Expert Systems with Applications 39 (2012) 626–637

Contents lists available at ScienceDirect

Expert Systems with Applications

journal homepage: www.elsevier .com/locate /eswa

The analytic network process for managing inter-enterprise collaboration:A case study in a collaborative enterprise network

María-José Verdecho ⇑, Juan-José Alfaro-Saiz, Raúl Rodríguez-Rodríguez, Angel Ortiz-BasResearch Centre on Production Management and Engineering (CIGIP), Department of Business Organization, Universitat Politècnica de València,Camino de Vera s/n, 46022 Valencia, Spain

a r t i c l e i n f o

Keywords:Analytic network processCollaborationPerformance managementEnterprise networkBalanced scorecard

0957-4174/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.eswa.2011.07.054

⇑ Corresponding author. Address: Department of Bude Vera s/n, Building 7D, Universidad Politécnica dSpain. Tel.: +34 963877007x76888; fax: +34 9638776

E-mail addresses: mverdecho@omp.upv.es (M.-J. V(J.-J. Alfaro-Saiz), raurodro@upvnet.upv.es (R. Rodrígupv.es (A. Ortiz-Bas).

a b s t r a c t

In the last years, collaboration among enterprises has gained attention in the business environment as ameans to remain competitive. Enterprises that are collaborating look for improving their performancebut, in real assessments, they often do not establish efficient frameworks to structure and manage theenterprise association/inter-enterprise performance. In addition, there are many factors that act asbarriers to effective collaboration and have to be also properly managed as they impact on the inter-enterprise performance. This paper provides a methodology based on the analytic network process(ANP) to identify and measure, under an integrated approach, both factors and inter-enterprise perfor-mance considering their reciprocal impact. With this innovative approach, enterprises will obtainsignificant information for the decision-making process regarding which are the factors and inter-enterprise performance elements that generate a higher impact and therefore have a high priority withinthe specific collaborative relationship. Thus, enterprises can focus their efforts on improving those mostimportant factors and performance elements, and consequently, enhancing their competitiveness.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Enterprise collaboration has been one of the most used businessmodels to compete and adapt to market requirements. Simatupangand Sridharan (2002) define supply chain collaboration as ‘two ormore companies working together to create a competitive advantageand higher profits than can be achieved by acting alone’. These com-panies are actively working together towards common objectivesand share information, knowledge, risks and profits (Mentzer,2001). Enterprises that are collaborating look for improving theirperformance in different aspects such as increased inventory turn-over, increased revenues, cost reductions, product availability, andeconomic value added (Fawcett, Magnan, & McCarter, 2008).

However, enterprises that desire to collaborate or are collabo-rating often do not establish efficient mechanisms to manage theperformance of the enterprise association, called global or inter-enterprise performance, which can be measured, and therefore,managed through performance measurement elements (objec-tives, performance indicators, etc.). In these contexts, it is impor-tant to define inter-enterprise performance elements in order to

ll rights reserved.

siness Organization, Caminoe Valencia, 46022 Valencia,89.erdecho), jalfaro@omp.upv.esuez-Rodríguez), aortiz@omp.

lead the activities of all the members towards the achievementof the commonly agreed objectives (Alfaro, Rodríguez, Verdecho,& Ortiz, 2009; Bititci, Mendibil, Martinez, & Albores, 2005; Verd-echo, Alfaro, & Rodriguez-Rodriguez, 2009). Thus, it is importantfor those enterprises to define and use a structured performancemeasurement framework that allows managing performance un-der various perspectives or dimensions so that they provide a rel-evant overview of their performance status. One of the mostimportant performance frameworks developed in the academic lit-erature and business applications is the balanced scorecard (BSC)by Kaplan and Norton (1992). In fact, the BSC, developed initiallyfor managing performance of enterprises, has been extended bydifferent authors for inter-enterprise performance managementsuch as the works by Brewer and Speh (2000), Bititci et al.(2005), Folan and Browne (2005) or Alfaro, Ortiz and Rodríguez(2007).

