A Review of Analytical Models, Approaches and Decision Support Tools in Project Monitoring and Control

8/18/2019 A Review of Analytical Models, Approaches and Decision Support Tools in Project Monitoring and Control

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A review of analytical models, approaches and decision support tools in project monitoring and control

Öncü Hazır

TED University, Faculty of Economics and Administrative Sciences, Ziya Gökalp Caddesi No. 48, 06420, Kolej, Çankaya, Ankara, Turkey

Received 30 January 2014; received in revised form 29 August 2014; accepted 9 September 2014Available online 26 September 2014

Abstract

This paper reviews the problems, approaches and analytical models on project control systems and discusses the possible research extensions.We focused on literature in Earned Value Analysis (EVA), optimization tools, and the design of decision support systems (DSS) that willcontribute to helping project managers in planning and controlling under uncertain project environments. The review reveals that further research isessential to develop analytical models using EVA metrics to forecast project performance. It also suggests that DSS should be model driven,function as early warning systems and should be integrated to commercial project management software.© 2014 Elsevier Ltd. APM and IPMA. All rights reserved.

Keywords: Project management; Project monitoring and control; Earned Value Analysis; Decision support systems; Project management software

1. Introduction

Projects are one of the most important components of today's organizations. In almost any firm and sector, organi-zations are becoming more and more project based. This may

be perceived as a consequence of the contemporary management practices that have transformed organizations from hierarchical tomore flat ones. As they have receded from a hierarchical andisolated nature, projects have become the medium for inter-departmental or even inter-organizational activities. Another factor that reinforces the rise of projects is the increasingcompetitive pressure. Competition, becoming fierce day by day,leads the firms to seek excellence in accomplishing the tasks.This pursuit of excellence in management has increased theimportance of coordination, monitoring and control functions.From this perspective, project based organizational structuressupport accomplishing specific purposes/outcomes, focusing onresponsibility and authority, ensuring better control and coordi-nation, and facilitating better communication and customer relationships (Meredith and Mantel, 2011).

In order to ensure these gains and the accomplishment of goalseven under the threat of various uncertainties (Aytug et al., 2005;Herroelen and Leus, 2005), employing effective project moni-toring and controlling systems has become essential in project

based organizations (Shtub et al., 2005). Considering this needand importance, in this paper we focus on development of thesesystems, their content and scope. Specifically, we investigatemodels and algorithms that will support managerial decisionmaking and constitute the foundations of these systems.

To put formally, a project monitoring and control system worksto minimize the deviations from the project plans and consists of identifying and reporting the status of the project, comparing it with the plan, analyzing the deviations, and implementing theappropriate corrective actions. Hence it includes the set of policies,methods and tools that would ensure the achievement of the

project targets. An effective system should clearly define thefollowing policies:

(a) monitoring policy: what, how, where, when and by whomto monitor,

(b) intervention and control policy: what, how, where, whenand by whom to prevent, intervene and correct. E-mail address: [email protected] .

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International Journal of Project Management 33 (2015) 808–815

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Mathematical modeling is one of the means to formulate andanalyze these policies. It has been used to investigate various

project management problems and literature review papers were published (some of them are by Herroelen (2005), Herroelen andLeus (2005) and Kolisch and Padman, 2001). These reviewscover studies that address a wide range of managerial problems

(time scheduling, resource allocation, quality assurance). Project monitoring and control from mathematical modeling perspective,conversely, have not received sufficient scholarly attention.Accordingly, we aim to address this lacuna in the literature.Different from its predecessors, this research presents a reviewwith a narrower scope. A more in-depth analysis of approachesand models on monitoring and control systems is performed.Furthermore, an initiative approach for designing model-drivenDSS for effective project monitoring and control is developed,with an emphasis on recent developments and studies.

Current studies on project monitoring and control mainlyexamine financial control tools and various accounting techniques

that managers use to monitor the project outcomes (Rozenes et al.,2006). We will not cover these accounting tools in this review.Instead, we will pay special attention to Earned Value Analysis(EVA), since it is the most widely used managerial control tool inthe industry. We will elaborate on analytical aspects of EVA andrelevant optimization models. We will give emphasis to theintegration of these models into decision support systems(DSS) and project management software. Regarding this specificapplication area, research areas demanding further effort and

promising extensions are explicitly listed.We organize the review and discussions as follows. First, in

Section 2.1, we present existing studies on EVA, as it is widelyused in practice. Then, in Section 2.2, we examine the optimization

models to set project control decision variables, since these modelsserve as a basis for DSS design. Afterwards, decision support toolsand relevant project management software are discussed inSections 3 and 4. Finally, in Section 5, we present conclusionsand make a summary of future research areas.

2. Literature review

2.1. A widely used managerial control tool: earned valueanalysis

EVA is a managerial methodology to monitor and control

projects and it uses monetary units as a common basis tomeasure and communicate the progress of a project. It is basedon comparing the actual and the budgeted values of the work

performed, the time taken and the costs incurred. Hence, timeand cost perspectives of a project control system are integrated.Cost and schedule variance are calculated to evaluate thecurrent project progress and also predict the total project cost and duration. We refer the readers to the books (Fleming andKoppelman, 2005; Shtub et al., 2005; Vanhoucke, 2009) for more detailed explanations on the basic principles and metrics.

