right the first time: A plan to reverse the declining standards in project design documentation within the building and construction industry. In Clients Driving Innovation: Moving Ideas into Practice: Second International Conference 2006. Gold Coast, Queensland, Australia: Cooperative Research Centre for Construction Innovation. Merkhover, M. W. 1987. Quantifying judgemental uncertainty: Methodology, experiences and insights. IEE Transactions on Systems, Management, and Cybernetics, 17 (5): 741-52. Merrow, E. W. 1978. Understanding the outcomes of mega-projects: Rand/R3360 PSSP. Santa Monica, CA: Rand Corporation. Might, R. 1984. An evaluation of the effectiveness of project control systems. IEEE Transactions on Engineering Management, 31 (3): 127-37. Milgrom, P. R. and R. J. Weber. 1982. A theory of auctions and competitive bidding. Econometrica, 50 (5): 1089-122. Minato, T. and D. B. Ashley. 1998. Data driven analysis of "corporate risk" using historical cost data. Journal of Construction Engineering and Management, 124 (1): 42-47. Molenaar, K. R. 2005. Programmatic cost risk analysis for highway megaprojects. Journal of Construction Engineering and Management, 131 (3): 343-53 (accessed March 1, 2005). Morgan, D. L. 1997. Focus groups as qualitative research. Second ed. London: Sage Publications. Morris, P. W. G. 1988. Managing project interfaces - Key points for project success. In Project Management Handbook, eds. D. I. Cleland and W. R. King, 06-55. New York: Van Nostrand Reinhold. Morris, P. W. G. and G. Hough. 1987. The Anatomy of Major Projects. New York: John Wiley. Moselhi, O. 1997. Risk assessment and contingency estimating. In AACE Transactions 13-16 July Conference. Dallas: AACE. Musgrave, R. A. and P. B. Musgrave. 1984. Public Finance in theory and practice. New York: McGraw-Hill. NAS. 2003. Completing the Big Dig: Managing the final stages of Boston's Central Artery/Tunnel Project. In Report to the National Academy of Sciences. Washington D.C: The National Academies Press. National Audit Office/Department of Transport. 1992. Contracting for roads. London: National Audit Office.


198

Ndekugri, I. and B. McDonnell. 1999. Differing site conditions risks: a FIDIC/engineering and construction contract comparison. Engineering, Construction and Architectural Management, 6 (2): 177-87. NEDO. 1982. Target cost contracts: A worthwhile alternative: National Economic Development Office, HMSO. Neter, J., W. Wasserman and M. Kutner. 1990. Applied Linear Statistical Models. Third Ed. ed. Homewood, Boston: Richard Irwin Inc. Newton, S. and B. S. Logan. 1988. Causation and its effect: the blackguard in CAD's clothing. Design Studies, 9 (4): 196-201. Ng, S. T. 1996. Case-based reasoning decision support for contractor prequalification. PhD Thesis, University of Manchester of Institute of Science and Technology, Manchester, England. Nijkamp, P. and B. Ubbels. 1999. How reliable are estimates of infrastructure costs: a comparative analysis? International Journal of Transport Economics, 26 (1): 23-53. Nilsen, T. and T. Aven. 2003. Models and model uncertainty in the context of risk analysis. Reliability Engineering & System Safety, 79 (3): 309-17. Niwa, K. and M. Okuma. 1982. Know-how transfer method and its application to risk management for large construction projects. IEEE Transactions on Engineering Management, 29/4 (4): 146-53. Norusis, M. J. 2005. SPSS 13.0 Statistical Procedures Companion. Chicago: SPSS, Inc. Oakes, M. 1986. Statistical Inference: A Commentary for the Social and Behavioural Sciences. Chichester, UK: Wiley. Oberlender, G., D and S. Trost, Michael. 2001. Predicting accuracy of early cost estimates based on estimate quality. Journal of Construction Engineering and Management (May/June): 173-82. OGC. 2002. Managing Successful Projects With PRINCE 2. 3rd ed. London: The Stationary Office. Ogunlana, O. 1989. Accuracy in Design Cost Estimating. PhD, Loughborough University of Technology. Ogunlana, O. 1991. Learning from experience in design cost estimating. Journal of Construction Management and Economics, 9 (1): 133-50. O'Reilly, M. 1995. Risk, construction contracts and construction disputes. Construction Law Journal, 11 (5): 343-54.

BIBLIOGRAPHY

199

Orji, H. O. 1988. Construction Financing in Nigeria: A banking perspective. Financial review, 1 (4): 12-13. Paek, J. H., Y. W. Lee and J. H. Ock. 1993. Pricing Construction Risk: Fuzzy Set Application. Journal of Construction Engineering and Management, ASCE, 117(3). Pakkala, P. 2002. Innovative Project Delivery Methods for Infrastructure - An International Perspective. Helsinki: Finnish Road Enterprise. Palaneeswaran, E. and M. Kumaraswamy. 2001. Recent advances and proposed improvements in contractor pre-qualification methodologies. Journal of Building and Environment, 36: 73-87. Palaneeswaran, E. and M. M. Kumaraswamy. 2000. Benchmarking Contractor Practices in Public-sector Construction - A Proposed Model. Journal of Engineering, Construction and Architectural Management, 7 (3): 285-99. Palaneeswaran, E., M. M. Kumaraswamy and P. W. M. Tam. 1999. Identifying best practices in contractor selection for enhanced harmony and profit. In Proceedings of CIB W92 Construction Procurement Symposium, pp. 637-47. Chiang Mai: CIB. Panagiotokopoulos, D. 1977. Cost-time model for large CPM project networks. Journal of Construction Engineering and Management, ASCE, 103 (C02): 201-11. Patrascu, A. 1988. Construction Cost Engineering Handbook. New York: Dekker. Patton, M. Q. 1986. Utilization-focused Evaluation. 2nd ed. New York.: Sage. Paul, A. and G. Gutierrez. 2005. Simple probability models for project contracting. European Journal of Operational Research, 165 (2): 329-38. Perry, J. G. and R. W. Hayes. 1985. Risk and its management in construction projects. Proceedings of the Institute of Civil Engineering, 78 ( 1): 499-521. Persad, K. R., J. T. O'Connor and K. Varghese. 1995. Forecasting engineering manpower requirements for highway preconstruction activities. Journal of Management Engineering, 11 (3): 41-7. Peurifoy, R. and G. Oberlender. 1989. Estimating Construction Costs. 4th ed. New York: McGraw-Hill Book Co. Inc. Potter, S. 1987. On the right lines? The limits of technological innovation. London.: Frances Pinter. Powell, C. G. 1980. An Economic History of the British Building History. London: Architectural Press. Project Management Institute, S. C. 2000. A Guide to the Project Management Body of Knowledge. 2nd ed. North Carolina, USA.: PMI Communications Publishing.


200

Queensland Department of Main Roads. ed. 2000. Main Roads' Project Delivery System - Major Works Pre-qualification System. Vol. Three. Brisbane: Queensland Department of Main Roads. Queensland Department of Main Roads. 2005. Roads Implementation Program 2004-05 to 2008-09. Main Roads Department Queeensland. http://www.mainroads.qld.gov.au/MRWEB/Prod/Content.nsf/1b117feddfff9cdb4a2569f10019412f/2f1dc785a18cc2ca4a256f4d00243e89!OpenDocument. Raftery, J. 1994. Risk Analysis in Project Management. London.: E & FN Spoon. Raftery, J. and S. T. Ng. 1993. Knowledge Based Approaches to Construction Risk Analysis. In International Symposium on Electronic Evaluation and the Built Environment, CIB W55 and W95, 152-65. Lisbon, Portugal. Rahman, M. M., E. Palaneeswaran and M. M. Kumaraswamy. 2001. Applying transaction costing and relational contracting principles to improved risk management and contractor selection. In Proceedings of the International Conference on Project Cost Management. Beijing. Reich, Y. 1994. Layered models of research methodologies: artificial intelligence for engineering design. Analysis and Manufacturing, 8: 263-74. Reutlinger, S. 1970. Techniques for project appraisal under uncertainty. In World Bank Staff Occasional Papers. Baltimore, Md: Johns Hopkins University. Revay, S. G. ed. 1992. Can construction claims be avoided? Edited by P. F. R. Gameson, Construction Conflict Management and Resolution. London: E & FN Spoon. RIBA. 1984. Handbook of Architectural Practice and Management. 4th ed. London: The Royal Institute of British Architects. Robinson, J. 1987. Comparison of tendering procedures and contractual arrangements. International Journal of Project Management, 5 (1): 19-24. Robinson, R. B., C. D. Cox and K. Odom. 2005. Identifying outliers in correlated water quality data. Journal of Environmental Engineering, 131 (4): 651-57 (accessed April 2005). Rosenberg, L. H., T. Hammer and A. Gallo. 1999. Continuous risk management at NASA. http://satc.gsfc.nasa.gov/support/ASM-FEB99/crm-at-nasa.html. Rowlinson, S. ed. 1999. A definition of procurement systems. Edited by S. M. Rowlinson, E. and Spon, F.N. (Eds), CIB W92 "Procurement Systems: A Guide to Best Practice in Construction". London: E and F.N. Spon,. Royer, P. S. 2000. Risk management: The undiscovered dimension of project management. Project Management Journal, 31 (1): 6-13. Rummel, R. J. 1970. Applied Factor Analysis. Evanston: Northwestern University Press.