Despite the benefits of collaboration, there are many factorsthat act as barriers to effective collaboration (Fawcett et al.,2008): lack of top management support, cross-functional conflicts,lack of trust, etc. In fact, many collaborative initiatives that initiallywere developed to improve the competitiveness of the enterprises,fail due to these factors (Bititci et al., 2007; Kampstra, Ashayeri, &Gattorna, 2006; Sabath & Fontanella, 2002). For that reason, it isimportant to manage those factors as they impact on the resultingperformance. In addition, the achievement or not achievement ofthe performance element targets impact on the factors, configuringa system of reciprocal influences between factors and performance

M.-J. Verdecho et al. / Expert Systems with Applications 39 (2012) 626–637 627

elements. For example, the achievement of one financial objective,e.g. increase profitability, impacts on trust, one of the most rele-vant factors widely analyzed in the literature. At the same time,trust among members impacts on increasing profitability, andthus, a reciprocal relationship between trust and increasing profit-ability objective is established. On the other hand, both factors andinter-enterprise performance elements are linked among them-selves at the internal level. Therefore, it is needed to develop a pro-posal that identifies and measures the factors and inter-enterpriseperformance elements under an integrated manner and make ex-plicit the overall real influences that exist within the system so thataccurate and significant results are obtained. This proposal shouldaddress some questions in order to manage effectively collabora-tive relationships (Busi & Bititci, 2006; Simatupang & Sridharan,2005): What are the relevant factors of collaborative relationships?How can these factors be associated to a performance measure-ment framework? How are these factors and performanceelements linked together? How should both factors and perfor-mance elements be measured? etc.

From a methodological point of view, it is important to select anadequate method to solve this issue. Suwignjo, Bititci, and Carrie(2000) suggest that integrating the multidimensional effects of fac-tors on performance into a single unit of measurement can only bedone through subjective, individual or group judgment. An exam-ple of this fact is the valuation of the trade-off between trust andperformance. There are not measures that can objectively dealwith this issue. For that reason, subjective measurement is widelyaccepted in Multi-Criteria Decision Analysis (MCDA) to deal withmulti-criteria problems (Suwignjo et al., 2000). MCDA comprisesa large variety of methods. One of the most extensively methodused is ANP introduced by Saaty (1996). There are three main rea-sons that suggest using ANP to model and solve the problem of thispaper. First, ANP allows modeling complex problems with a net-work structure integrating interdependences and feedback amongelements. Second, ANP is adequate to solve problems with bothqualitative and quantitative factors (Peniwati, 2007). This is impor-tant as many of the collaboration factors are qualitative such ascultural factors, and many of the methods are developed solelyfor quantitative measurement. Third, ANP has been used ingroup-decision problems (Erdogmus�, Kapanoglu, & Koç, 2005; Levy& Taji, 2007), which is the case of a collaborative relationship.

The aim of this paper is to provide a methodology based on ANPthat aids to make decisions to enterprises that collaborate by iden-tifying and measuring, under an integrated approach, both factorsand inter-enterprise performance elements considering their reci-procal and internal relationships. The structure of this paper is asfollows. Firstly, a literature review regarding relevant factors ofcollaborative relationships and the application of ANP for perfor-mance measurement are analyzed. Secondly, the methodologydeveloped is described. Then, the application of the methodology

Table 1Summary of relevant factors on collaborative relationships.

Factors Refe

Strategic factors Joint vision, design of the inter-enterprise network,equity, top management support

Spek(200(200

Business process andinfrastructurefactors

Process alignment, IS/ICTs interoperability,complementary skills, coordination between activities

MohandMat

Organizationalstructure factors

Collaboration leadership, compatibility ofmanagement styles, joint decision-making,multidisciplinary teams

BoddBurg(200

Cultural factors Trust, commitment, cooperation, information shared,conflict resolution management

Moh(200Gian

to a case study is exposed. Finally, conclusions and research impli-cations are presented.

2. Literature review

2.1. Relevant factors of collaborative relationships

There are numerous works within the literature that deal withidentifying main factors of inter-enterprise relationships. Some ofthese works present classifications of inter-enterprise environ-ments according to the level of maturity reached in different as-pects of their relationships, i.e. they present supply chainevolutionary models (from lower to higher level of collaboration).Sabath and Fontanella (2002) present a supply chain classificationdepending on two main aspects: strategic value of the relationshipand technology used to support it. From a process perspective,Lockamy and McCormack (2004) develop a model to classify sup-ply chains based on the maturity of their processes. Each level ischaracterized according to different factors such as alignment ofprocesses, organizational structure, cooperation, process perfor-mance and trust. Lejeune and Yakova (2005) expose a typologyfor supply chain characterization related to social relationshipstheory and the interdependence concept.

