AGILE PARTNER SELECTION: A HIERARCHICAL MODEL AND ...€¦ · agile. The use of the hierarchical structure as a decision support model to aid decision makers in selecting suitable

INTERNATIONAL JOURNAL OF INFORMATION AND SYSTEMS SCIENCES Volume 1, Number 1, Pages 1-22

©2005 Institute for Scientific Computing and Information

AGILE PARTNER SELECTION: A HIERARCHICAL MODEL AND EMPIRICAL INVESTIGATION

JUN REN, YAHAYA Y. YUSUF AND NEIL D. BURNS

Abstract Within the operational function of an Agility-based company, one of the prime responsibilities is the evaluation and selection of business partners. This paper proposes a decision-making methodology and a hierarchical model for the selection of agile partners. The methodology allows for the evaluation of partners to help organisations become more agile. The use of the hierarchical structure as a decision support model to aid decision makers in selecting suitable agile partners is demonstrated in this paper. The block diagram of the selection is established and a set of agile decision domains and attributes is identified. In order to provide empirical evidence for the proposed model and selection program, a questionnaire survey is conducted. Based on the questionnaire survey, the weights of agile dimensions, decision domains and attributes are determined. The reliability analysis reveals that all agile attributes show a high level of internal consistency, thus they provide a validated base for the research. A test case is used to illustrate the methodology as well. The paper ends with a discussion of managerial implications and directions for future research. Key Words, Partner selection, Decision making method, Agile enterprise, Empirical study

1. Introduction

Agility describes a company that is able to change and adapt quickly to changing circumstances. In the words of one of the early proponents of the concept, “Agility is dynamic, context specific, aggressively change embracing, and growth oriented. It is not about improving efficiency, cutting costs, or battening down the business hatches to ride out fearsome competitive storms. It is about succeeding and about winning profits, market share and customers in the very centre of competitive storms that many companies now fear” [5]. Increasingly agile manufacturing is attracting greater attention from both academic and industrial communities. As a result, a number of programs are being conducted on relevant issues to propagate agile manufacturing concepts, build agile enterprise prototypes and form an agile industry eventually.

Individual company, however, cannot become agile without agile partners. Partners

form part of company’s supply chain, therefore partner selection plays an important role in supply chain management. The positive impact of the partner selection on a firm’s supply chain performance has been reported from many industries. Chrysler, for instance, strengthened its leadership position in automobile industry by launching a partner (supplier) involvement program, Supplier Cost Reduction Effort (SCORE). Chrysler announced that it achieved more than US$1.2 billion in cost savings through 1997 due to the SCORE program. Another example is Honeywell Industrial Automation and Control, the company reported a 90% reduction of product defect rates based on its excellent practice of partner selection and supply chain management program during the period of 1990 through 1996 [18].

Received by the editors January 10, 2004 1

2 J. REN, Y. Y. YUSUF AND N. D. BURNS

Though there is a wide acceptance of the importance of partner selection in supply chains, the existing studies are conceptual in nature or based on a few case studies. The model and program employed to assess a partner’s operations strategy in terms of Agility using empirical data are still rare. In this review, the focus of this research is on the model establishment and criteria selection. In a sample of 105 leading manufacturers in UK, it is empirically examined that how industry managers treat each criterion when they actually choose business partners. In doing so, previous studies carried out by the authors [14-26] provide the opportunity to use attribute-dependent criteria to identify potential agile companies that are deserving of further collaboration. Through easy-to-use software, expert judgement on key criteria could be obtained and processed to provide scores for each company which are then used to rank companies in order of preference. Furthermore, knowledge acquired through the empirical analysis can be passed on to, and processed by, an agile decision making system [25]. This will build a solid foundation to develop an agile decision making system which should comprise decision methods and database that incorporate external databases, heuristic decision principle obtained from experts, and other external information to yield even more powerful predictions for agile partners.

This paper is organised as follows. First, the partner selection literature is reviewed.

The methodology is then described, including the partner selection process, sampling process and partner selection model. The reliability of these agile attributes is also examined and following that, the weights of each element are calculated based on questionnaire survey. Finally, an example problem is solved, validating the established methodology.

2. Literature review

Global competition is forcing corporations to look at their partnership map to reduce costs and time involved in the process. This is done periodically. The arrangement art of relationship management is now crucial to business success. A solid partnership helps corporations in gaining significant advantages over their global competitors. In the literature, partner selection research often starts at choosing criteria in particular organisation or industry. The literature review of this paper will investigate both criteria selection and decision making method selection which are the key issues in agile partner selection.

2.1 Partner selection criteria

Many authors [1][2][3][11][21][22][24] investigated how to choose decision criteria. For example, Angeles and Nath [2] identified the trading partner selection criteria used by firms in a customer-supplier pair. By using factor analysis, the authors grouped all possible criteria into six critical factors that can be used as selection criteria: strategic commitment, trading partner flexibility, joint partnering, readiness, infrastructure, and communications. In consistent with the factor analysis, the authors conducted statistical analysis MANOVA and t-tests to test differences in terms of the responses of customer and supplier firms to the selection criteria.