BIBLIOGRAPHY

201

Russell, J. S., D. E. Hancher and M. J. Skibniewski. 1992. Contractor pre-qualification data for construction owners. Construction Management Economics, 10: 117-35. Russell, J. S. and M. J. Skibniewski. 1988. Decision criteria in contractor prequalification. Journal of Management in Engineering., 4: 148-64. Russell, J. S., M. J. Skibniewski and D. R. Cozier. 1990. Qualifier-2: knowledge-based system for contractor prequalification. Journal of Construction Engineering and Management, 116: 157-71. Rutgers, A. and D. H. Haley. 1997. Project risk and risk allocation. Cost Engineering, 39 (9): 27-9. Sackman, H. 1972. Delphi Critique: Expert Opinion, Forecasting and Group Process. Lexington, MA: Lexington Books. Salapatas, J. N. and W. S. Sawte. 1986. Measuring success of utility projects past, present and future. In Proceedings of the 18th Annual Seminar/symposium of the Project Management Institute. Montreal,. Schexnayder, C. and L. G. Ohrn. 1997. Highway specifications quality versus pay. Journal of Construction Engineering and Management, 123 (4,): 437-43. Seppalla, C. R. 1991. Contractor's claims under FIDC civil engineering contract. International Business Lawyer (October): 457-60. Shadbolt, R. A. 1990. Unexpected ground conditions - English Law Perspective. International Business Lawyer (June): 277-81. Shafer, G. 1976. A Mathematical Theory of Evidence. Princeton, NY: Princeton University Press. Shen, L. Y., W. lu, Q. Shen and H. Li. 2003. A computer-aided decision support system for assessing a contractor's competitiveness. Automation in Construction, 12: 577-87. Shepherd, B. 1997. Managing Risk. In The 29th Annual Project Management Institute Seminars and Symposium, 79-87. Chicago: Project Management Institute. Sidwell, A. C., R. J. Kennedy and A. P. C. Chan. 2002. Re-engineering the Construction Delivery Process. Brisbane: Construction Industry Institute, Australia. Simon, P., D. Hillson and K. Newland. 1997. PRAM-Project risk analysis and management guide. High Wycombe, UK: Association for Project Management. Skitmore, R. M. 1996. The Raftery Curve. In Proceedings: International Council for Building Research Studies and Documentation, 241-352: CIB. Slovic, P., B. Fischhoff and S. Lichenstein. 1980. Facts and fears: understanding perceived risk. In Societal Risk Assessment: How safe is enough?, eds. R. C. Schwing and W. A. Albers (eds.), 181-214. New York: Plenum Press.


202

Smith, A. J. 1995a. Estimating, Tendering and Building for Construction. London: Macmillam Press Ltd. Smith, G. R. and C. M. Bohn. 1999. Small to medium contractor contingency and assumption of risk. Journal of Construction Engineering and Management, 125 (2): 101-8. Smith, M. A. ed. 1996. Construction Insurance, Bonding, and Risk Management. Edited by W.J Palmer, J. M. Maloney and J. l. Heffron. New York: McGraw-Hill. Smith, R. J. 1995b. Risk identification and allocation: saving money by improving contracts and contracting practices. International Construction Law Review, 12 (1): 40-71. Smith(a), R. 1995. International construction review. Engineering News Record, January: 7-8. Soanes, C. and A. Stevenson. eds. 2003. Oxford Dictionary of English. Oxford: Oxford University Press. Spooner, J. E. 1974. Probabilistic estimating. American Society of Civil Engineers, Journal of Construction Division, March 1974: 65-77. Standards Australia. 1999. AS/NZS 4360: Risk Management. Strathfield NSW: Standards Association of Australia. Statman, M. and T. T. Tyebjee. 1984. The risk of investment in technological innovation. IEEE Transactions on Engineering Management, 31 (4): 165-71. Staugus, J. 1995. Variations. Journal of Building and Construction Law, 11 (3): 156-58. Steiner, G. A. 1969. Top management planning. In Project Management Handbook, eds. D. I. Cleland and W. R. King. New York: Van Nostrand Reinhold. Stevens, J. D. 1995. Cost estimating and forecasting for highway work in Kentucky: Research Report KTC 95-12. Lexington, Ky.: Kentucky Transportation Centre, University of Kentucky. Stewart, V. and A. Stewart. 1981. Business Applications of Repertory Grid. London: McGraw-Hill. Stone, E. F. 1978. Research Methods in Organisational Behaviour. Santa Monica, Ca: Goodyear Publishing Company. Summers, R. 1967. Cost estimates as predictors of actual costs: A statistical study of military developments.. In Strategy for R&D: Studies in the microeconomics of development, eds. T. Marschak, T. K. Glennan and R. Summers, 140–89. Berlin: Springer-Verlag. Sykes, A. 1982. Reducing neglected risks on giant projects. In New Dimensions of Project Management, ed. A. J. Kelley(ed). Lexington, MA: Lexington Books.

BIBLIOGRAPHY

203

Szyliowicz , J. S. and A. R. Goetz. 1995. Getting realistic about megaproject planning. The case of the Denver International Airport. Policy Sciences, 28: 347-67. Tabachnick, B. G. and L. S. Fidell. 2001. Using Multivariate Statistics, Fourth Edition. Boston: Allyn and Bacon. Tagliaventi, M. R. 1991. Civil Engineering Contracts: Organisational and Managerial Aspects and Economic Effects. Rome: Gangemi Editor. Tah, J. H. M. and V. Carr. 2001. Towards a framework for project risk knowledge management in the construction supply chain. Advances in Engineering Software, 32: 835-46. Taha, M. A., S. C. Park and J. S. Russell. 1995. Knowledge based DSS for construction contractor pre-screening. European Journal of Operational Research, 84: 35-46. Taylor, M. 1993. Raising awareness of risk. Architects Journal, May: 26. Thomas, H. R. 1985. Comparative analysis of time and schedule performance on highway construction projects involving project claims. Washington, DC: Federal Highway Administration Report No. FHWA-TS-85-217. Thompson, P. A. and J. G. Perry. 1992. Engineering construction risks: A guide to project risk analysis and risk management. SERC Project Report. London: Thomas Telford Ltd. Thompson, P. J. and S. R. Sanders. 1998. Partnering continuum. Journal of Management in Engineering ASCE, 14 (5): 73-8. Tilley, P. A., S. L. McFallen and S. N. Tucker. 2000. Design and documentation quality and its impact on the contract process. Brisbane: CSIRO. Tiong, R. L. K. 1995. Risks and guarantees in BOT tender. Journal of Construction Engineering and Management, 121. (2): 183-88. Toakley, A. R. 1995. Risk management applications - a review. Australia Institute of Building Papers, 6: 77-85. Topcu, Y. I. 2004. A decision model proposal for construction contractor selection in Turkey. Journal of Building and Environment, 39: 469-81. Touran, A. 2003. Probabilistic model for cost contingency. Journal of Construction Engineering and Management :ASCE, May/June, 2003: 280-84. Tran, V. H. Q. 2002. Practical frontier in construction pre-qualification using data envelope analysis. MASc. thesis, Department of Civil Engineering, University of Toronto, Toronto. Trost, S., Michael and G. Oberlender, D. 2003. Predicting accuracy of early cost estimates using factor analysis and multivariate regression. Journal of Construction and Engineering Management, March/April: 198-204.


204

Tummala, V. M. R. and Y. H. Leung. 1999. Applying a risk management process (RMP) to manage cost risk for an EHV transmission line project. International Journal of Project Management, 17 (4): 223-35. Tuohey, P. 2002. The use of risk management processes in construction contracts. In The Contract in Successful Project Management Conference. Christchurch, NZ: Worley Group. Turner, J. R. 1999. The Handbook of Project-based Management. New York: McGraw-Hill. Tversky, A. and D. Kahneman. 1981. The framing of decisions and the psychology of choice. Science, 211: 453-58. Uff, J. and A. Odams. 1995. Contract Documents and the Division of Risk. In Risk Management and Procurement Practices, eds. J. Uff and A. Odams. Kings College, London: Centre for Construction Law and Management. Uher, T. 1994. A general risk classification model for construction. Australian Institute of Building Papers, 5: 25-33. Under water, o. b. 1989. Under water, over budget. The Economist, October: 37-8. Velicer, W. F. and D. N. Jackson. 1990. Component analysis versus common factor analysis: Some issues in selecting an appropriate procedure. Multivariate Behavioral Research, 25 (1): 1-28. Vickrey, W. 1961. Counter-speculation, Auctions, and Competitive Sealed Tenders. Journal of Finance, 16: 8-37. Victorian-Government. 1999. Private Provision of Public Infrastructure: Risk Identification and Allocation Project: Victorian Government, Department of Treasury and Finance; University of Melbourne, Department of Civil and Environmental Engineering. Vojir, C.P., K.R. Jones, R. Fink. And E. Hutt. 2006. Scientific Enquiry: What a difference a point makes. Journal of Specialists in Pediatratic Nursing, 11 (4) 257-59. Walker, D. H. T., K. Hampson and R. Peters. 2000. Project alliancing and project partnering - what's the difference? - Partner selection on the Australian National Museum project: a case study. In Proceedings of the CIB W92 Conference, 641-55. Chile. Walker, D. H. T. and K. D. Hampson. 2002. Procurement Strategies. A Relationship Based Approach. Oxford: Blackwell Publishing. Wallace, I. N. D. 1995. Hudson's Building and Engineering Contracts. 11th edition ed. London: Sweet & Maxwell. Wang, J. X. and M. L. Roush. 2000. What Every Engineer Should Know About Risk Engineering and Risk Management. New York, NY.: Marcel Dekker. Wang, M.-T. and H.-Y. Chou. 2003. Risk allocation and risk handling of highway projects in Taiwan. Journal of Management in Engineering, 19 (2): 60-8.