In practice, EVA has been generally used to measure project performance throughout the life of a project. However, it couldalso be used in forecasting the resulting project outcomes;specifically to estimate the expected project time and cost using

the current status of the project. In this aspect, Vandevoordeand Vanhoucke (2006), and Vanhoucke and Vandevoorde(2007) developed three forecasting methods that are basedon EVA metrics and compared them in terms of predictionaccuracy. For that purpose, nine scenarios and possible outcomeswere considered and Monte-Carlo simulation was employed. In

addition, activity sensitivity measures and their relationships withforecasting and use in deciding on project control strategy wereinvestigated (Elshaer, 2013; Vanhoucke, 2010).

In order to improve the prediction performance of EVA,statistical methods could be integrated to the analysis (Lipke et al., 2009; Narbaev and Marco, 2014; Tseng, 2011). In thisregard, Caron et al. (2013) followed Bayesian approach andintegrated experts' opinions in describing the probability of events. In addition to statistical analysis, learning curves andrisk management tools were also combined with EVA. Plazaand Turetken (2009) investigated the effects of learning anddeveloped a spreadsheet based DSS.

Concerning risk management, Pajares and Lopez-Paredes(2011) developed two metrics that support managers indifferentiating whether project over-runs are within the expectedvariability or due to structural deviations. In the case of deviations,decisions on corrective actions become critical. For supportingdecision making, Aliverdi et al. (2013) and Acebes et al. (2014)used simulation and statistical control charts. In addition toanalysis of risks, hedging against uncertainty is important toachieve project targets. For this purpose, Naeni and Salehipour (2011) modeled percent completions as fuzzy numbers and usedfuzzy set theory for estimating project performance.

All the abovementioned studies addressed single project organizations. However, firms invest in many projects and

these projects have resource dependencies within the firms.Portfolio management, which aims to choose and managemultiple projects in a way that enhances business strategy andcontributes to achieving organizational goals, has become moreand more critical in organizations. To assess the performance of the projects in the portfolio, Vitner et al. (2006) combined EVAwith a multidimensional control system and used Data Envelop-ment Analysis (DEA), which is a mathematical approach toevaluate the efficiency of decision making units (DMUs). In the

project management context, every project was modeled as aDMU and its efficiency was measured as a weighted sum of itsoutputs divided by a weighted sum of its inputs (see Farris et al.

(2006) to evaluate the performance of engineering design projectsusing DEA).

Other than examining the use of EVA in forecasting and performance assessments, graphical illustrations of EVA param-eters have been widely utilized by project managers before takingcontrol decisions. To illustrate the deviations from plans andemphasize the need of corrective actions, graphical tools could bevery helpful (Anbari, 2003; Cioffi, 2005). For this reason, Hazir and Shtub (2011) focused on graphical and tabular presentationsof EVA. They questioned the relationship between information

presentation format and project control. Monte-Carlo simulationwas used to replicate and model the uncertain project environ-ments. This simulation technique was used by other researchersto compare two project tracking methods: top-down or bottom-up

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(project or activity based) approaches (Vanhoucke, 2011). A project based method relies on EVA data that can be used as earlywarning signals, whereas an activity based one is founded on riskanalysis and uses the activity sensitivity information to decide onthe critical activities to focus on. The project based approach wasfound to be more efficient for networks with a serial activity

structure, whereas the activity based one performs better in parallel structured networks (Vanhoucke, 2011).

Even though EVA has been increasingly getting attentionfrom project managers, there are some limitations inimplementing EVA in practice. Hall (2012) listed them as:

1. Critical and noncritical activities are not differentiated.2. Activities are assumed to be independent.3. Behavioral aspects of management are not taken into account.4. Quality of processes and output are not assessed.5. Information requirement is high.

In addition to these points, we note that EVA only considerstwo dimensions of project planning and control: time andcost. However, other performance measures such as technical,operational and quality specifications could be also critical. To

broaden that limited focus of EVA, Rozenes et al. (2004) proposed a multi-dimensional control system. Their systememphasizes monitoring work breakdown structure (WBS) at work package level (El-Mashaleh and Chasey, 1999). Another recent critical point is on assessing schedule performance usingmonetary values. To eliminate this, Khamooshi and Golafshani(2014) focused on durations and measuring earned durationinstead of earned value. All these limitations and concerns

should be carefully analyzed and taken into account in theapplications.

2.2. Optimization tools: how to set control variables

In designing an effective project control system, it is critical tofind out the optimal timing and magnitude of project controlactivities. For thispurpose, operations research (OR) methods suchas simulation, dynamic programming and stochastic optimizationhave been commonly applied. We will pay special attention tothese optimization based studies and discuss how these OR toolsare utilized.

Among those who employ simulation, Partovi and Burton(1993) evaluated control-timing policies: equal intervals, front loading, end loading, random and no control. Their study revealedthat the end loaded policy performs best in preventing timeoverruns; however, there were no significant differences amongthe policies for the cost required to recover from deviations.Falco and Macchiaroli (1998) focused on effort concentration andformulated a function that is linearly dependent on the totalnumber of active operations and is inversely related to total slack.They increased the frequency of checkpoints at the critical time

periods, for instance, when resource needs are maximal. On theother hand, Bowman (2006) examined activity duration specifi-cation limits. He assumed that durations that exceed the limitscould be moved back to the limits with additional costs.