Other works aim at identifying the main factors that impact onpartnerships. Mohr and Spekman (1996) identify, from an empiri-cal study, the factors that contribute to successful partnerships:relationship attributes (coordination, commitment, and trust),communication behaviour and joint problem solving techniques.Boddy, Macbeth, and Wagner (2000) identify seven factors forpartnering contexts: business processes, people, trust, technology,structure, financial resources and culture. Table 1 presents a sum-mary of relevant factors on collaborative relationships. The classi-fication is structured by adapting the main blocks of the MIT’90framework (Scott-Morton, 1991). Despite the MIT’90 frameworkwas developed for individual enterprise contexts, if we conceptual-ize a collaborative inter-enterprise context as an organization thatpursues common objectives, its application is justified.

2.2. ANP for performance measurement

ANP has been recently applied for performance measurementapplications at both intra-enterprise and inter-enterprise levels.Talluri and Sarkis (2002) develop an ANP model with traditionalquality control methods in manufacturing. The approach consistsin a system to monitor the performance of a manufacturing enter-prise at the strategic, tactical and operational levels. Yurdakul(2003) present an ANP model to select those areas of higher suc-cess (priority areas) within a company, depending on the compet-itive strategy (innovation, customization, cost reduction, etc.).

rences

man, Kamauff, and Myhr (1998), Boddy et al. (2000), Sabath and Fontanella2), Barratt (2004), Chopra and Meindl (2004, chap. 1), Simatupang and Sridharan5) and Wu et al. (2009)r and Spekman (1996), Barratt (2004), Lockamy and McCormack (2004), LejeuneYakova (2005), Min et al. (2005), Simatupang and Sridharan (2005) andopoulos, Vlachopoulou, Manthou, and Manos (2007)

y et al., 2000, Barratt (2004), Lockamy and McCormack (2004), Min et al. (2005),ess and Singh (2006), Camarinha-Matos, Afsarmanesh, Galeano, and Molina9) and Wu et al., 2009r and Spekman (1996), Boddy et al. (2000), Barratt (2004), Handfield and Bechtel4), Lam and Chin (2005), Lejeune and Yakova (2005), Burgess and Singh (2006),nakis (2007), Matopoulos et al. (2007) and Wu et al. (2009)

628 M.-J. Verdecho et al. / Expert Systems with Applications 39 (2012) 626–637

Other applications pursue to select suppliers to form strategic alli-ances (Chen, Lee, & Wu, 2008; Kirytopoulos, Leopoulos, & Voulgar-idou, 2008; Wu, Shih, & Chan, 2009). Yuksel and Dagdeviren (2010)present an ANP model for a BSC and an application to a manufac-turing firm. However, few of these works consider relevant collab-orative factors within their models, e.g. Wu et al. (2009) considerthe culture and compatibility among management styles factors.However, there is not a model developed to manage, under an inte-grated and structured approach, both factors and performance ele-ments considering the relationships between them. In addition, theworks that use the BSC for managing performance are only appliedat the individual enterprise level (intra-enterprise level). Therefore,it is possible to affirm that there is not yet a model for inter-enter-prise performance management that allows managing perfor-mance under a structured performance measurement framework.For these reasons, this paper proposes a novel approach to fill thisresearch gap. Thus, enterprises will obtain relevant information foraiding the decision-making process that identifies the factors and

Fig. 1. Main elements o

Fig. 2. Steps of the

inter-enterprise performance elements that generate a higher im-pact, and therefore, have a high priority for their competitiveness.

3. The proposed methodology

Fig. 1 shows the main elements of the methodological approach.The purpose of the methodology is to identify and measure rele-vant collaborative factors and inter-enterprise performance ele-ments considering their reciprocal impact as well as their innerdependences. For building and solving this problem, the ANPmethod will be used. The proposed methodology is composed ofthe following five steps (Fig. 2):

Step 1. Characterize the collaborative context. This step aims atobtaining a general overview of the inter-enterprise envi-ronment and, specifically, the deep and width of the col-laboration relationships among their members.

f the methodology.

methodology.