BIBLIOGRAPHY

205

Wang, W.-C. 2003. SIM-UTILITY: Model for project ceiling price determination. Journal of Construction and Engineering Management, 1218 (1): 76-84. Ward, S. 1999. Requirements for an Effective Project Risk Management Process. Project management Journal, 30(3). Ward, S. and C. Chapman. 2003. Transforming project risk management into project uncertainty management. International Journal of Project Management, 21 (2): 97-105. Weverbergh, M. 2002. A comment of 'predicting the probability of winning sealed bid auctions: a comparison of models'. Journal of the Operational Research Society, 45 (11). White, D. and J. Fortune. 2002. Current practice in project management: An imperial study. International Journal of Project Management, 25 (4). Wilcox, R. C., Z. I. Karaszewski and B. M. Ayyub. 1996. Methodology for Risk-based Technology Applications to Marine System Safety. In Proceedings of the Ship Structures Symposium, 1-15. Arlington: SSC. Williams(a), T. M. 1993. Risk management infrastructures. International Journal of Project Management, 11 (1): 5-10. Williams(b), T. M. 1993. Using the risk register to integrate risk management in project definition. International Journal of Project Management, 12: 17-22. Williams, T. M. 1995. A classified bibliography of recent research relating to project risk management. European Journal of Operational Research., 85 (1): 18-38. Williams, T. M. 1999. The need for new paradigms for complex projects. International Journal of Project Management, 17 (5): 269-73. Williams, T. M., F. R. Akermann and C. L. Eden. eds. 1997. Project risk: systemicity, cause mapping and a scenario approach. Edited by K. Kahkonen and K. A. Arto, Managing Risks in Projects. London: E & F.N. Spoon. Williams, T. P. 2003. Predicting final cost for competitively bid construction projects using regression models. International Journal of Project Management, 21: 593-99. Williams, T. P., J. C. Miles and C. J. Moore. 1999. Predicted cost escalations in competitively bid highway projects. Proceedings of the Institution of Civil Engineers-Transport, 135 (4): 195-99. Wilmot, C. G. and C. Cheng. 2003. Estimating future highway construction costs. Journal of Construction Engineering and Management, May/June: 272-79 (accessed May/June 2003). Winch, G. 1989. The construction firm and the construction project: a transaction cost approach. Construction Management and Economics,. 7: 331-45.


206

Woodward, J. F. 1975. Quantitive Methods in Construction Management and Design. London: Macmillan. World Bank. 1994. World Development Report 1994: Infrastructure for development. Oxford, UK: Oxford University Press. Yeo, K. T. 1990. Risks, classification of estimates, and contingency management. Journal of Management in Engineering,, 6 (4): 458-70. Yin, R. K. 2003. Case Study Research: Design and Methods, 3rd Ed. 3rd ed. ed. Thousand Oaks, Calif.: Sage Publications. Young, T. L. 1996. The Handbook of Project Management: A Practical Guide to Effective Policies and Procedures. London: Kogan Page. Zack, J. G. J. 1996. Claims prevention: offense versus defense. AACE International. Transactions of the Annual Meeting. http://www.sciencedirect.com/science/article/B6WPV-3THVNX0-3FD/2/0532d489e2d4228267f87857cda1881d. Zaghloul, R. and F. Hartman. 2003. Construction contracts: the cost of mistrust. International Journal of Project Management, 21: 419-24. Zeitoun, A. A. and D. O. Oberlender. eds. 1993. Early Warning Signs of Project Changes: Document 91. Edited by C. I. Institute. Austin, Texas: Construction Industry Institute.

APPENDIX A

207

APPENDIX A

Road Input Cost Indices for the period 1995-96 to 2002-03

Compiled from Australian Bureau of Statistics data


208

Month Year RICI RICI Percent

September 1984 61.21 0 December 1984 61.38 0.28 March 1985 61.70 0.52 June 1985 63.20 2.43

Financial year average 61.78 Datum September 1985 63.66 0.73 December 1985 65.63 3.09 March 1986 66.00 0.56 June 1986 66.41 0.62

Financial year average 65.15 5.45 September 1986 68.06 2.48 December 1986 68.64 0.85 March 1987 70.55 2.78 June 1987 70.92 0.52




Financial year average 84.30 6.78 September 1990 87.74 0.94 December 1990 88.21 0.54 March 1991 88.85 0.73

APPENDIX A

209

June 1991 89.30 0.51 Financial year average 88.33 4.78

September 1991 90.64 1.50 December 1991 91.12 0.53 March 1992 91.37 0.27 June 1992 92.06 0.76








210

400.00 Financial year average 100.00 1.66

September 1998 101.50 0.79 December 1998 101.98 0.47 March 1999 102.41 0.42 June 1999 102.69 0.27 408.58

Financial year average 102.15 2.15 September 1999 103.50 0.79 December 1999 104.05 0.53 March 2000 104.56 0.49 June 2000 105.05 0.47 417.16





Financial year average 116.57 2.95

Source: Australian Bureau of Statistics

APPENDIX B

211

APPENDIX B

Programmed and actual project costs indexed to year 2003


212

Project number Year project

completed Indexed prog cost

$m Indexed actual cost

$m 12/46C/803 1996 2.152 2.849 107/852/804 1996 1.396 1.997 80/140/13 1996 2.908 4.380 160/11A/2 1996 7.443 10.395 25/126/22 1996 1.251 1.605 100/851/26 1996 5.338 6.891 24/162/13 1996 2.239 2.744

122/31A/804 1996 2.326 2.873 30/8202/803 1996 2.291 2.777 146/166/15 1996 1.215 1.700

113/35A/807 1996 1.977 2.710 94/36b/19 1996 2.093 2.353 128/48/15 1996 3.256 3.838 66/814/5 1997 2.059 3.337

140/U16/819 & 820 1997 1.803 3.131 158/10P/811 1997 2.300 2.574 22/18E/807 1997 1.854 2.293 25/10A/802 1997 3.565 4.879 130/45B/14 1997 1.550 1.880 5/10K/19 1997 1.495 1.691

150/10L/38 & 39 1997 9.051 10.567 160/103/2 1997 5.290 7.418 160/12B/5 1997 42.550 47.909

24/10C/22 & 64/10C/3 1997 1.351 1.692 25/126/23 1997 1.283 2.214 158/20A/2 1997 2.990 4.156 70/25A/11 1997 22.749 29.937 92/133/11 1997 2.415 2.887 45/655/18 1997 1.898 3.908 158/647/15 1997 3.283 4.016 37/90B/43 1997 2.482 2.828

30/8202/303 1997 2.645 3.148 148/17B/20 1997 2.530 3.146 25/126/24 1997 1.196 1.680

S140/U12B/28 1997 1.021 2.933 140/U18B/61 1997 1.567 1.796 134/14C/301 1997 4.165 6.138 23/78A/17 1998 16.980 18.678 70/108/300 1998 1.301 2.177

36/15A/301 &302 1998 6.905 7.647 25/1204/301 1998 2.151 2.390 89/41A/17 1998 2.830 3.113 24/1632/12 1998 1.366 1.519 40/22A/28 1998 3.034 3.588

150/10L/38 & 39;150/10M/26

1998 8.857 11.030

160/102/3 1998 4.528 6.012

APPENDIX B

213

160/103/6 1998 15.848 18.112 160/203/3 1998 1.358 2.897 48/641/301 1998 3.283 3.736 70/204/20 1998 7.083 8.420

140/U15/44 1998 2.297 4.098 140/U96/738 1998 1.193 1.735

92/140/11 1998 1.698 2.691 76/132/19 1998 5.660 6.674 45/655/18 1998 4.356 5.284 30/6204/13 1998 2.264 2.558 36/89A/303 1998 1.456 1.796 83/182/18 1998 4.811 5.637 97/403/32 1998 1.613 2.206 160/104/4 1998 1.132 1.288

140/U18B/61 1998 1.872 2.142 125/88B/40 1998 1.878 2.395 148/2104/8 1998 3.076 3.639 88/23C/20 1998 2.377 2.728 148/3042/2 1999 1.808 3.265 20/85C/32 1999 4.436 5.196 80/133/734 1999 3.538 4.055 17/18D/301 1999 7.763 9.308 104/416/301 1999 2.955 3.432 35/22B/802 1999 2.229 2.963

80/497/301&76/497/801 1999 4.347 5.232 150/832/2 1999 8.611 9.713 160/102/2 1999 5.989 6.579 148/18A/2 1999 11.090 16.635 140/U88/9 1999 7.228 7.981

160/12A/8.566 1999 46.024 57.668 148/17B/301 1999 1.664 2.239 140/904/301 1999 1.792 2.197

76/153/3 1999 2.551 5.362 34/110/18 1999 1.557 1.800

140/U18B/63 1999 2.995 3.793 37/91A/20 1999 4.731 10.092 42/83A/21 1999 2.551 2.950 25/493/3 1999 2.218 2.867

83/1821/18 1999 5.578 7.332 19/93D/17 1999 2.218 3.124 37/90C/36 1999 1.774 3.075

95/27B/302 1999 1.222 1.363 36/93E/706 1999 1.170 1.342 72/44A/303 1999 2.551 2.994 13/25B/301 1999 1.331 1.641 22/4397/14 1999 3.771 4.464 160/203/3 1999 2.838 3.438