To decide on timing of the control activities, Raz and Erel(2000) used dynamic programming. They maximized the amount of information that is produced by control operations. This amount depends on the intensity of the activities that have been performedafter the last control operation and on the amount of time that

passed since activity completion. This dynamic approach is based

on the assumption that some of the information generated bycontrol operations will be lost over time.

To determine the inspection points, Golenko-Ginzburg andGonik (1997) formulated a stochastic optimization model anddeveloped a heuristic on-line control tool and set the proper

project speed. In the model, the progress of a project is assessedonly via inspection at control points and the project speed can

be altered at these points. Note that in modeling stochastic project networks, usually a common PERT (Program Evalua-tion and Review Technique) assumption is made meaning that activity durations are independent. However, this assumptionmight not be valid for many real life cases. Regarding this,

Markov chains have been used; this approach allows correctionor repetition of earlier activities (Hardie, 2001).Regarding the content and magnitude of control activities,

simulation and optimal control theory have been applied to modelintervening policies and their impacts on project outcomes. Bymeans of simulation, Hazir and Shtub (2011) modeled theimpacts of two types of corrective actions: minor and major interventions.Minors refer to short-term, operational actions suchas workers working overtime in a road construction project.However, majors such as new technology investments createstructural changes on cost figures and have long-term effects. Intheir simulation application, watching the snapshot views in agiven display format, simulation users decided on taking a

corrective action, and which corrective actions should be takenfor a given project in a decision period.

In addition to simulation, another appropriate technique tomodel the effects of intervening activities is optimal controltheory. It is a field of mathematics that aims to determine effectivecontrol policies for dynamic systems. Using this theory, a control

problem is defined as a function of state and control variables andmodeled using differential equations that express the impact of thecontrol variables to minimize the given cost function. Varioustime-dependent control problems encountered in management science and economics have been modeled using this theory. Inthis regard, we refer to the book of Sethi and Thompson (2002)

for various applications. Even though this literature offers astrong theoretical background to model dynamic, time dependent

behavior of projects, optimal control studies on project manage-ment problems are scarce.

In scheduling projects, optimal control theory has been usedto allocate continuously divisible resources in a least costly way( Nowicki and Zdrzalka, 1984; Weglarz, 1981). Moreover, therelationship between control efforts and project cost deviations has

been investigated: Kogan et al. (2002) developed a continuousmodel to determine the optimal control effort with the assumptionthat control activities have a negativeeffect on project performancedeviations. Concerning real life implementations of the theory, acontrol model was developed to allocate testing resources in asoftware development project efficiently (Kapur et al., 2013).

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We note that the abovementioned optimal control applicationsassume continuously available resources or model continuoustime systems. However, in real life, resources are usually availablein discrete quantities, such as the workers or the machines. Inaddition, modeling the discrete time characteristics of projects suchas the timing of the beginning and end of activities and the

corresponding time–resource profile is important.Considering these characteristics, Azaron et al. (2007)

presented a multi-objective model to optimally control the resourceallocation. They addressed the time–cost trade-off problem wherethe cost of each activity is a non-decreasing function of the amount of resource allocated to it. They optimized the project direct cost,the mean of the project completion time and its variance.Regarding these criteria, non-dominated solutions were deter-mined by using discrete-time approximation. More recently, Hazir and Schmidt (2013) analyzed multi-mode project networksassuming a discrete time/cost function. They described an optimalcontrol problem that reflects the dynamic evolution of the cost

savings that depend on the level of control and the processingtechnology (modes).Having summarized the limited number of analytical models

on project control systems in the literature, we notice that further studies, especially on optimization models and tools arein need. Moreover, in order to make the results of all thesesummarized studies concrete and applicable to the problems of the industry, it is crucial to embed them in support tools that guide managers in deciding on corrective actions.

3. Decision support systems

DSS are computer-based systems that support decision

making by combining and analyzing data and providinganalytical models and tools that contribute to the selection of alternatives (see Shim et al., 2002) for a discussion on thedefinitions, components and evolution of DSS technologies).They have been widely used in planning, organizing andmanaging manufacturing or service operations (Eom and Kim,1997). From the project management perspective, project

planning and control are suitable application areas for DSS,since unstructured or semi-structured decision-making problemsare confronted; and many alternative solutions should beconsidered. Moreover, in today's rapidly changing, competitiveenvironment, organizations faces to manage portfolios of projects.

In this regard, we will discuss which OR methods could be used to facilitate planning and control under uncertain,multi-project environments and how these methods could beintegrated to DSS. OR literature has contributed to solvingoperational and level problems in project management, such asvarious scheduling and resource allocation problems (Tavares,2002; Williams, 2003), but recently practitioners have also

been working on developing support tools for strategic-levelneeds. Although DSS tools have been increasingly used in

production and operations management (Eom and Kim, 1997),applications in project management are scarce. We start byreviewing this limited number of applications.

DSS applications in the literature mostly concentrate onscheduling and risk analysis. Kolisch and Padman (2001)

reviewed some of the scheduling studies that make use of exact and approximate optimization tools. More recently, Trietsch andBaker (2012) proposed a new stochastic scheduling framework toenhance DSS so that reliable solutions are produced. Also in riskmanagement, planning activities are shown to be effective inreducing the negative effects of uncertainty on project targets

(Zwikael and Ahn, 2011). Integrating scheduling and riskanalysis, Megow et al. (2011) developed a DSS for planninglarge scale maintenance operations in chemical manufacturing.