Tatoglu [22] examined partner selection criteria using a typology that distinguishes

between partner-related and task-related selection criteria. The author depicted the two different methods regarding selection criteria and used partner-related strategy to

A HIERARCHICAL MODEL AND EMPIRICAL INVESTIGATION 3

investigate the motivation for joint venture formation for a sample of Western partner firms. The advantage of the partner-related selection criteria was demonstrated through the process of identification for relationship between the relative importance of selection criteria and the nationality of foreign partner.

Al-Khalifa and Peterson [1] argued that the motivation for partner selection must be

distinguished from that needed to enter into a joint venture, and the latter may be considered the “ends” dimension of motivation while the former is concerned with “means”. The research recognised the importance to distinguish between “task related” factors and “partner related” factors in analysing the partner selection process. Based on a survey of 42 international joint ventures, respondents rank partner related factors as significantly more important than task factors in selecting a partner. The existence of partner related factors as a separate construct was also confirmed by the authors via factor analysis.

Luo [11] investigated the success of international joint ventures investing in P.R. China,

largely depends on the selection of local partners. The study illuminated various partner selection criteria that are important to the survival and growth of foreign companies active in P.R. China. Specifically, the study emphasized three categories of criteria: strategic, organisational, and financial. The survey concluded that a partner with superior strategic traits but lacking strong organisational and financial characteristics may result in an unstable joint venture. The possession of desirable organisational attributes without corresponding strategic and financial competence may leave the joint venture unprofitable. A partner with superior financial strengths without strategic and organisational competencies can lead to an unsustainable venture.

Talluri et al. [21] suggested that multi-organisational structures could be viewed as a

solution for rapid introduction of products while maintaining high quality and minimal costs. One key issue in designing these new forms of organisations is the partner selection process. The business processes, owned by organisational partners, must be efficient both individually and as a collective group. The author proposed a two-phase quantitative framework to aid the decision making process in effectively selecting an efficient and a compatible set of partners. Phase 1 identifies efficient candidates for each type of business process (e.g. design, manufacturing, distribution, etc.) utilising data envelopment analysis. Phase 2 involves the execution of an integer goal-programming model to determine the best portfolio of efficient partners based on a number of compatibility objectives.

2.2 Partner selection method

Agile partner selection involves many aspects including agile dimensions, decision domains and agile attributes. Each aspect may involve many factors, each factor may be further broken down to smaller factors (sub-factors). In addition, some elements (factors and sub-factors) can be measured quantitatively whilst others may only be described qualitatively. Apparently traditional methods cannot deal with such multi-level and multi-criterion problem (Marlow W.H., 1993). This leads to the multi-criteria decision analysis (MCDA). The MCDA methodology is made up of four steps: (1) identifying the decision problems, (2) structuring the preferences, (3) aggregating the alternative evaluations and (4) making recommendations. Traditional MCDA methods include the


categorical method, weighted-point method, matrix approach, and multiple objective programming (MOP) such as goal programming. The categorical method rates companies on a number of equally-weighted factors and then allows the decision maker to evaluate the company with the total score. This method is simple and easily understandable, but it often introduces subjective error into the decision and oversimplifies the selection scenario by equally weighting all factors [10]. The matrix approach evaluates and selects companies based on weighted scores obtained from a set of pre-determined benchmarks [19]. To improve this matrix approach further, some authors suggested a performance matrix approach that can measure the company’s performance under various unforeseen scenarios by estimating the probable deviations from the original goal that the company needs to obtain [23]. Regardless of their strengths, none of these approaches can systematically measure both qualitative and quantitative factors and structure complex problems with a large number of criteria, attributes and alternatives.

2.3 Hierarchical model and Analytic Hierarchy Process (AHP)

From a review of the literature on criteria selection and decision making method selection, this paper built on the existing body of knowledge on the attributes of an agile organisation to use hierarchical model as the tool to assist managers in selecting agile partners. The most important reason to choose hierarchical model was the usage of Analytic Hierarchy Process (AHP). AHP has the advantage of simplicity of its procedures. Partovi [20] regarded this simplicity as one of the benefits of using AHP. In fact, during the research it was noticed that the AHP-based method is simple enough to be explained on and well accepted by different levels of the company’s hierarchy. Secondly, the easiness of the use makes AHP much more probable than a method that requires higher education from the people who are using it. In fact, AHP is a popular management science tool that was developed more than two decades ago [16]. It enables managers to make potentially more effective decisions by structuring and evaluating the relative attractiveness of competing options for any type of managerial decision. Although AHP frequently has been used in a variety of decision scenarios, it has not been used in the area of agile partner selection, even though other researchers have noted the usefulness of AHP in such contexts [7].