150/10M/301 1999 4.225 4.701 114/313/12 1999 1.664 2.287

140/U18A/14 1999 2.102 2.734


214

41/92A/27 1999 3.848 4.602 125/88B/42 1999 2.556 2.971 55/99C/15 1999 5.046 5.719 60/176/15 1999 1.673 2.237

134/14C/305 1999 1.885 2.131 64/19B/301 1999 2.218 2.693 22/4397/15 1999 3.327 4.158 140/902/2 2000 16.412 18.469

47/16B/302 2000 2.415 2.726 80/10A/772 2000 4.344 5.647 141/19A/806 2000 2.389 3.334 148/3042/2 2000 2.830 3.844

148/17B/302 2000 3.475 3.855 148/18A/708 2000 1.685 2.048 20/10G/301 2000 5.321 6.081 128/10B/808 2000 2.672 3.898 80/496/301 2000 2.818 3.606 48/6404/3 2000 8.688 14.016 160/11A/3 2000 4.997 5.713 160/103/3 2000 11.947 13.603 34/110/16 2000 6.065 7.140

160/12A/8.565 2000 28.225 34.968 140/U13A/729 2000 1.195 1.617

140/900/2 2000 15.542 17.215 148/3042/300 2000 2.323 2.896

37/91A/17 2000 8.362 9.519 66/626/4 2000 1.140 1.553

107/8509/8 2000 1.200 1.856 13/202/16 2000 1.738 2.020 97/122/801 2000 2.744 3.204

140/U90/301 2000 3.367 4.375 37/91A/32 2000 2.715 3.527 25/493/4 2000 3.329 4.154

8/26A/302 2000 2.606 2.902 89/41A/18 2000 2.172 2.715 97/403/27 2000 4.980 5.741

160/11B/313 2000 1.195 1.529 72/41A/9 2000 1.086 1.444

150/831/801 2000 1.412 1.596 80/495/897 2000 1.195 2.340

134/14C/302 2000 5.104 6.506 114/18A/764 2001 1.455 1.711 80/10A/772 2001 5.448 6.053

120/8506/301 2001 2.760 3.210 47/16B/302 2001 2.737 3.028 148/302/2 2001 1.228 1.435

22/18E/807 2001 2.118 2.974 90/33B/302 2001 2.549 2.971 128/143/301 2001 1.646 1.874 47/46C/306 2001 4.237 5.092

APPENDIX B

215

48/21A802 2001 1.134 1.867 148/18A/2 2001 14.682 16.409 36/15A/13 2001 6.075 7.668 160/103/4 2001 10.636 13.710 120/854/6 2001 1.805 2.091

148/17B/21 2001 5.522 6.397 140/U88/3 2001 47.790 56.403

160/12A/8.57-59 2001 29.948 38.232 140/U16/301 2001 7.434 11.476 148/17A/49 2001 1.629 1.888 34/112/38 2001 2.124 2.634 120/854/3 2001 1.373 1.635 44/93D/8 2001 2.156 2.851 55/99C/13 2001 2.055 2.322

66/8108/302 2001 1.417 2.091 50/27A/305 2001 1.109 1.286 30/6204/14 2001 1.900 2.393 76/490/3 2001 2.438 3.108

5/10K/809 2001 1.299 2.491 140/U12B/727 2001 1.087 1.406

125/88B/42 2001 4.086 5.127 59/32C/25 2001 2.177 2.670 55/99C/13 2001 5.477 7.716

20/85C/302 2001 2.177 2.628 79/14D/304 2001 2.018 3.063 161/46A/17 2001 2.784 3.076 128/141/18 2001 2.124 2.713 133/89B/49 2001 1.701 2.243

80/151/2 2002 17.578 19.749 140/12A/301 2002 1.499 1.872 148/17B/805 2002 2.895 3.259 140/U91/813 2002 1.241 1.448 33/18C/301 2002 1.654 1.865 70/25A/13 2002 32.713 41.123 120/856/3 2002 3.810 6.307 5/10K/22 2002 1.143 1.448 10/15C/2 2002 15.675 20.404 13/202/18 2002 2.569 2.953 9/16D/302 2002 2.016 2.224 99/332/301 2002 1.241 1.448 58/214/301 2002 1.034 1.286 27/46A/16 2002 1.068 1.391

160/12B/802 2002 1.034 1.633 99/323/13 2002 3.743 4.262 76/493/7 2002 1.034 1.564

73/45A/20 2002 1.241 1.453 140/U18A/14 2002 2.543 2.818 149/18A/23 2002 2.792 3.629 160/101/302 2002 1.034 1.344 150/10L/55 2003 35.4 46.890


216

37/91A/26 2003 6.755 7.950 80/151/2 2003 19.1 22.500

30/10N/88 2003 1.12 1.340 25/40A/812 2003 1.275 1.590 128/43/304 2003 1.25 1.800 97/407/11 2003 1.483 2.301 160/105/5 2003 9.049 11.300 70/25A/13 2003 40.424 46.350 160/204/2 2003 18.59 21.098

158/10P/17 2003 5.82 7.320 160/103/4 2003 12.193 14.850

140/U15/721 2003 3.227 4.013 13/25A/48 2003 8.649 9.835 160/200/12 2003 1.898 2.186 25/40A/35 2003 2.5 2.862 37/91A/36 2003 7.021 8.055 116/26B/31 2003 1.498 1.661 128/483/303 2003 1.7 2.200 134/14D/302 2003 2.25 2.900

99/332/10 2003 1 1.246 150/548/301 2003 1.082 1.365 63/17D/302 2003 1 1.127 36/14E/301 2003 2.1 2.682 25/401/302 2003 2.8 3.600

125/88A/806 2003 1.3 1.550 34/1102/17 2003 2 3.209 119/32B/27 2003 1.018 1.273

APPENDIX C

217

APPENDIX C

Questionnaire format to establish NGT membership


218

Questionnaire:

Date of Completion: …………………..

Dear interviewee, The aim of this questionnaire is to elicit expert information on the process of preparing total project cost estimates at the time of design, ideally in road construction. For the purpose of this questionnaire and research, the total project cost estimate includes the estimated costs of all component activities from the initiation of the project proposal to finalisation. This total project cost estimate is usually prepared at the design stage of a project and can often include the costs of the following:

• conducting investigations and developing the design,

• detailing the design, • acquiring land,

• altering public utility plant, • construction, • project administration, and handover

The information supplied in this completed questionnaire will be used for broad research purposes only. All specific company and interviewee information will be kept confidential at all times. Only generalised analysis of the information contained within this completed questionnaire will be utilised in the research process.

Regards, Garry Creedy

7 December 2004

Section 1: Company Information 1.1 Company Name (optional): 1.2 Volume of Construction Work/year in A$ 1.3 Predominant activity for company: Project Owner/Client General Contractor Consultant

Other 1.4 Describe types of projects predominantly involved in: Section 2: Information about Interviewee 2.1 Name (optional) 2.2 Phone contact/email (optional) 2.3 Related Qualifications 2.4 Years employed in company 2.5 Related experience:

Estimating (years) Design (years) Construction (years)

Management (years)

APPENDIX C

219

(This section refers to the whole of the project estimate … not just the construction estimate.) Section 3: Total Project Estimating 3.1 In your opinion, what are the four most important factors/issues in project estimating? 3.2 In your experience, what role does value engineering and/or value management play in total

project cost estimating and also in design? 3.3 What are the estimating methods most often used in your organisation for total project

estimating at the design stage? e.g. parametric factored basic cost unit rate

Explain. 3.4 Do you use a computerised system for project design estimating?

Also, describe in brief any off-the-shelf software utilised.. 3.5 Describe the typical project scope definition information available to you for project design

estimating. 3.6 What approach do you use for contingency analysis in project design estimates? Probability based

Historical values Decision analysis Experience based

Other (explain) 3.7 List items at the project level that you normally include as contingencies?

e.g. - Design allowances - Force majeure - -

3.8 List the main factors that you take into account for contingency analysis and

evaluation ranked according to their importance. e.g. Nature of project

Level of design completeness


220

3.9 In your experience contingency is usually allocated as: Per item basis

Associated with main project components Bottom line of project estimate amount Other---define.

3.10 Please list four of the most frequent caused errors, mistakes, omissions in project design

estimating. 3.11 What are four main problems or constraints encountered in project design estimates? 3.12 What is your percent confidence in the expected accuracy level of project design estimates

for the following? Roads Bridges Miscellaneous (road related) 3.13 Do you have any comments on how to improve total project estimates and how to achieve

this? 3.14 What is your ideal profile of an expert project design estimator?

(background, experience, skills, training, etc) Section 4: Project Delivery 4.1 In your experience, what percentage of projects is usually delivered as: Design-Bid-Build Design and Build Other (specify) 4.2 What percentage of projects is usually delivered as: Schedule of Rates Contracts Lump Sum Contracts

Alliance Other (specify)

APPENDIX C

221

Section 5: Other: 5.1 Would you like to participate in a personal interview to expand your answers or provide

more nformation about total project design estimating?