To model andestimate the risks and quantify the consequenceson project targets, Monte Carlo simulation has been widely usedin the literature. The reviews of Kwaka and Ingall (2007) andVanhoucke (2013) summarizedthe advantages and disadvantagesof using Monte Carlo simulation to model projects, especially for risk analysis and control. In addition, we cite four nice riskanalysis applications. In the first one, a DSS to predict project riskand assess impact on the project cost was developed ( Nguyen et al., 2013). The decision making tool followed a scenario based

probabilistic approach. In three others, Analytical HierarchyProcess (AHP), simulation and fuzzy logic were used (Dey, 2001;Fang and Marle, 2012; Liu et al., 2006).

As a result of easiness and simplicity in modeling and solving,existing academic studies have usually modeled projects individ-ually. However, in practice, organizations engage in managing

project portfolios and work to share and distribute resources andcapabilities over many projects effectively. Following the existingapproaches andtreating the multi-project planning problem as a set of independent single-project problems result in local optimumsolutions for the organizations but a global analysis is requiredto facilitate effective Project Portfolio Management and achieve

better results regarding strategic objectives of organizations.

Also in program management, there exist a limited number of studies on using DSS. Slowinski et al. (1994) developedDSS that address non-preemptive scheduling problems that contain multiple activity modes. To solve these problems, theyemployed three different heuristics: priority rules, simulatedannealing and branch-and-bound based approximation algo-rithms. Differently, Arauzo et al. (2010) used simulation toevaluate various scheduling policies for multiple projects.Considering the scarcity in theoretical studies, we emphasizethat multi-project organizations require novel program man-agement techniques to cope with the inherent complexity indistributing resources among various projects. We note that

application areas and DSS needs are not limited to schedulingand risk analysis. Next, we summarize these limited numbers of applications.

Cohen et al. (2005) focused on how projects are accepted andadded to the system. Thereby, they found out the optimal loadingof projects. Considering the dynamic and stochastic behavior of the problem, they used cross entropy, which is a technique tomodel and solve rare event simulation and stochastic combina-torial optimization problems. On the other hand, Hans et al.(2007) examined multi-project planning under uncertainty. Theyreviewed the studies in hierarchical planning and introduced ageneric hierarchical project planning-and-control framework.

Multi-criteria decision approaches and methods could beeffectively used in DSS (Olson, 2008). A recent interesting



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project management DSS application of Lauras et al. (2010)addressed many performance criteria (cost, time, quality, risk,etc.). Their performance assessment system contained a methodof defining the weights for criteria and allows making a globalanalysis of project performance. To assess the performance, Wiand Jung (2010) considered a project-oriented virtual organi-

zation and developed an index that contains quality, timeand budget components. For this purpose, fuzzy approach todecision making was also employed (Dweiri and Kablan,2006).

We also note that in project management DSS, scheduling/ rescheduling and control functions have been usually examinedseparately.However, integration of these directly linked functionsis important for effective management. Control policies should

be determined in coordination with scheduling objectives anddata on the outcomes of control activities should be used indetermining the re-scheduling needs. Despite this importance, thisrelationship has not been studied sufficiently. The relationship

between scheduling and control functions, characteristics of datasharing among them and possible integration strategies should betheoretically investigated. Moreover, from the application per-spective, DSS are required in preparing schedules that can tolerateuncertainties, and for determining when control is necessary andwhen and how corrective actions should be taken.

As a final point, considering the assumptions and propertiesof all these academic studies and projecting on the requirementsof the practice, we realize that there exists a theory– practicegap. Academic studies mainly investigate closed systems, manytimes assume that information is known in advance, and workon less complex problems. However, managers face multi-dimensional, dynamic and open systems and require solutions/

predictions to more complex and non-deterministic problems.To fill this supply and demand gap between academia andindustry, Herroelen (2005) emphasized the integration of scheduling theory and risk analysis tools into the current

project management practice, particularly the use of commer-cial project management software. We believe that further research on DSS, and their practical use in program management will help to close the abovementioned theory– practice gap. Thecompliance of DSS with program management applications and

project management software will facilitate these approachingefforts.

4. Project management software and DSS integration

Software support is indispensable in performing variousfunctions of project management (Shtub et al., 2005). Profes-sionals widely use projectmanagement software packagessuchasMicrosoft Project, Primavera, etc. Some surveys (by Liberatoreand Pollack-Johnson, 2003; Liberatore et al., 2001) revealedthat these packages have been mainly used for critical path

planning (87% of 200 responses), whereas there is much less useof more complicated methods like time–cost trade-off analysisand probabilistic analysis or simulation (19%, 21% respectively).On the other hand, for controlling, earned value is employed by53% of the managers. Interestingly, critical path analysis is alsoused for control purposes (66%).

Microsoft Project is the most commonly used software andis mainly used for planning. Even if EVA tools are integrated tothis software, in practice, these tools have been mostly usedonly to collect and present the data in various formats. Thislimitation might stem from the following:

• Managers might not be aware of the relevant DSS or theymight find them too sophisticated to use.

• Acquiring these tools could be found infeasible regarding the project budget, or expensive regarding the benefits expected.