3. Research methodology 3.1 Research methodology statement

Agility is an organisation’s capacity to respond rapidly and effectively to unanticipated opportunities and to proactively develop solutions for potential needs. In order to establish and maintain this capability, manufacturing companies are forced to create agile business processes. Among them, one of the key issues is to select agile partners. The selection process involves many elements including different dimensions, decision domains and organisational attributes. Since the elements have systemic characteristics, they may be integrated into one model. Some authors [6][14] have considered the potential need for such a model. Our study was to provide decision maker such a model to select suitable partners.

In the first phase of this study, based on literature review, the elements on different

decision levels were identified to describe different aspects of the potential partners. In this phase a five level hierarchical structure was produced. This hierarchical structure can


help decision maker get judgement easily. At the conclusion of this phase, the four main dimensions were identified: enriching the customer, co-operation, mastering change and uncertainty and leveraging the impact of people, information and technology. The ten decision domains with thirty-two relevant organisation attributes were also examined.

In the second phase of the research, the identified elements on each level of the hierarchical structure were included in a questionnaire survey designed for this study. The collected data were analysed with SPSS software package. The dominant features on each level of agility structure were identified based on the statistical analysis. The results of this phase delivered the weights of each element and that provided managers a comparative standard to make their own judgement.

The last phase is a simple case study problem used to illustrate the proposed

methodology.

3.2. The sample The sample profile given in Table 1 shows the number of employees at site, annual

turnover and business category of the firms. The sample consisted of 105 leading companies in the UK. The table shows that companies with less than £30 million annual turnover represented about 60 percent of all respondents. Those with fewer than 500 employees accounted for 68.6 per cent of the total, indicating an industry structure dominated by SMEs. While respondents are well distributed across industrial sectors, there are two dominant groups: mechanical engineering, and Automotive. These two groups combined represent 44.7 per cent of the sample.

Table 1 Profile of sample size and business category

Number of employees % Turnover (£ million) % Business sectors %

Less than 50 1.9 Less than ten 16.2 Aerospace and Defence 4.8

50-250 43.8 10-20 30.5 Automotive 19.0

251-500 22.9 21-30 13.3 Pharmaceuticals 2.9

501-1000 14.3 31-50 20.0 Electrical/electronics 1.9

1001-2000 9.5 51-100 4.8 Computer/Telecommunications 8.6

Over 2000 5.7 101-200 2.9 Construction 1.9

201-500 7.6 Plastics, Rubber and Polymers 2.9

Over 500 3.8 Steel 2.9

Foods & tobacco 6.7

Wood, paper & textiles 1.0

Mechanical Engineering 25.7

Light engineering 1.0

Chemistry 4.8

3.3 Data collection

In this research, we developed the survey instrument for the study by searching relevant literature to review the instruments and perspectives adopted by researchers who had studied similar issues. The questionnaire was pre-tested and pilot-tested.


Pre-testing was carried out by discussing the questionnaire with colleagues working in the same area in the department. The pilot-testing was conducted via email and Fax. From the pre-tested and piloted-test results, we found that the initial questionnaire was too large, some questions were not clear, and the layout needed to be rearranged. The questionnaire was accordingly modified. The hard copies of the questionnaire were mailed out to addresses taken from the European Business Directory. A covering letter which described the objectives of the research and procedures for completing the questionnaire, and a stamp addressed envelope was enclosed to facilitate returning of the questionnaire. A telephone reminder was made to some non-respondents two weeks after the questionnaires were mailed out. We targeted chief executive officers (CEOs) and senior managers to serve as respondents. Of the 1400 questionnaires mailed, 185 were returned with 105 valid for analysis. The overall response rate was therefore about 13.2 percent. To make sure the questionnaire meets the research needs in a useful way, internal consistency reliability was analysed to determine the extent to which the elements in the questionnaire are related to each other. Actually, internal consistency reliability is a measure in assessing survey instruments and scale. It is the indicator of how well the different elements measure the same issues [12]. High internal consistency of the attributes is necessary to ensure the accuracy or precision of the attribute. Cronbach’s coefficient alpha (α) is most commonly used to assess the scale reliability. Normally the value of Cronbach’s coefficient alpha (α) is between 0 and 1 and a higher level of α indicates a higher reliability of the scale. Lau [9] stated that as a rule, alpha levels as low as 0.6 are acceptable. Meanwhile it would be difficult to justify a proposed indicator of a latent variable if its reliability measures were less than 0.5. In this research, as shown in Table 2, all α values range between 0.7327 and 0.9573 and thus all attributes show a relatively high level of internal consistency to the domains. Table 2. Attributes list with Cronbach Coefficient (alpha)

Decision domains Cronbach Coefficient (alpha)