Yes No 5.2 Any other comments you wish to provide: Please return this completed questionnaire to:

Garry Creedy

Queensland University of Technology School of Construction Management and Property,

Level 4, L Block, Gardens Point Campus GPO Box 2434, Brisbane 4001.

or Fax 38641170

Version 1.0 dated 7/12/2004


222

APPENDIX D

223

APPENDIX D

Completed Questionnaire details of NGT membership


224

Questionnaire:

Date of Completion: 14/12/04………………….. Dear interviewer, The aim of this questionnaire is to elicit expert information on the process of preparing total project cost estimates at the time of design, ideally in road construction. For the purpose of this questionnaire and research, the total project cost estimate includes the estimated costs of all component activities from the initiation of the project proposal to finalisation. This total project cost estimate is usually prepared at the design stage of a project and can often include the costs of the following:

• conducting investigations and developing the design • detailing the design • acquiring land • altering public utility plant • construction • project administration, and handover

The information supplied in this completed questionnaire will be used for broad research purposes only. All specific company and interviewee information will be kept confidential at all times. Only generalised analysis of the information contained within this completed questionnaire will be utilised in the research process.

Regards,

Garry Creedy 7 December 2004

APPENDIX D

225

Section 1: Company Information 1.1 Company Name (optional):

Department of Main Roads, Queensland 1.2 Volume of Construction Work/year in A$ 500+m. 1.3 Predominant activity for company: Project Owner/Client 50% General Contractor 50% Consultant

Other 1.4 Describe types of projects predominantly involved in: Road and Bridge Construction Road Rehabilitation and Maintenance Section 2: Information about Interviewee 2.1 Name (optional)

1. 2. 3. 4. 5. 6. 7.

2.2 Phone contact/email (optional) 2.3 Related Qualifications

1. Associate Diploma Civil Engineering, Diploma in Business Admin. 2. Bachelor of Engineering. PhD 3. Bachelor of Engineering (Civil). Diploma in Business Administration 4. Bachelor of Engineering (Civil). Bachelor of Economics. 5. Bachelor of Engineering (Civil). 6. Bachelor of Engineering (Honours). Master Engineering Science 7. Associate Diploma Civil Engineering. Master in Business Admin.

2.4 Years employed in company 1. 41 2. 35 3. 32 4. 39 5. 14 6. 17 7. 40

2.5 Related experience:

Estimating (years) 2. 10 3. 10 4. * embodied in construction and maintenance 5. 14 (Continuous, ongoing role) 7. 5 Design (years) 2. 17 3. 10 4. * embodied in construction and maintenance 5. 1 6. 12 7. 5


226

Construction (years) 1. 10 2. 3 3. 10 4. 10* 6. 2 7. 15

Management (years) 2. 16

3. 15 4. 29* 5. 6 6. 3 7. 15

(This section refers to the whole of the project estimate … not just the construction estimate.) Section 3: Total Project Estimating 3.1 In your opinion, what are the four most important factors/issues in project estimating?

2. Detailed geotechnical investigation. Competent design. Detailed quantity calculations. database for tender bids on similar projects over >5 years.

3. Knowing and understanding project scope. Historical costs. Identification and management of risks.

4. Knowledge of construction processes/techniques/materials etc. Understanding the real needs of the project. Clients’ expectations. Risk assessment.

5. Knowing the scope of the project. Suitable allowances for risks and contingencies. Breaking the project down into significant detail to estimate cost of works; i.e. not using $/km. Using correct items and quantities in breakdown.

6. Understanding the design and specifications, and knowing what work the designers intend to be included in each of the items in the schedule. Having sufficient contingency where the nature of the work is not well understood. Experience not only in estimating but in all phases of the project eg. planning, design, and construction. Local knowledge of materials, conditions, prices.

7. Completeness of scope. Ensuring estimate is in out-turn $’s. Adequate time allowed for estimating costs. Experienced estimators.

3.2 In your experience, what role does value engineering and/or value management play in total

project cost estimating and also in design? 2. Clarify scope. Set design performance standards.

3. Very important. 4. Identifying likely issues/problems and bringing together a broadly based

view on likely solutions; i.e. facilitating an understanding across the supply chain.

5. Useful in ensuring scope is adequately understood and defined. 7. Allows completeness of adequate design and completeness of scope.

3.3 What are the estimating methods most often used in your organisation for total project estimating at the design stage?

e.g. parametric 7. factored

APPENDIX D

227

basic cost 3. (For Commercial use) 5. unit rate 2. 3. (For Corporate use) 4. 5. 7.

Explain. 4. Contracts are basically schedule of rates type. Separation of the

organisation into purchaser/provider has resulted in less ability of the client to do basic cost estimating.

5. Mainly unit rate. Some major items are calculated using basic cost. Many staff have insufficient experience to effectively use basic cost estimates.

7. Bridges – initial estimate using parametric method. design estimate for road and bridge projects use unit rate and bill of quantities.

3.4 Do you use a computerised system for project design estimating?

2. Standard schedules. Quantities calculations. Databases for tender prices.

3. No 5. Use an in-house system for assembling the schedule. The program will

accept unit rate but has no capability to assist with basic cost estimates. 7. No

Also, describe in brief any off-the-shelf software utilised. 5. RoadTek use Expert Estimator

7. P3 used on larger projects for schedule, etc. 3.5 Describe the typical project scope definition information available to you for project design

estimating. 2. Bridge fixings – width, length, height of bridge. 3. Extent of physical project scope. Survey data. Planning layouts.

4. DMR is introducing a formalised project management approach with a tight scope definition profile.

5. Extremely variable. Often fairly limited at concept phase, usually fairly well defined by detailed design.

7. Services. resumption requirements. Planning studies. Native title clearances. Consultation outcomes.

3.6 What approach do you use for contingency analysis in project design estimates? Probability based 4.

Historical values 2., 7. Decision analysis 3. Experience based 2., 3. Other (explain) 5. Risk identification. Risk register. Assessment. Management plan. Re-assessment. Likelihood. Potential cost. Contingency pool.

3.7 List items at the project level that you normally include as contingencies?

e.g. - Design allowances - Force majeure 1. Resumptions. Services Relocation. Administration Costs.


228

2. Remote project location. Non-competitive bids. Variation in founding levels for substructures. Varition in materials, eg excavation in piles. Flood delays – effects on falsework, cofferdams. Variation in materials cost/supply shortages. Industry capacity issues on major projects. Effects of major projects on other (resource competing) minor projects

3. Public utility conflicts. Adverse weather. Unsuitable material. Community requests for scope change.

5. Needs to be project specific and be reviewed and updated throughout the project life-cycle.

7. Management reserve for scope increase. Rise and fall costs for contract process. Contract variations. Services relocation overrun. Extension of time (Project management costs increase). resumption accommodation works additions to severance properties.

3.8 List the main factors that you take into account for contingency analysis and evaluation ranked according to their importance.

e.g. Nature of project Level of design completeness 2. Nature of the project

3. Impact of risk to project scope, duration, cost (in $ terms, not as a percentage). Probability of being able to mitigate the risks.

5. Identified risks are rated according to: a) the likelihood of the risk occurring, b) the effect to the project id the event (risk) occurs. This gives us a ranking. Risk management is considered for all extreme, high and medium risks.

7. Type of delivery contract. Constructability. 3.9 In your experience contingency is usually allocated as: Per item basis 3.

Associated with main project components 2. Mainly Bottom line of project estimate amount 2. Sometimes. 7. Other---define. 4. There is the question of what you mean by contingency –v-

allowance for risk. i.e. you can build in some risk allowance on an item. You can also add a contingency allowance to your final figure.

5. Combination of per item and main project components. Some risks are associated with a single item. Others affect a number of items whilst others are not directly related to any items in the schedule.

3.10 Please list four of the most frequent caused errors, mistakes, omissions in project design

estimating. 1. Amount of unsuitable material. Pipe length specified. Concrete quantities. No

allocation for rideability bonus or rise in the cost of bitumen. 2. Errors in quantities. Misinterpretation of borelogs/foundation reports. Lack of

data on similar projects eg remote location projects. Lack of market suppliers – one bidder, so no competition.

3. Overlooking whole items of cost eg. resumptions, public utilities, removal of existing bridge of pavement after new installation. poor and conflicting scheme documentation (plans-V-specifications). Scope increase. Using inappropriate historical data.

4. Quantity errors. Constructability issues. inconsistent specifications. 5. Final scope of project is not what was originally estimated. Very imprecise

“global estimating” used at concept phase and this determines project budget. Inadequate or no contingency allowance. Higher than expected cost escalation.

APPENDIX D

229

6. Use of historical rates not appropriate to the scale of the works. Inexperience. Time pressure to “fast track” projects can lead to design errors.

7. Quantity errors. Inexperienced estimators. Poor project scope definition. Plan and document errors in contracts.

3.11 What are four main problems or constraints encountered in project design estimates?

2. Estimates made several years ago and not updated. Rapidly changing market prices when construction program increases rapidly.

3. Lack of input from construction experts. Pressure of time. Misuse of “expert” systems.

4. Quantity errors. Constructability issues. inconsistent specifications. 5. Political pressure to produce estimates in unrealistic short timeframes. Political

influences and lobby groups resulting in change in project scope. 6. Insufficient geotechnical investigation/lack of understanding of ground

conditions. Lump sum items for traffic management and environmental management – different understanding of what is required. Need to break these down into sub-items. In a “hot” market, prices can rise rapidly due to demand exceeding supply. Design estimate may be out of date by the time the job is tendered.

7. Time allowed. Contracting climate at time of tender –v- time of estimate. Time delay for approvals to project elements. Inexperienced estimators in road and bridge projects.

3.12 What is your percent confidence in the expected accuracy level of project design estimates

for the following? Roads 1. 75% 3. Within +20% and -5%. 4. Within +- 10% for design estimates 5. +- 10% at detailed design.