To compare the well known software packages, Kastor and Sirakoulis (2009) and Trautmann and Baumann (2009)concentrated on scheduling and resource allocation capabilities.These packages contain scheduling and resource levelingheuristics. They can display the schedules using Gantt-chartsand resource usages and capacities with bar charts. However,regarding scheduling outputs, there exists a great variance

among these packages and also when compared to the existingtheoretical results. Using the same data, the packages might output schedules with considerably different project completiontimes. Moreover, they perform evidently worse compared tooptimal solutions generated by well-known scheduling algo-rithms in the academic literature. Therefore, better performingschedule-generation methods should be integrated to commer-cial software (Kastor and Sirakoulis, 2009; Trautmann andBaumann, 2009).

Other than scheduling and planning, the second wideapplication area of project management software is risk analysis.Plug-in software such as @ Risk and Crystal Ball are well known.The basic strength of these simulation based tools is integrability

to Microsoft Excel. Today, spreadsheets are inevitably usedin every industry. Many decision support functions can be

performed, even some optimization models could be implement-ed using them. For this reason, spreadsheet based DSS offer asignificant research and application potential (see Buehlmann et al., 2000 as a good example in manufacturing).

In addition to these tools that support scheduling and riskanalysis, project managers require reliable early warning systems(Vanhoucke, 2012), and the majority of existing software doesnot include these systems (Trietsch and Baker, 2012). Analytical

project control tools that support the managers to interveneeffectively and stimulate them to undertake corrective measures

at the right time are needed. In this regard, statistical processcontrol techniques could be utilized (Bausch and Chung, 2001;Chang and Tong, 2013). Simulation software could integrate theeffects of such managerial intervening actions (Williams, 1999).Integrating DSS that counsel decision makers in determining thetiming and magnitude of corrective actions effectively willcontribute to fulfilling the needs of project managers. For instance, this DSS could inform managers about the optimalfrequency of rescheduling during project application.

Moreover, the industry has a considerable need for themodel driven DSS in which quantitative models are the basiccomponents that allow decision makers to manipulate model

parameters, and perform a sensitivity or what/if analysis (seethe survey of Hahn and Kuhn (2012) on model-driven



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approach). DSS should provide a simplified representation of the problem in a simple, understandable way. In that sense,

project control problems are a promising research area for model-driven DSS that have been utilized in various fieldssuch as production planning and supply chain management (Power and Sharda, 2007). These DSS should contain these two

important components:

1. Analytical models and solution algorithms Model basewhich encompasses simulation and optimization models:The simulation module uses random distribution functionsto sample activity costs and durations and facilitates anunderstanding of the complex, stochastic and dynamic

behaviors of real project systems. The distribution of thetotal project cost or completion time could be estimated.The optimization module addresses when and how theintervention should be undertaken. Linear and integer

programming, and network analysis are the OR tools that

have been increasingly utilized in model bases (see Eomand Kim (1997) for the tools embedded in DSS). In additionto these techniques, dynamic programming and optimalcontrol could be used because of the dynamic and time-dependent behaviors. From the multi-criteria decisionmaking perspective, AHP has been increasingly applied toresource allocation and conflict resolution problemsand project control is claimed to be a prominent area (Subramanian andRamanathan, 2012).Finally, simulation and optimization modules should interact.For instance, the optimization module could invoke thesimulation module to generate random data. Using this data,optimization models could be solved.

2. Data presentation and graphical interface Managers areusually required to consider many alternatives beforedeciding. They prefer to generate these alternatives, modifythem and make sensitivity analysis using various visualrepresentations. These visual interactive capabilities areincreasingly embedded in commercial software packages.Information presentation format was shown to be influentialon the quality of managers' decisions (Hazir and Shtub,2011). Particularly, variance graphs and numerical tablesare found to be effective. In this aspect, there is a managerialdemand for visual interactive systems that use appropriate

presentation formats and facilitate monitoring and control-

ling projects. Simulations supported with effective visualtools might also aid learning, and they might be used asan efficient and effective way of teaching and learningcomplex and dynamic systems (Davidovitch et al., 2010;Shtub et al., 2006).

5. Conclusions

The aim of the article is to review the current studies on project control systems. The focus is on analytical models, algorithms, andDSS applications. We comprehensively discussed the managerialtools (basically EVA) and examined both the progress in academicknowledge and the current needs of the practitioners. In theseregards, we emphasized the importance of embedding these

optimization methods in DSS and its integration to commonlyused project management software. We underlined that DSSshould be model driven and serve as an early warning system totrigger effective corrective actions. We also noted the componentsthat it should involve. The software integration is crucial for reducing the current gap between project management theory and

practice.To conclude, we highlight the research areas that are worth

further investigation and summarize some possible researchdirections.

There is a need to develop analytical models to forecast project performance more accurately especially studies on predicting performance based on new comprehensive EVA metrics andintegration of statistical control tools. Regarding EVA, there aresome limitations (Section 2.1) and new approaches that are moreflexible and suitable for more general cases are required to better explain the practice.

Optimal control theory could be further studied in control

systems design. Accordingly, integrated models could be devel-oped. How to combine scheduling/rescheduling and how tocontrol functions to facilitate information sharing, coordinationand effective resource allocation are open questions. The modelsand algorithms developed could constitute the foundations of DSS to determine the possible needs for corrective actions. Thesesystems should contain early warning mechanisms, and user friendly interfaces and be integrated to commercial software

packages. Both interface design and integration to softwaresystems are effectively challenging but important issues.