Integration 0.7915

Competence 0.7327

Team building 0.7577

Technology 0.8654

Quality 0.8166

Change 0.8632


Partnership 0.8274

Market 0.7988

Education 0.9573

Welfare N/A

4. Model and analysis 4.1 Partner selection process Based on the literature review, a detailed partner selection process is proposed as shown in Figure 1. The first step in the selection is the creation of a decision-making team. The team members are chosen from different levels of the organisation involved with the selection process to widen the points of views in the process. After choosing the decision-making team and establishing the general selection guidelines, the next two steps are to choose pertinent decision domains and attributes, and to establish hierarchy structure based on the company’s strategy. At this stage, important guidelines have been suggested by Satty [17]:

• representing the problem as thoroughly as possible; • considering the environment surrounding the problem; • identifying the issues or attributes that contribute to the solution; and • clarifying the necessary participants associated with the problem.

Step 1:Creation of selection team(partners need identified and justified)

Step 3: Establish AHP structure

Step 2: Identify pertinent domains and attributes

Step 4: rank eachpotential partners

Step 5: Analyse comparative results

Step 6: Identify preferred partner(s) and Finalrecommendations

F i g u r e 1 A g i l e p a r t n e r s o l u t i o n p r o c e s s


In step 4, the priority weights of each alternative are then determined through pair-wise comparison or other way like questionnaire survey. The weight of the corresponding higher level element is then used to weight the elements at the lower level in the hierarchy (composite weight). The procedure is repeated by moving downward along the hierarchy, computing the weight of each element at a particular level and using these to determine composite weights for succeeding levels. Then it is necessary to analyse comparative results that include weight analysis and consistency. Weight analysis can be used to assess the reliability of the weight. The priority weights of each element are the eigen-values in the corresponding eigen-vector of each matrix. The consistency of the data may be investigated during the analysis. The composite indices of the previous matrix represent the consistency of the data in the matrix with regard to the eigen-values. In the final step of the proposed diagram, the partner that has the highest overall priority score is identified as the preferred one. The selection decision process ends when final recommendations are made concerning the most suitable partners. 4.2. Hierarchical model Based on the above analysis, a five level hierarchical model is devised (see Figure 2). The first level sets the main objective, here referred to as the agile partner selection. The main objective (i.e. referred to as the main considerations) is divided into four main dimensions or sub-objectives, which are enriching the customer (EC), co-operation (CO), mastering change and uncertainty (MCU) and leveraging the impact of people, information and technology (LI). As shown in the figure 2, the first dimension is enriching the customer. This entails a quick understanding of the unique requirements of each individual customer and rapidly providing it. The second dimension entails co-operation (intra-organisational, inter-organisational co-operation such as supplier partnerships and virtual relationships with competing organisations) in order to enhance competitiveness. The third dimension utilises new organisational structure(s) to master change and uncertainty through techniques such as concurrent engineering and cross-functional teams. The fourth dimension leverages the impact of people, information and technology and recognises the importance of employees as a company asset, placing greater emphasis on education, training and empowerment. For further details of these four dimensions, see Goldman et al [5]. The third level hierarchy includes ten decision domains and the fourth level consists of


thirty-two attributes [26]. In fact, to achieve agility in terms of the four dimensions, some decision domains with related attributes must be carefully identified. Team building, for example, is a key concept in manufacturing strategies and a critical decision domain. The associated attributes involve: Empowered individuals working in teams; Cross functional teams; Teams across company borders and Decentralised decision making. Empowerment enables workforce to make decision and work autonomously. That speeds up business processes and creates an environment in which innovation may thrive; Cross functional teams solves problems using a team of people from different business disciplines enabling better, broader and more robust solutions to be devised; Encouragement of teaming with other customers, even competitors allows companies to react quickly to market changes at low cost; Decentralised decision making speeds up operations, allows for workforce innovation, maintains information flow at a workable level. The ten decision domains and the associated attributes are shown in Table 3. Finally, at the fifth level of the hierarchy, there are some potential partners referred to as alternatives 1, 2, 3, …, and n, which are to be assessed and compared. Table 3 Domains and attributes (with Abbreviations used)

Decision

domains

Attributes with abbreviation

Integration Concurrent execution of activities(CEA) ,Enterprise integration(EI), Information accessible to employees(IAE);

Competence Multi-venturing capabilities(MVC), Developed business practice difficult to Copy(DBPD);

Team building Empowered individuals working in teams(EIW), Cross functional teams(CFT), Teams

across company borders(TACB), Decentralised decision making(DDM);

Technology Technology awareness(TA), Leadership in the use of current technology(LD), Skill and

knowledge enhancing technologies(S&KT), Flexible production technology(FPT);

Quality Quality over product life(QPL), Products with substantial value-addition(PVA), First-time

right design(FRD), Short development cycle times(SDCT);