7. 85% Bridges 1. 90% 2. Coefficient of variation 10% on standard projects. 3. Within +15% and -5%. 4. Within +- 10% for design estimates 5. +- 10% at detailed design

7. 95% Miscellaneous (road related) 3. within +30% and -5%.

7. 85% 3.13 Do you have any comments on how to improve total project estimates and how to achieve

this? 1. Use a quantity surveyor who specialises in Road/Bridge estimates. 2. Adequate geotechnical info. Competent design. Accurate schedule of quantities. 3. Knowing and understanding project scope. Historical costs. Identification and

management of risks. 4. Better risk assessment. Better documentation i.e. plans, specifications, etc. Use

of basic cost estimating by client. Adequate time allowed for design. Clear scope definitions.

5. Ensure project scope is adequately defined. Allow sufficient time and resources to estimate the cost of the project.


230

7. Value engineering. Peer review of designs and of estimates. Freeze scope of project at time of tender. Reduce provisional quantities in bill of quantities schedule.

3.14 What is your ideal profile of an expert project design estimator?

(background, experience, skills, training, etc) 2. To have done both design and construction supervision of many similar projects

over 15+ years. Has access to up-to-date tender data. 3. It is a job for a team, including experience in design, construction, relationships

with suppliers, contractors. (Relying on one will end up in errors). 4. Knowledge and experience in construction. Understanding of contracts and

contractual processes. A person with a good level of attention to detail. 5. Project engineers are technicians with a number of years of construction. 6. Someone who has extensive estimating experience but who has also had some

experience in the other phases of the project eg. involvement in design activities, use of specification, on site supervising construction. Someone who has estimated for a range of different project sizes. Someone who has local knowledge including local prices, availability of materials, local conditions.

7. Civil design background. Site draftsman experience. Good time management skill. Good database management skills. Trained in and uses project management software for probability scheduling and estimating.

Section 4: Project Delivery 4.1 In your experience, what percentage of projects is usually delivered as: Design-Bid-Build 2. 95%

3. 90% 4. 95% 5. 98% 7. 97%

Design and Build 1. One design-construct-maintain project in metropolitan area south

Brisbane. One Design and construct in metropolitan area of north Brisbane.

2. A few major projects. 3. 8% 4. 5% 5. 2% 7. 2%

Other (specify) 3. Alliance 2%

7. 1% 4.2 What percentage of projects is usually delivered as: Schedule of Rates Contracts

1. 100% in Metropolitan Brisbane 2. 95% 3. 98% 4. 98% 7. 99%

Lump Sum Contracts 3. Nil

4. 1% 7. 1%

APPENDIX D

231

Alliance 2. 5%

3. 2% 4. 1% 5. 5% 7. 1% Other (specify)

Section 5: Other: 5.1 Would you like to participate in a personal interview to expand your answers or provide

more information about total project design estimating?

Yes No 5.2 Any other comments you wish to provide:

2. The issue in MRD appears to revolve around our inability to develop a program over several years, and accept that initial project concept estimates can vary widely from final project cost, and that the estimate should be refined through the following stages: Concept Estimate; Preliminary Design Estimate; Final Design & Documentation Estimate; Contractors’ Tender Price and Contingencies Estimate. Being realistic about the project development and cost development phases, and adjusting the RIP to fit, would solve many problems.

4. I think you need to send the questionnaire to hands on people. I am too far and

too long removed from the detail to effectively answer the questions accurately. Please return this completed questionnaire to: Garry Creedy

Queensland University of Technology School of Construction Management and Property,

Level 4, L Block, Gardens Point Campus GPO Box 2434, 4001.

Fax 38641170 Version 1.1 compiled on 15/04/2005 to incorporate seven detailed responses.


232

APPENDIX E

233

APPENDIX E

Client risks in highway project delivery


234

The following client project risks which have prevailed in historical highway projects and caused significant cost overruns. Detailed descriptions of those risks are included here so that project estimators and project managers can be made more aware of their potential to impact costs in highway project budgets. A Project acceleration requirement This is a requirement imposed on the project by the client for the contractor to vary their working schedule. This can be imposed for a number of reasons. The most pressing would be because of severe disruption to the motoring public either because of ongoing, severe traffic congestion during the construction not identified pre-contract stage, or because of the sudden loss of a facility such as a bridge washout or landslide that necessitated accelerated effort by way of longer working hours or even round-the-clock effort. In certain agreed cases, the costs associated with such acceleration direction would be recovered from the client by the contractor. C Constructability difficulty costs This project cost can occur where the tender price is based on an approved construction process or sequence and this has to be varied by the client for certain reasons. The most prevalent cause of this cost can be that the foundation design criterion was not achievable due to unforseen site conditions. Also, constructability difficulty costs can be incurred in a project where certain traffic arrangements are required to be maintained. This can then severely restrict the economy of scale of certain construction processes, such as paving runs as well as concrete placement in drainage and bridge structures. Poorly coordinated designs, plans and specifications can lead to contractors’ claims during construction because of conflicting project information that, when clarified, causes the contractor to perform differently than was planned at the time of preparing the bid. When multiple contractors are planned to be working simultaneously on a project site, the designer should develop a site utilisation plan that will define the area(s) each contractor has jurisdiction over and how to accommodate the other contractor(s). If this has not been done properly, there will be contractor claims usually based on delays that will result in costs to the owner as well as potential time delays in achieving project completion. At the project estimating stage, studies are often required for significant items of work to determine the feasibility and efficiency of alternative production methods. In estimating the construction component of a project for example, the project manager may need to examine the mass haul diagram for earthworks to evaluate haul distances, borrow and spoil requirements and the most effective construction fleet for the particular site conditions. Similarly, for major road projects in high traffic areas, it may be necessary to develop traffic management and construction-staging plans in order to evaluate traffic management requirements. CT Constructability under traffic Increases in project costs resulting from carrying out certain road construction processes under flowing traffic conditions can be substantial in highly trafficked areas. While generally all contracts for projects in such situations are specific in detailing the construction risks of

APPENDIX E

235

process continuity and efficiency, situations can arise in contracts where the contractor’s obligations are compromised by client instructions and directions that require the contractor to incur costs other than provided for in the contract provisions. Limiting a contractor’s work schedule to out-of-peak traffic times as a result of unforseen peak hour traffic congestion is one example where lower daily productivity and additional project costs might need to be borne by the client. In urban areas, even say a traffic problem occurring on a nearby (arterial) road can force traffic through construction projects which normally would not have attracted higher flows, resulting in subsequent traffic congestion and safety issues through the project site. Side tracking of traffic is some urban and rural projects is also a safe and efficient means of managing traffic during construction. Again, there are times when contingent emergencies or processes can occur that will disrupt this orderly traffic management strategy that results in construction processes having to be carried out under flowing traffic conditions. This may be outside the control of the contractor and then could be deemed the cost responsibility of the client. D Design/project scope change This is a general category into which all non-defined project scope changes that increase the cost are allocated. They exclude the specific areas of: drainage, environment, design error, pavement materials and pavement depth, earthworks preload, changes to pavement subgrade and changes as a result of physical site safety audits. This variation category includes such design scope changes as vertical and horizontal alignment changes that can result in changes in quantities, as well as rework. It can include scope changes in traffic guardrail, traffic signage and kerb and channel and traffic island changes. Changes in this category are generally limited to one or a number of locations within the project and would not generally include a major design change such as the addition of further traffic lanes to the full length of the project. DE Design scope change – environmental issues Large costs can be incurred in environmental assessments that not only include the client but also a large amount of public involvement. If the process is not executed properly, then it can lead to further costs and schedule delay in projects. Environmental assessment is becoming more complex and requires experienced professional input into project designs. Over the years, environmental laws concerning toxic and contaminated wastes in the ground have required project owners to be careful about construction in such areas. Where environmental audits have been inadequately carried out, the cost of clean up during construction can be excessive. Even when a site environmental audit has been carried out during the design and pre-construction stages, it is possible to miss pockets of contamination that can lead to additional project cost to the client. It has been found prudent for a project owner to identify a disposal site and to define what treatment and disposal procedures are required to ameliorate the situation should they be discovered during construction. Such discoveries would almost surely require the contractor to stop work in those areas until a plan was worked out if there had not been the pre-arranged procedures in place at pre-bid. It can also leave the project owner in a vulnerable position when negotiating the cost of disposal. This would almost surely lead to significant project cost overrun.