Finally further studies on modeling and solving complexreal life instances are needed. Efficient approximate algorithmsto solve portfolio management and, multi-objective program

management problems shall be developed.

Conict of interest

There is no conflict of interest.

Acknowledgments

This study was supported by The Scientific and TechnologicalResearch Council of Turkey under grant SOBAG 113K245.

References

Acebes, F., Pajares, J., Galan, J.M., Lopez-Paredes, A., 2014. A new approachfor project control under uncertainty. going back to the basics. Int. J. Proj.Manag. 32, 423–434.

Aliverdi, R., Naeni, L.M., Salehipour, A., 2013. Monitoring project durationand cost in a construction project by applying statistical quality controlcharts. Int. J. Proj. Manag. 31, 411–423.

Anbari, F.T., 2003. Earned value project management method and extensions.Proj. Manag. J. 34, 12–23.

Arauzo, J.A., Pajares, J., Lopez-Paredes, A., 2010. Simulating the dynamicscheduling of project portfolios. Simul. Model. Pract. Theory 18, 1428–1441.

Aytug, H., Lawley, M.A., McKay, K., Mohan, S., Uzsoy, R., 2005. Executing production schedules in the face of uncertainties: a review and some futuredirections. Eur. J. Oper. Res. 161, 86–110.

Azaron, A., Katagiri, H., Sakawa, M., 2007. Time–

cost trade-off via optimalcontrol theory in Markov PERT networks. Ann. Oper. Res. 150, 47–64.


http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0005http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0005http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0005http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0005http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0005http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0010http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0010http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0010http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0010http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0010http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0015http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0015http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0015http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0015http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0020http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0020http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0020http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0020http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0025http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0025http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0025http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0025http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0025http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0025http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0030http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0030http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0030http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0030http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0030http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0030http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0030http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0030http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0025http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0025http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0025http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0020http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0020http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0015http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0015http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0010http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0010http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0010http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0005http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0005http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0005


7/8

Bausch, G.T., Chung, C.A., 2001. A statistical project control tool for engineeringmanagers. Proj. Manag. J. 32, 37–44.

Bowman, R.A., 2006. Developing activity duration specification limits for effective project control. Eur. J. Oper. Res. 174, 1191–1204.

Buehlmann, U., Ragsdale, C.T., Gfeller, B., 2000. A spreadsheet-baseddecision support system for wood panel manufacturing. Decis. Support.Syst. 29, 207–227.

Caron, F., Ruggeri, F., Merli, A., 2013. A Bayesian approach to improveestimate at completion in earned value management. Proj. Manag. J. 44,3–16.

Chang, C.W., Tong, L.I., 2013. Monitoring the software development processusing a short-run control chart. Softw. Qual. J. 21, 479–499.

Cioffi, D.F., 2005. A tool for managing projects: an analytic parameterization of the S-curve. Int. J. Proj. Manag. 23, 215–222.

Cohen, I., Golany, B., Shtub, A., 2005. Managing stochastic, finite capacity,multi-project systems through the cross-entropy methodology. Ann. Oper.Res. 134, 183–199.

Davidovitch, L., Parush, A., Shtub, A., 2010. Simulator-based team training toshare resources in a matrix structure organization. IEEE Trans. Eng. Manag.57, 288–300.

Dey, P.K., 2001. Decision support system for risk management: a case study.Manag. Decis. 39, 634–649.

Dweiri, F., Kablan, M., 2006. Using fuzzy decision making for the evaluationof the project management internal efficiency. Decis. Support. Syst. 42,712–726.

El-Mashaleh, M.S., Chasey, A.D., 1999. Improving the cost and schedulecontrol system. Cost Eng. 41, 39–42.

Elshaer, R., 2013. Impact of sensitivity information on the prediction of project's duration using earned schedule method. Int. J. Proj. Manag. 31,579–588.

Eom, S., Kim, E., 1997. A survey of decision support system applications(1995–2001). J. Oper. Res. Soc. 57, 1264–1278.

Falco, M.D., Macchiaroli, R., 1998. Timing of control activities in project planning. Int. J. Proj. Manag. 16, 51–58.

Fang, C., Marle, F., 2012. A simulation-based risk network model for decisionsupport in project risk management. Decis. Support. Syst. 52, 635–644.

Farris, J., Groesbeck, R., Van Aken, E., Letens, G., 2006. Evaluating the

relative performance of engineering design projects: a case study using dataenvelopment analysis. IEEE Trans. Eng. Manag. 53, 471–482.

Fleming, Q., Koppelman, J., 2005. Earned Value Project Management, 3rdedition. Project Management Institute.

Golenko-Ginzburg, D., Gonik, A., 1997. On-line control model for networkconstruction projects. J. Oper. Res. Soc. 48, 175–183.

Hahn, G., Kuhn, H., 2012. Designing decision support systems for value-basedmanagement: a survey and an architecture. Decis. Support. Syst. 53, 591–598.

Hall, N.G., 2012. Project management: recent developments and researchopportunities. J. Syst. Sci. Syst. Eng. 21, 129–143.

Hans, E., Herroelen, W., Leus, R., Wullink, G., 2007. A hierarchical approachto multi-project planning under uncertainty. Omega 35, 563–577.