Change Continuous improvement (CONI), Culture of change (CLC);

Partnership Rapid partnership formation (RPF), Strategic relationship with customers (SRC), Close

relationship with suppliers(CRS), Trust-based relationship with customers/suppliers(TBR);

Market New product introduction (NPI), Customer-driven innovations (CDI), Customer

satisfaction (CS), Response to changing market requirements(RCMR);

Education Learning organization (LO), Multi-skilled and flexible people (MS&FP), Workforce skill

upgrade(WSU), Continuous training and development(CT&D);

Welfare Employee satisfaction(ES). Source: Yusuf et al [26]


4.3. Statistical analysis On the basis of developed hierarchical model shown in Figure 3, the elements on all levels of the model were suggested to which respondents indicated the level of importance. Five possible levels of importance were asked to be identified, including not important, slightly important, important, very important and absolutely important. Each of these importance levels was coded into SPSS as 1, 3, 5, 7, and 9 respectively. By calculating the means, the weights can be computed by the following formula :

∑=

=

n

jj

i

M

MiW

1

Where: Wi = weight of the i th element; Mi = mean of the i th element; n = number of elements

Agile Enterprise

EC CO LIMCU

Integratio WelfwareEducationMarketPartnershiChangeQualityTechTeamCompeten

CEA QPL CONI RPF NPI ESLOTAEIWMVC

EI

IAE

DBPD CFT

TACB

DDM

LD

S&KT

FTP

PVA

FRD

SDCT

CLC SRC

CRS

TBR

CDI

CS

RCMR

MS&FP

WSU

CT&D

Alternative 1 Alternative 2 ...... Alternative N

F ig u r e 2 A g ile P a r tn e r S e le c tio n M o d e l


Dimensions The mean values of the level of dimension are shown in Table 4. It can be seen from the table that the mean importance of all the four dimensions was greater than 5. That reflected that respondents thought all the four dimensions are important. It is not surprising that enriching the customer and industrial co-operation are highly ranked. A customer focus entails directing the efforts of the firm to meeting customer needs and wants. Recent research shows that enriching customer has been propelled by an increasingly competitive global business environment, accelerating technological developments which have shortened product life cycles and difficulty of many organisations in sustaining superior performance [8]. Industrial co-operation is also a key issue in modern operational strategy. A single organisation may not be able to respond quickly to changing market requirements. Industrial co-operation based on core competencies of firms will help to improve the flexibility and responsiveness of organisations. Appropriate strategies/methods such as communication, training and education, and strategic alliances can be adopted for an effective co-ordination and integration of participating firms at different levels of co-operation [6]. Table 4 Means and weights for agile dimensions

Dimension Mean Std.Deviation weight

Enriching the customer 5.57 2.03 0.2639

Internal and external Co-operation 5.39 1.67 0.2552

Mastering the changing 5.04 1.84 0.2387

Leveraging the impact of people,

information and technology

5.11 1.71 0.2421

Decision domains The mean values of the level of decision domains are shown in Table 5 in rank order. As shown in the table, the mean values range from 5.027 to 6.257, ensuring the set of decision domains was comprehensive and meaningful. Four domains were identified as the dominant features: team building; quality, partnership and market. They took weights 0.1091, 0.1051, 0.1047 and 0.1025 respectively. Apparently, team building and partnership are highly co-operation related. Team building focuses on intra-organisational co-operation, and partnership focuses on inter-organisational co-operation. It is consistent with the above dimension analysis that market is strongly highlighted by respondents regarding enriching the customer.


Table 5 Means and weights for decision domains Decision domains Rank Mean Std. Deviation weight

Team building 1 6.257 2.070 0.1091

Quality 2 6.037 1.837 0.1052

Partnership 3 6.007 1.852 0.1047

Market 4 5.882 1.845 0.1025

Competence 5 5.687 1.765 0.0991

Technology 6 5.687 1.887 0.0991

Integration 7 5.635 1.715 0.0982

Change 8 5.580 1.912 0.0973

Education 9 5.545 1.890 0.0966

Welfare 10 5.027 1.830 0.0876

Agile attributes The thirty-two attributes are set in rank order in Table 6. The high-ranked group can be identified as: Customer satisfaction (CS), Technology awareness (TA), Leadership in the use of current technology (LD), New product introduction (NPI), Teams across company borders (TACB), Strategic relationship with customers (SRC), Skill and knowledge enhancing technologies (S&KT), Cross functional teams (CFT), Continuous improvement (CONI) and Trust-based relationship with customers/suppliers (TBR). The ten attributes are from six decision domains: team building, technology, change, partnership, market and education. It is interesting that none of quality related attributes are included in this set. The highest rank attribute regarding quality is Products with substantial value-addition (PVA), only stands the fourteenth position on the list. Perhaps quality should be treated collectively or individually to obtain a competitive advantage rather than divided to many attributes. Another reason is maybe dividing quality into some attributes is difficult since such dividing is often coloured by a particular managerial situation or problem [4]. Despite all of this, it is clear from the table that the high-ranked set of criteria reflects the respondents’ concern with understanding the nature of the agility and ensuring adequate foundation to competitive bases. The understanding of the agility is thus the major concerns of the agile partner selection criteria. It may be conjectured that this set of selection criteria is the underlying motivation for agility formation. Table 6 Means and weights for agile attributes