236

DD Design scope change – drainage This category is for design scope changes specifically related to drainage and can include the addition of drainage structures where pre-existing structures are deemed to be inadequate upon detailed site inspection. In many cases, detailed site inspections cannot be adequately carried our without heavy excavation equipment. In some cases, especially where the design calls for the extension or addition to existing drainage structures, the condition of the existing drainage structure is not fully revealed until excavation work is carried out in the vicinity of the structure. Site investigation can then reveal substantial deterioration to the extent that the existing structure requires full replacement or substantial repairs. Where existing drainage structures have been constructed of corrugated helical steel pipes, the inverts have often rusted through, even resulting in scouring out of fill behind the structure. In most cases, this damage is not obvious at the time of site investigation for design because inverts can be permanently under water, or heavily silted. Drainage design scope changes can occur when an increase is required in the flow capacity due to changes in hydraulic gradients due to conflicts with underground service installations or other expensive obstructions. In some urban and semi-urban areas, changes in zoning in upstream catchments can result in older drainage designs requiring redesign due to revised runoff coefficients as a result of planned land use. A small factor that sometimes requires design consideration is when new metric drainage installations are required to join to very old imperial drainage pipes and cells. The design modifications are small, but real and often overlooked by designers. DF Design scope change – design error This category is for design scope changes specifically related to errors found in design or documentation during the construction step. Such errors can result in substantial cost to the client in the form of rework of completed works. If the design error is substantial, such rework may even involve demolition of completed construction. This category does not include design errors in drainage works DM Design scope change – pavement materials/depth Pavement materials: This category occurs when there is a need to change the specified pavement materials to a different specification. This can happen for a number of reasons. Primarily, a change in material could be required where the excavated pavement box might have poor drainage and there is a possibility for moisture to build up due to difficulty in draining adequately. It may be more economical to improve the specified material quality rather than install additional pavement drainage. This occurs sometimes in subgrades of rock cutting where cement stabilisation might be ordered in lieu of drainage blankets or additional sub-soil drainage lines. In some urban construction, specified granular paving materials may be changed to asphalt material where construction is required under heavy traffic. As well, there is the potential for granular pavement material to disperse under heavy turning traffic or create undue dust where the material cannot be kept wet due to then slippery road conditions that could cause traffic accidents. There also may be the need to install temporary traffic white lining for traffic directions and control, even overnight, which requires installation on a sound pavement surface.

APPENDIX E

237

Pavement Depth: Changes in pavement depth occur when field measured strength of pavement subgrades are weaker than the assumed strength that the pavement designs were based on. Revised pavement designs usually result in increased pavement depths. In most cases, this also requires the raising of the highway vertical grade line by the same amount as that of the pavement. Minor regrading back down to original design grade levels at bridges and new drainage culverts may be required if the pavement redesign extends over a substantial distance. DPL Design preload requirements Earthworks preload is usually required in embankments, especially where differential settlement cannot be tolerated, such at embankment approaches to bridge structures. Change in preload design is usually as a consequence of an embankment that is under preload not performing as expected, usually with slower settlement rated than calculated. The design considerations then usually require either an increase in the preload height, a further waiting period until design settlement is achieved or a redesign to the grade line to allow for further settlement during and possibly after construction completion. DSG Design change to subgrade Design change to subgrade can be brought about by the need to regrade upwards of downwards due to some limiting constraint, such as preserving clearance to existing underground services, bridge clearances, level crossing level compatibility etc. Where unsuitable material is encountered at subgrade, there could be an option to carry out further excavation with an appropriate pavement redesign. DSA Design scope change – safety audit requirement This change in design scope can be brought about by the need to carry out a safety audit on parts of the road under planning or design because of safety concerns about certain aspects of the road that may not have been considered in the original project. Issues such as ineffective guardrail installations, signage and pavement roughness or shoving can be adequate reasons for increasing the project scope. A safety audit can also identify aspects such as visibility and stopping distances that could warrant additional works being carried out that were not considered in the original design brief. Existing pavement slipperiness and pavement drainage are two further considerations that may require scope changes EU Extras unspecified This is a category that is intended to be a catch all for all cost increase factors that cannot be categorised elsewhere due to inadequate project documentation or because of there being large number of small project variations. G Government initiative – employment continuity This category is to accommodate the additional costs associated with a Government decision to have the project carried out by a certain workforce so as to provide employment continuity to that particular group. The Government has a regional development policy that guarantees a certain level of road construction funding to certain regional areas. These funds are normally allocated through certain Local Governments specifically for use on road construction and maintenance projects. In some instances, the cost of delivering a project under this Government initiative can lead to additional costs above what might be budgeted for had the road project been delivered by open tender. This is a deliberate strategy


238

employed by Government in certain instances where, otherwise, the option might be permanent job losses in that region. GCD Government initiative – contribution by developer GCLG Government initiative – contribution by Local Government GCR Government initiative – contribution by rail These categories are to accommodate the instances where additional works are agreed to be included into a road project that should be funded by another organisation. In certain circumstances, there are agreed firm upper limits that can be imposed on these organisations for the incorporation of these specific works. In limited cases the actual costs of such incorporated works can exceed the agreed contribution and then the project estimate will be exceeded and project overrun costs incurred by the client highway organisation. H Cultural heritage issues This category identifies the additional costs associated with cultural heritage issues impacting on the particular project. The Queensland Heritage Act 1995, and the more recent Queensland Aboriginal Cultural Heritage Act 2003, requires certain steps, consultations and clearances be sought and obtained for the road reserve for proposed highway and bridge projects. This consultation period can be excessive in some instances where there are complex issues to be resolved and which can lead to additional unplanned costs. Additionally, there can be stringent requirements placed on the design and construction steps of the project for the identification and preservation of heritage sites, artefacts etc. that can add significant costs to redesigns within the project. Such additional costs have not always been identified and incorporated in project estimates. Instances can occur during the project construction step where certain cultural heritage issues have been revealed for which no one had knowledge. The project was then required to deal with such issues in a defined and thorough manner. In some instances, such occurrences during construction can require substantial redesign of project elements and additional heritage clearances. LD Latent condition – requires design change LR Latent condition – rock encountered LSG Latent condition – additional stabilising LUS Latent condition – removal and replacement of unsuitable material These risk factors are a grouping of latent conditions that can occur when constructing a road project. There are two types of latent conditions. Type 1 is a condition not shown or improperly shown on the project plans or documents that substantially alters the pre-bid anticipated performance of the contractor through delays, reduced productivity impossibility of performance, suspension of work or extra works to fix or solve a situation. Type 2 is a condition not normally found in the performance of work and thus not normally contemplated by a reasonably prudent contractor in bidding work as planned and specified in the contract. These unforseen site conditions can be very subjective, hard to document and difficult to validate. Highway construction contracts generally contain unforseen, latent site condition clauses that spell out the circumstances under which they can be deemed to be additional to the contract, and thus incur additional client costs. Four discrete latent condition occurrences are identified and described below:

APPENDIX E

239

1. LD Latent condition requiring design change: This includes the discovery of any unidentified underground obstructions that require modifications to the design. This can include modifications to new drainage designs and partial to full foundation redesigns to structures due to unsuitable foundation conditions. This can include increases or reduction in foundation levels that can lead to adjustments to the physical dimensions of structures, etc.

2. LR Latent condition – rock encountered: This risk factor is self explanatory and can occur in earthworks operations in cuttings with the presence of rock bars which may not have been identified by a site inspection or bore log. Rock is normally defined as sound and solid mass and of such hardness that it cannot be loosened or broken down by a certain defined size and capacity of excavation equipment under the assessment of the client. Unrecorded rock outcrops can occur in excavations for drainage structures, underground ducting etc. and such installations might not be able to be raised or deviated to miss the obstruction.

3. LSG Latent condition – additional stabilising: During the process of new pavement construction or the strengthening an existing pavement with a structural overlay of paving material, an assessment should be made of the strength of the insitu subgrade or sub-layer material so that the design strength of the paving layer is not exceeded during the pavement service life. In most cases, in-situ testing against the design axle loading, on which the pavement depth is designed, is carried out. During the construction process there is a risk that the exposed subgrade material can test weaker than its design assumption. In such a case, the in-situ strength of such material can be improved by the addition of cement or lime by in-situ stabilisation of the subgrade material. This process has the advantage that the design pavement depth can still be installed without the need to regrade the pavement levels.

4. LUS Latent condition – removal and replacement of unsuitable material: This process is used to overcome situations where foundation materials for pavement, drainage structures and structures are found to be inferior and the material is ordered to be removed to a specified depth and replaced with material of a certain quality. In some situations, the knowledge of the presence of unsuitable material can be ascertained by bore probes or other testing methods and adequate provision made in the schedule of quantities to cover such treatments. In cases where none or limited testing is carried out in the design stage, then it is appropriate to include only a nominal provision in the quantities to cover the risk of discovering and having to replace unsuitable material. Usually, contract provisions allow for such variations as a cost to the client.

MA Material cost increase – asphalt MB Material cost increase – bitumen price ME Material cost increase – earthworks MP Material cost increase – pavement materials

1. MA & MB Material cost increases – asphalt and bitumen prices: There are a number of road making components for which the manufactured cost or basic cost can vary substantially from the time of an estimate through to its incorporation in the project. One such major component is bitumen which is used in bitumen spraying as well as in the manufacture of asphalt. The price of bitumen is strongly linked to the price of oil and can fluctuate widely over time. Provision for material cost adjustments are


240

usually made in projects for the risk of price increases but instances can occur where higher than predicted price increases can impact on the final cost of bitumen.

2. ME Material cost increases – earthworks: Road designers generally endeavour to obtain a balance between the volumes of excavated material as opposed to the compacted volume of material required for fill for roadway embankments. Instances can occur where the risk of not achieving an economic balance can lead to large unplanned costs for either the disposal of excess material or the cost of importing additional material onto the project. The major reason for such a net imbalance is that certain excavated material may not be able to be incorporated into the project as fill because of its inferior quality or because of its contamination from some site source that was not known at the time of design. Where measured quantities of both excavation and fill materials substantially differ from the scheduled quantities, then there can be circumstances demonstrated when additional costs may need to be borne by the project.

3. MP Material cost increase – paving materials: Pavement design changes can substantially increase paving material volumes which can lead to the risk of increased pavement materials and hence increased costs. In addition, for certain project locations, there may be no economically suitable source of paving materials and hence the material then has to be transported large distances from established quarries to the project. In some rural projects, there may be raw material suitable for paving gravel production close to a rural project but no suitable crushing equipment. It may then be a matter of establishing crushing facilities at the material source, if suitable equipment can be enticed there. In this case, the volume of paving material to be produced can be the important economic factor in determining the supply price of the paving material. Either supply option can be significantly more expensive than conventional quarries that are close to projects. This can have significant influence on the final cost of a project.