Hardie, N., 2001. The prediction and control of project duration: a recursivemodel. Int. J. Proj. Manag. 19, 401–409.

Hazir, O., Schmidt, K., 2013. An integrated scheduling and control model for

multi-mode projects. Flex. Serv. Manuf. J. 25, 230–254.Hazir, O., Shtub, A., 2011. Effects of the information presentation format on

project control. J. Oper. Res. Soc. 62, 2157–2161.Herroelen, W., 2005. Project scheduling — theory and practice. Prod. Oper.

Manag. 14, 413–432.Herroelen, W., Leus, R., 2005. Project scheduling under uncertainty: survey

and research potentials. Eur. J. Oper. Res. 165, 289–306.Kapur, P., Pham, H., Chanda, U., Kumar, V., 2013. Optimal allocation of

testing effort during testing and debugging phases: a control theoreticapproach. Int. J. Syst. Sci. 44, 1639–1650.

Kastor, A., Sirakoulis, K., 2009. The effectiveness of resource levelling toolsfor resource constraint project scheduling problem. Int. J. Proj. Manag. 27,493–500.

Khamooshi, H., Golafshani, H., 2014. EDM: earned duration management, anew approach to schedule performance management and measurement. Int.

J. Proj. Manag. 32, 1019–1041.

Kogan, K., Raz, T., Elitzur, R., 2002. Optimal control in homogeneous projects:analytically solvable deterministic cases. IIE Trans. 34, 63–75.

Kolisch, R., Padman, R., 2001. An integrated survey of deterministic project scheduling. Omega 29, 249–272.

Kwaka, Y.H., Ingall, L., 2007. Exploring Monte Carlo simulation applicationsfor project management. Risk Manag. 9, 44–57.

Lauras, M., Marques, G., Gourc, D., 2010. Towards a multi-dimensional project

performance measurement system. Decis. Support. Syst. 48, 342–353.Liberatore, M., Pollack-Johnson, B., 2003. Factors influencing the usage and

selection of project management software. IEEE Trans. Eng. Manag. 50,164–174.

Liberatore, M., Pollack-Johnson, B., Smith, C., 2001. Project management inconstruction: software use and research directions. J. Constr. Eng. Manag.127, 101–107.

Lipke, W., Zwikael, O., Henderson, K., Anbari, F., 2009. Prediction of project outcome: the application of statistical methods to earned value management and earned schedule performance indexes. Int. J. Proj. Manag. 27, 400–407.

Liu, X.F., Kane, G., Bambroo, M., 2006. An intelligent early warning systemfor software quality improvement and project management. J. Syst. Softw.79, 1552–1564.

Megow,N., Möhring, R.H., Schulz, J., 2011. Decision support and optimization inshutdown and turnaround scheduling. INFORMS J. Comput. 23, 189–204.

Meredith, J.R., Mantel, S.J., 2011. Project Management: A ManagerialApproach. John Wiley & Sons.

Naeni, L.M., Salehipour, A., 2011. Evaluating fuzzy earned value indices andestimates by applying alpha cuts. Expert Syst. Appl. 38, 8193–8198.

Narbaev, T., Marco, A.D., 2014. An earned schedule-based regression model toimprove cost estimate at completion. Int. J. Proj. Manag. 32, 1007–1018.

Nguyen, T.H., Marmier, F., Gourc, D., 2013. A decision-making tool tomaximize chances of meeting project commitments. Int. J. Prod. Econ. 142,214–224.

Nowicki, E., Zdrzalka, S., 1984. Optimal control policies for resource allocationin an activity network. Eur. J. Oper. Res. 16, 198–214.

Olson, D.L., 2008. Multi-criteria decision support. Handbook on DecisionSupport Systems 1. Springer, pp. 299–314.

Pajares, J., Lopez-Paredes, A., 2011. An extension of the EVM analysis for project monitoring: the cost control index and the schedule control index.

Int. J. Proj. Manag. 29, 615–621.Partovi, F., Burton, J., 1993. Timing of monitoring and control of CPM

projects. IEEE Trans. Eng. Manag. 40, 68–75.Plaza, M., Turetken, O., 2009. A model-based DSS for integrating the impact of

learning in project control. Decis. Support. Syst. 47, 488–499.Power, D.J., Sharda, R., 2007. Model-driven decision support systems:

concepts and research directions. Decis. Support. Syst. 43, 1044–1061.Raz, T., Erel, E., 2000. Optimal timing of project control points. Eur. J. Oper.

Res. 127, 252–261.Rozenes, S., Vitner, G., Spraggett, S., 2004. MPCS: multidimensional project

control system. Int. J. Proj. Manag. 22, 109–118.Rozenes, S., Vitner, G., Spraggett, S., 2006. Project control: literature review.

Proj. Manag. J. 37, 5–14.Sethi, S., Thompson, G., 2002. Optimal Control Theory: Applications to

Management Science and Economics, 3rd edition. Kluwer Academic

Publishers.Shim, J., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R., Carlsson, C.,

2002. Past, present, and future of decision support technology. Decis.Support. Syst. 33, 111–126.

Shtub, A., Bard, J.F., Globerson S., S., 2005. Project Management: Processes,Methodologies, and Economics, 2nd edition. Pearson, Prentice Hall .

Shtub, A., Parush, A., Davidovitch, L., 2006. Simulation-based learning inengineering education: performance and transfer in learning project manage-ment. J. Eng. Educ. 95.