Attributes Rank Mean Std.D weight Attributes Rank Mean Std.D Weight

CS 1 6.34 1.97 0.0370 EI 17 5.30 1.43 0.0309

TA 2 6.16 1.71 0.0359 QPL 18 5.30 1.87 0.0309

LD 3 5.88 1.88 0.0343 CEA 19 5.27 1.77 0.0307

NPI 4 5.84 1.95 0.0341 CT&D 20 5.27 1.60 0.0307

TACB 5 5.77 1.66 0.0336 ES 21 5.27 1.51 0.0307

SRC 6 5.73 1.82 0.0334 CDI 22 5.25 1.86 0.0306

S&KT 7 5.70 1.78 0.0332 IAE 23 5.20 1.70 0.0303

CFT 8 5.66 1.60 0.0330 FRD 24 5.16 2.45 0.0301

CONI 9 5.59 1.88 0.0326 MS&FP 25 5.16 1.85 0.0301

TBR 10 5.59 1.91 0.0326 LO 26 5.09 1.73 0.0297

RCMR 11 5.52 1.74 0.0322 WSU 27 5.05 1.67 0.0294

CLC 12 5.45 1.95 0.0318 RPF 28 5.02 1.81 0.0293

MVC 13 5.41 1.75 0.0315 DBPD 29 4.70 2.06 0.0274

PVA 14 5.41 2.02 0.0315 SDCT 30 4.64 2.40 0.0270

CRS 15 5.38 1.51 0.0314 DDM 31 4.59 2.33 0.0268

FPT 16 5.36 2.44 0.0312 EIW 32 4.20 2.10 0.0245

The second group of selection criteria can be identified which are of intermediate rank: Response to changing market requirements (RCMR), Culture of change (CLC), Multi-venturing capabilities (MVC), Products with substantial value-addition (PVA), Close relationship with suppliers (CRS), Flexible production technology (FPT), Enterprise integration EI, Quality over product life (QPL), Concurrent execution of activities (CEA), Continuous training and development (CT&D). This group reflects a concern with the operational requirement of the agile company. These operational concerns are clearly secondary. 4.4. Managerial implications In this study, we have attempted to give empirical evidence to provide manufacturing managers a practical methodology for selecting suitable partners. A five-level hierarchical structure has been produced here. It has been clear that the structure can be used to help decision makers get judgement easily. The methodology suggests the need for identification for relative importance of each element on the different hierarchical levels. Linked to these is the need to choose a calculation instrument to meet the hierarchical structure. Thus AHP is highly recommended. However, different companies are living in different competitive circumstances and thereby different important values (weights) could be given to each AHP elements. The conducted questionnaire survey


gave companies a guideline of how other companies treat selection criteria. The survey has highlighted enriching the customer and industrial co-operation as the key agile dimensions. Also, team building; quality, partnership and market were examined as major decision domains. The significant contributions of high-ranked group of organisation attributes (i.e. Customer satisfaction (CS), technology awareness (TA)) were expected. However, the reason for an insignificant result for quality related attributes deserves further investigation. In light of the empirical evidence produced in this study and that found in existing literature, we established the hierarchical structure of agile partner selection and empirically recognised the weights of each AHP-based element. The outcomes of the research provide decision makers a practical tool when they have to choose partner. In fact, the hierarchical structure made the complex selection process easy to be understood; the established weight values gave a clear imagination of the relative importance of each selection criteria. As will be seen in the next section, a decision may be made easily using our proposed methodology. However, people could argue that the weight values or even the structure of the model may vary considering their own situation. Alternatively we will provide them a software package tool based on AHP. By using the tool decision makers can develop their own hierarchical structure and figure out the weights values. 4.5. Case study of a partner selection scenario The best way to test and justify the proposed hierarchical model and selection procedure is to imagine trying to model a situation which reflects what actually happens in real world. For demonstration purposes, supposing three potential partners (Partner1, Partner 2 and Partner 3) have been proposed for consideration. As an example, the first comparison matrix in Appendix A is briefly explained here. Number 4 in row 2, column 1 is assigned to partner 2 as this is envisaged to have a higher preference (about important) than partner 1. Number 5 in row 1, column 3 is chosen to indicate partner 1 is important than partner 3 regarding the integration level of Concurrent execution of activities. Number 7 in row 2, column 3 is chosen to indicate that partner 2 is obvious important than partner 3. To form the comparison matrix, the preference rates indicated above are placed in front of each factor to form the matrix. The remaining matrices can be obtained in the same manner, based on the judgement of experts from company. The pair-wise comparison data are summarised on the basis of Satty’s eigenvector procedure, in the absolute priority weights that will be used to calculate the overall score of each elements. The pair-wise comparison data are translated into the absolute values