MQ Material process/ quality issue The cost of having to rework or worse, replace material due to poor quality or process issues can be a real risk factor that can lead to significant cost overruns in projects. Contract provisions usually protect the position of the client in not incurring additional costs due to the contractor’s supply of poor material quality or processes. However, there can be occasions where material is supplied or provided by the client as a provision of the contract. The contractor is then required to incorporate that material into the project. If the material is found to be deficient in quality, then there may be a case where the client, and hence the project, has to bear additional costs to replace, rework or modify the supplied materials. The supply of material by the client can occur when there is expected to be considerable lead time required for the supply of, say, paving gravel in stockpile in remote areas and some forms of pre-cast bridge components, such as concrete piles and girders. Material is generally only supplied by the client when there is the risk that long lead times in procurement of specialist components or material in remote locations will severely impact on the desired completion date. In some instances in remote locations, paving material is stockpiled or concrete bridge components can be supplied at the job site by the client well prior to the letting of the highway construction contract. N Contract failure – new contract establishment costs Contractor default is one of the most costly events for a project owner. An existing road contract can fail and the owner is then required to terminate the contract under the defined steps of the contract. The outstanding contract work can be re-let as a new contract or can be

APPENDIX E

241

completed using a dedicated work force on a cost-plus basis. The method of completing the contract works will generally be determined by the client by taking into account factors such as the amount of works outstanding, the potential for deterioration of the already provided works and public and works safety provisions. A number of factors can lead to a contract termination, however there are also procedures usually adopted to minimise the risk of such an occurrence. These include:

• pre-qualification of contractors that includes financial capability checks • tender evaluation procedures to check that submitted contract rates are commercially

realistic. Provisions to protect the client in the event of a contract failure can include the provision of an insurance bond on the value of the work and/or the retention of a certain percentage of progress payments during the conduct of the contract. The additional costs to a project as a result of a contract failure usually includes the need for a complete and certified measure up of completed works and works in progress, the making safe of the works, ongoing traffic control provisions where the public traverse the project, legal costs associated with, say, bankruptcy of the contractor, re-documentation and calling of a new project contract. All these activities will consume the time of a project manager and administration support and can add up to a considerable unplanned project cost. O Remote location costs In remote rural project locations, there is a need to provide adequate accommodation for project workers. Sometimes, this can be an addition to normal project on-costs and hence factored into project costs. When projects extend past their estimated completion dates, then these remote location costs such as accommodation costs can add substantially to the project’s budget estimate. P Project administration cost increase When a project extends past its planned duration, then project on-costs can be incurred that were not otherwise planned. Other factors can also contribute to an increase in project administration costs, such as:

• highly litigious contractor that embarks on claims to recover costs for various aspects of the contract

• concern over quality control processes in the project that calls for increased audit and process surveillance activities on the side of the client

• ongoing community consultation and additional public relations processes where projects need to be managed in complex urban environments or under increasingly heavy traffic congestion and pedestrian safety aspects

• ongoing environmental or cultural heritage issues that were not anticipated to extend into or present during the project delivery step.

Q Quantity increased measure In unit rate road contracts, the final value of the project contract is determined from the final measurement of the scheduled work that forms part of the contract. Site measured quantities can vary substantially from scheduled quantities for a number of reasons, including:

• that the original quantity was incorrect • errors in the initial measuring ground/foundation levels • increases in ordered works, dimensions and measurements • revised pavement thicknesses.


242

All net increases in measured quantities can have a substantial effect on the final cost of a project. Initial inaccurate quantity take-offs for schedule of rates contracts can mislead contractors resulting in costly errors in bid pricing in other areas such as quality assurance requirements. R Resumption/accommodation works Where private land is required on which all or part of the project is to be built, the land can be purchased outright from the owner or can be compulsorily acquired under the powers of the government. In the latter case, the land owner also has recourse to an appeal mechanism that can take a considerable period of time. The outcome of this appeal mechanism can in some circumstances increase the cost of acquiring the land for the project. In addition to compensation for the taking part land parcels, the government is obliged to carry out accommodation works adjacent to the resumed land so as not to affect the amenity of that land. This can be costly and can increase project costs over that estimated at the time of commencement of any land procurement. Property acquisition costs can be impacted by a design that does not consider the effects that the project has on adjoining properties. Poor or restricted access to residual and adjacent properties can have a direct bearing on the property acquisition cost and on the potential consequential damages to adjacent properties. Construction sequencing required to build a project that causes multiple disruptions to the activities on abutting property can often result in claims and/or litigation by the adjacent owners to recover damages to his operations. S Services relocation costs The relocation of utilities and services can be a significant part of projects and in transportation projects in particular can cause heavy disruption to existing utilities. The road reserve is also used by services (utility) authorities under their various legislation heads. While pre-approval is usually required for the service authority to install their particular facility, this approval usually obliges the road authority to pay for the cost of relocating such existing services where the new road-design conflicts. The physical relocation work is usually carried out on an actual cost basis and this can be substantially greater that preliminary estimated costs. These preliminary estimate costs are what are generally used when developing road project estimates. Actual cost figures can be greater than estimated costs for a number of reasons, the most common is workplace safety provisions that requires workers to sometimes work under potentially dangerous traffic conditions. This can lead to the requirement to install additional temporary protective measures and/or the need to have the work carried out at night or on weekends when traffic conditions, and the potential for danger, may be lessened. Early and careful planning and coordination with the owners of the affected utilities is the key to minimising the cost overrun of relocating services and maintaining services to adjacent properties. Depending on the regulations and agreements that allow the utility owners to be in their pre-project locations, the cost of relocation could be borne by the utility owner or the project. Poor coordination of utility relocations (i.e. requiring multiple moves of the same utility) will result in additional costs that usually have to be borne by the project. It should be noted that there are circumstances where multiple moves cannot be avoided, and when recognised by both parties, the cost is usually included in the project budget. It is the unexpected multiple relocations, which could have been avoided, that have the impact on the project budget. The project design team usually has a major responsibility to plan for the

APPENDIX E

243

coordination of the utility relocations with the utility owner. This can have a serious effect on the project budget if it has not been coordinated adequately well in this area. A utility activity that involves substantial management at the design stage is the locating of existing utilities and is often shared with the utility owner that has been designated for an area. Regardless of any shared responsibility, the project designers should ensure that they are getting the best information available as there can be substantial cost saving when designing a project with the intention of not requiring relocation of certain types of utilities. When it is discovered during construction that a utility is in fact in conflict with the construction, then the cost impact can be large since it often requires the contractor to shut down activities in that area of conflict or develop some expensive ‘work-around’ scheme to avoid the shutdown. In this case, these costs come back to the client who may or may not seek to have the designer share in paying for the cost. Another utility cost overrun area is the lack of appreciation of long lead times necessary to procure special materials, equipment, as well as system testing of the relocated utilities before they can be placed in service. Telecommunication and power facilities are often candidates that require longer lead times. When this long lead time requirement is not recognised or not appreciated then there is often a schedule as well as a cost impact to the client.. SC Specification change The quality of specified works and/or materials can be varied by the client during the construction of a project, however this is not done as a matter of course. This can be done to rectify an error in the specification or drawings. It can also be done to improve the specified quality of certain material properties that may not perform adequately under anticipated service conditions. For example, specifications can be changed to vary the specified finishes on concrete surfaces from wood to steel float finish for durability. As well, finishing construction tolerances for pavements can be changed to provide improved the completed running surface finish than that documented. In most cases, where a specification change has been requested by the client, then it is treated as a variation under the terms of the contract. TH Contract tender price higher than original estimate The project estimated cost is based on the forecast tender cost of the construction component of the project delivery. Where the actual tender cost is higher than the original estimate, then there is an adjustment required to the project estimate. TCI Contract tender price increase due to inflation Contract tender prices can be adjusted for inflation costs for labour and materials when there are provisions within the contract provisions. The project estimated cost is based on the forecast tender cost of the construction component of the project delivery and also includes an estimated allowance for inflation costs. Where the actual inflation costs calculated for the contract are higher than the original estimate provision, then there is an adjustment required to the project estimate. Adjustments to contract payments are usually made on a defined formula which incorporates a monthly Roadwork’s Input Cost Index (RICI) that is published by the Australian Bureau of Statistics. Separate adjustments are also made for the cost of bitumen for works which incorporate predominately bitumen products. WW Wet weather effects/rework Because of the nature of highway construction, the works can be exposed to prolonged wet weather that can have an adverse effect on construction productivity, especially if substantial


244

parts of a project are saturated from rain. Generally contract conditions shift the risk of the occurrence of wet weather on to the contractor and the cost of rework are deemed to be included in the cost of particular construction processes that are contracted. The probability of the occurrence of rain can generally be derived from the use of historic rainfall charts and probability. A contractor can also insure against losses from the effects of rain. Where intense rainfall periods can be predicted, contracts can be let such that the project works can be reasonably completed outside the windows of anticipated rains. A prudent contractor would plan to construct those portions of a road project vulnerable to the effects of prolonged wet weather during periods of lower wet weather probability. Contract provisions normally provide only for an extension of time to the construction period for wet weather or its effects. Generally, the contract does not allow for any contractor cost reimbursement for wet weather. Where there is demonstrated a case of extremely un-seasonal or exceptional wet weather, then there may be a case where the client may incur such certain extra costs.