Slowinski, R., Soniewicki, B., Weglarz, J., 1994. DSS for multiobjective project scheduling. Eur. J. Oper. Res. 79, 220–229.

Subramanian, N., Ramanathan, R., 2012. A review of applications of analytic hierarchy process in operations management. Int. J. Prod. Econ.138, 215–241.

Tavares, L., 2002. A review of the contribution of operational research to

project management. Eur. J. Oper. Res. 136, 1–18.


http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0035http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0035http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0035http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0035http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0040http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0040http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0040http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0040http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0045http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0045http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0045http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0045http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0045http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0050http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0050http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0050http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0050http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0050http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0055http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0055http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0055http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0055http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0055http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0060http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0060http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0060http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0060http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0065http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0065http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0065http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0065http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0065http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0070http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0070http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0070http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0070http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0070http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0075http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0075http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0075http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0075http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0075http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0080http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0080http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0080http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0080http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0080http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0085http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0085http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0085http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0085http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0090http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0090http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0090http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0090http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0090http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0095http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0095http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0095http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0095http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0095http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0095http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0100http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0100http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0100http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0100http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0105http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0105http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0105http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0105http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0110http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0110http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0110http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0110http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0110http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0380http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0380http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0385http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0385http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0385http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0385http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0385http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0115http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0115http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0115http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0115http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0115http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0120http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0120http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0120http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0120http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0125http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0125http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0125http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0125http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0130http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0130http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0130http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0130http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0135http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0135http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0135http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0135http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0140http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0140http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0140http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0140http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0145http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0145http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0145http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0145http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0145http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0145http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0150http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0150http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0150http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0150http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0155http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0155http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0155http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0155http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0155http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0160http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0160http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0160http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0160http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0160http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0390http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0390http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0390http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0390http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0390http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0170http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0170http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0170http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0170http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0170http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0175http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0175http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0175http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0175http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0180http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0180http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0180http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0180http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0180http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0185http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0185http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0185http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0185http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0190http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0190http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0190http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0190http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0190http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0190http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0195http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0195http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0195http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0195http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0195http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0200http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0200http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0200http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0200http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0200http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0200http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0205http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0205http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0205http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0205http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0205http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0210http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0210http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0210http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0210http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0215http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0215http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0220http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0220http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0220http://refhub.elsevier.com/S0263-7863(14)0014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63(14)00142-2/rf0085http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0080http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0080http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0080http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0075http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0075http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0070http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0070http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0070http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0065http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0065http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0065http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0060http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0060http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0055http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0055http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0050http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0050http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0050http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0045http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0045http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0045http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0040http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0040http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0035http://refhub.elsevier.com/S0263-7863(14)00142-2/rf0035


8/8

Trautmann, N., Baumann, P., 2009. Resource-constrained scheduling of a real project from the construction industry: a comparison of software

packages for project management. IEEM 2009. IEEE InternationalConference on Industrial Engineering and Engineering Management, pp.628–632.

Trietsch, D., Baker, K.R., 2012. PERT 21: fitting PERT/CPM for use in the 21st century. Int. J. Proj. Manag. 30, 490–502.

Tseng, C.C., 2011. Statistical analysis for comparison of overall performance of projects using Weibull analysis on earned value metrics. J. Chin. Inst. Ind.Eng. 28, 165–178.

Vandevoorde, S., Vanhoucke, M., 2006. A comparison of different project duration forecasting methods using earned value metrics. J. Oper. Res. Soc.24, 289–302.

Vanhoucke, M., 2009. Measuring Time — Improving Project PerformanceUsing Earned Value Management. Springer .

Vanhoucke, M., 2010. Using activity sensitivity and network topologyinformation to monitor project time performance. Omega 38, 359–370.

Vanhoucke, M., 2011. On the dynamic use of project performance and schedulerisk information during project tracking. Omega 39, 416–426.

Vanhoucke, M., 2012. Measuring the efficiency of project control usingfictitious and empirical project data. Int. J. Proj. Manag. 30, 252–263.

Vanhoucke, M., 2013. An overview of recent research results and futureresearch avenues using simulation studies in project management. ISRNComput. Math. http://dx.doi.org/ 10.1155/2013/513549 .

Vanhoucke, M., Vandevoorde, S., 2007. A simulation and evaluation of earnedvalue metrics to forecast the project duration. Int. J. Proj. Manag. 58,1361–1374.

Vitner, G., Rozenes, S., Spraggett, S., 2006. Using data envelope analysis to

compare project efficiency in a multi-project environment. Int. J. Proj.Manag. 24, 323–329.

Weglarz, J., 1981. Project scheduling with continuously-divisible, doublyconstrained resources. Manag. Sci. 27, 1040–1053.

Wi, H., Jung, M., 2010. Modeling and analysis of project performance factors inan extended project-oriented virtual organization (EPROVO). Expert Syst.Appl. 37, 1143–1151.

Williams, T., 1999. Towards realism in network simulation. Omega 27, 305–314.Williams, T., 2003. The contribution of mathematical modelling to the practice

of project management. IMA J. Manag. Math. 14, 3–30.Zwikael, O., Ahn, M., 2011. The effectiveness of risk management: an analysis

of project risk planning across industries and countries. Risk Anal. 31,25–37.

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