by solving the following matrix equation: A * AW = k* AW Where: A = the pair-wise comparisons matrix; AW = the vector of the absolute values; k = the highest of the eigen-values of the matrix A. Appendix Table A reports the paired comparison data and the absolute ratings of the partner candidates regarding particular agile attributes. The overall priority score for each partner candidates is computed by multiplying the rating score of the partner concerning the given factor by the priority weight of corresponding factor and summarising these products. The final result for the example is shown in Table 7. As a result, Partner1 is the preferred partner because it has the highest score (0.4293) among the three candidates. Partner2 is the next best partner with the next highest score (0.3590). 5. Conclusions The need for developing an agile partner selection process is a continuous task. The selection model presented in this paper is aimed to aid decision makers in the turbulence competitive environment. The proposed methodology integrated various characteristics of Agility (agile dimensions, decision domains and attributes) and their relationships. It has been proven that the methodology will be beneficial in considering different characteristics on different levels. This technique has also been proven useful for structuring the decision in the decision maker’s mind. The proposed structure provides for simplification of a very difficulty decision with many dimensions. However, the large number of pair-wise comparison can be tedious for decision makers. In our example problem, 288 pair-wise comparisons were required. If we choose five partner candidates, the pair-wise comparison will increase to 800. Due to time and effort required for a typical AHP process, an application of this methodology should be targeted at more strategies decision. Practically, the methodology envisaged in this research will not only helps in agile partner selection but also in measuring different kind of partners and thus ensures protection of investment. Acknowledgments This work forms part of the project supported by the UK Engineering and Physical Science Research Council (EPSRC) under Grant Reference GR/M58085.


Table 7 Overall rating of three partners Factor Priority

(Wj) Partner1(r1j) Partner2(r2j) Partner3(r3j)

CEA 0.0307 0.244 0.687 0.069

EI 0.0309 0.559 0.383 0.058

IAE 0.0303 0.524 0.342 0.134

MVC 0.0315 0.691 0.149 0.160

DBPD 0.0274 0.312 0.594 0.094

EIW 0.0245 0.651 0.127 0.223

CFT 0.0330 0.625 0.092 0.283

TACB 0.0336 0.098 0.817 0.715

DDM 0.0268 0.240 0.483 0.273

TACB 0.0359 0.344 0.067 0.589

LD 0.0343 0.218 0.715 0.067

S&KT 0.0332 0.256 0.679 0.064

FPT 0.0312 0.570 0.087 0.333

QPL 0.0309 0.095 0.655 0.250

PVA 0.0315 0.312 0.594 0.094

FRD 0.0301 0.691 0.120 0.189

SDCT 0.0270 0.676 0.077 0.247

CONI 0.0326 0.110 0.609 0.280

CLC 0.0318 0.299 0.414 0.287

RPF 0.0293 0.344 0.067 0.589

SRC 0.0334 0.642 0.285 0.072

CRS 0.0314 0.559 0.383 0.058

TABR 0.0326 0.778 0.111 0.111

NPI 0.0341 0.667 0.167 0.167

CDI 0.0306 0.335 0.431 0.245

CS 0.0370 0.651 0.127 0.223

RCMR 0.0322 0.528 0.333 0.140

LO 0.0297 0.091 0.151 0.758

MS&FP 0.0301 0.246 0.483 0.253

WSU 0.0294 0.414 0.281 0.305

CT&D 0.0307 0.256 0.679 0.064

ES 0.0307 0.690 0.257 0.053

Scores = Σ wj*rij 0.4293 0.3590 0.2330


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Appendix: Priority weights calculation of example problem Table A Rating on each partner

Partner1 Partner2 Partner3 Ratings

A1

Partner1 1 1/4 5 0.244

Partner2 4 1 7 0.687

Partner3 1/5 1/7 1 0.069

A2

Partner1 1 2 7 0.559

Partner2 1/2 1 9 0.383

Partner3 1/7 1/9 1 0.058

A3

Partner1 1 3 2 0.524

Partner2 1/3 1 5 0.342

Partner3 1/2 1/5 1 0.134

A4

Partner1 1 5 4 0.691

Partner2 1/5 1 1 0.149


Partner3 ¼ 1 1 0.160

A5

Partner1 1 1/4 7 0.312

Partner2 4 1 1/3 0.594

Partner3 1/7 3 1 0.094

A6

Partner1 1 3 5 0.651

Partner2 1/3 1 1/3 0.127

Partner3 1/5 3 1 0.223

A7

Partner1 1 3 5 0.625

Partner2 1/3 1 1/7 0.092

Partner3 1/5 7 1 0.283

A8

Partner1 1 1/2 1/7 0.098

Partner2 2 1 ¼ 0.817

Partner3 7 4 1 0.715

A9

Partner1 1 1/7 3 0.240

Partner2 7 1 ½ 0.483

Partner3 1/3 2 1 0.273

A10

Partner1 1 9 1/3 0.344

Partner2 1/9 1 5 0.067

Partner3 3 1/5 1 0.589

A11

Partner1 1 1/5 5 0.218

Partner2 5 1 7 0.715

Partner3 1/5 1/7 1 0.067

A12

Partner1 1 1/4 6 0.256

Partner2 4 1 7 0.679

Partner3 1/6 1/7 1 0.064

A13

Partner1 1 5 2 0.570

Partner2 5 1 ¼ 0.097

Partner3 1/2 4 1 0.333


A14

Partner1 1 1/6 1/3 0.095

Partner2 6 1 3 0.655

Partner3 3 1/3 1 0.250

A15

Partner1 1 1/4 7 0.312

Partner2 4 1 1/3 0.594

Partner3 1/7 3 1 0.094

A16

Partner1 1 3 7 0.691

Partner2 1/3 1 1/3 0.120

Partner3 1/7 3 1 0.189

A17

Partner1 1 4 6 0.676

Partner2 1/4 1 1/7 0.077

Partner3 1/6 7 1 0.247

A18

Partner1 1 1/2 1/7 0.110

Partner2 2 1 6 0.609

Partner3 7 1/6 1 0.280

A19

Partner1 1 ¼ 3 0.299

Partner2 4 1 ½ 0.414

Partner3 1/3 2 1 0.287

A20

Partner1 1 9 1/3 0.344

Partner2 1/9 1 5 0.067

Partner3 3 1/5 1 0.589

A21

Partner1 1 4 5 0.642

Partner2 1/4 1 7 0.285

Partner3 1/5 1/7 1 0.072

A22

Partner1 1 2 7 0.559

Partner2 1/2 1 9 0.383

Partner3 1/7 1/9 1 0.058

A23


Partner1 1 7 7 0.778

Partner2 1/7 1 1 0.111

Partner3 1/7 1 1 0.111

A24

Partner1 1 4 4 0.667

Partner2 ¼ 1 1 0.167

Partner3 ¼ 1 1 0.167

A25

Partner1 1 1/7 7 0.335

Partner2 7 1 1/3 0.431

Partner3 1/7 3 1 0.245

A26

Partner1 1 3 5 0.651

Partner2 1/3 1 1/3 0.127

Partner3 1/5 3 1 0.223

A27

Partner1 1 2 3 0.528

Partner2 ½ 1 3 0.333

Partner3 1/3 1/3 1 0.140

A28

Partner1 1 1/2 1/7 0.091

Partner2 2 1 1/6 0.151

Partner3 7 6 1 0.758

A29

Partner1 1 1/7 4 0.264

Partner2 7 1 ½ 0.483

Partner3 1/4 2 1 0.253

A30

Partner1 1 8 1/4 0.414

Partner2 1/8 1 5 0.281

Partner3 4 1/5 1 0.305

A31

Partner1 1 1/4 6 0.256

Partner2 4 1 7 0.679

Partner3 1/5 1/6 1 0.064

A32

Partner1 1 5 7 0.690


Partner2 1/5 1 9 0.257

Partner3 1/7 1/9 1 0.053

Jun Ren, Department of Engineering, Liverpool John Moores University, Byrom

Street, L3 3AF, UK. Email: [email protected]. Dr Ren is a senior research fellow at

Liverpool John Moores University. He received his BSc degree in Mathematics from

Sichuan University in 1985, MSc degree in Management Information Systems from

Harbin Institute of Technology, China in 1991. He obtained a PhD in Operations

Management at the University of Exeter, UK. His research interests are in the areas of

Agile Manufacturing, Management Information Systems and Data Mining. He has

published several papers in these areas.

Black/white Photograph

Yahaya Y Yusuf, Business School, University of Hull. Cottingham Road, Hull, HU6

7RX, UK. Email: [email protected]. Dr Yusuf is a senior lecturer in

operations and information systems at the University of Hull, Hull, UK; His research

interests are in the areas of Analysis and Control of Production Systems, Operations

Strategy and Enterprise Information Systems.

Neil D Burns, Department of Manufacturing Engineering, Loughborough University,

UK. Email: [email protected]. Professor Burns is a professor of Manufacturing

Systems and holds the Davy Chair of Manufacturing Systems at Loughborough

University, Loughborough, UK. His research areas of interests are Agile

Manufacturing, Performance Measurement Systems, Organisational designs and

Enterprise psychology.

mailto:[email protected]



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