M.-J. Verdecho et al. / Expert Systems with Applications 39 (2012) 626–637 629

Step 2. Establishment of the group of experts. The group ofexperts should include people within three types of skills:strategic, process and consultant. Strategic and processprofiles are people from all the enterprises that are collab-orating and have expertise on strategic and process issues.The consultant can be either internal or external to thecollaborative enterprises and will act as organizer andmoderator of the meetings for implementing themethodology.

Fig. 3. Step 4. Definition of the inter-enterprise performance elements: activities.

Fig. 4. Step 5. Obtain the priorities of the relevant factors on collaborative

Step 3. Analyze and synthesize the relevant factors on collabora-tive relationships. In this step, this study conceptualizesthe collaboration framework from the literature reviewedon Section 2 and the opinions of the expert team. As pre-viously stated, the framework comprises factors groupedinto four main blocks: strategic, business process andinfrastructure, organizational structure and cultural fac-tors. The dashed line between steps 3 and 4 of the method-ology, in Fig. 2, makes reference to the conceptualrelationships among both factors and performance ele-ments respectively what will be established in the modelon step 5. The members of group of experts are responsiblefor reviewing the factors identified and validating thatthese factors are relevant for its specific collaborative con-text. In addition, they can add any other factor that theyconsider relevant, e.g. specific factors within an industry.

Step 4. Definition of the inter-enterprise performance elements.This step consists of the definition of the performance ele-ments for the collaborative enterprises as well as theirinterdependences. This step comprises four activities(Fig. 3).

relations

Activity 4.1. Define the joint mission and vision of the col-laborative enterprises.Activity 4.2. Define the stakeholder requirements (share-holders, suppliers, customers, staff and community) ofthe collaborative enterprises.Activity 4.3. Establish the performance objectives of theBSC in coherence with the previous elements (mission,vision and stakeholder requirements). The objectives aredefined for the four perspectives of the BSC: financial, cus-tomer, process, and innovation and learning.Activity 4.4. Identify and represent influences amongobjectives. The objectives of the four perspectives presentcause-effect relationships. Thus, objectives are linked in amanner that the achievement of one objective influences

hips and inter-enterprise performance elements: activities.

Table 2Performan

Perspe

Finance

Custom

Process

Innovalear

630 M.-J. Verdecho et al. / Expert Systems with Applications 39 (2012) 626–637

the achievement of the objectives that are linked to it. Torepresent influences, objectives are projected into a strat-egy map (Kaplan & Norton, 2004).Activity 4.5. Verify consistency among objectives. In thisactivity it is checked that objectives of lower perspectivessupport the objectives of upper perspectives (Alfaro et al.,2007; Kaplan & Norton, 2004).

Step 5. Obtain the priorities of the relevant factors on collabora-tive relationships and inter-enterprise performance ele-ments. This final step aims at composing, solving andanalyzing the results of the ANP model. This step is com-posed of the following activities (Fig. 4):

Activity 5.1. Build the ANP model. Following the ANPmethod (Saaty, 1996), factors and performance objectivesare elements that can be structured into clusters, specifi-cally into five clusters. One cluster contains the perfor-mance objectives and the other four clusters contain thefour types of factors. The model is structured in this man-ner to compare among 7 ± 2 elements following the limita-tion of human capability to process information (Miller,1956). Then, interdependences and feedback among allthe elements are defined.Activity 5.2. Complete the pairwise comparison matricesamong elements by using the fundamental scale of Saaty(1980). In order to facilitate the comparison process, aquestionnaire is provided to the experts and, afterwards,responses are translated into the numerical scale. Then,for each pairwise comparison matrix, the eigenvector (pri-ority vector) is calculated and consistency checked (Saaty,2001, p. 57).

ce objectives for the collaborative network enterprise.

ctive Performance objective

Increase 10% turnover (FSO1)Increase 5% profitability (FSO2)

er Increase 15% the power installed by customer (CSO1)Increase 10% the number of new customers byrecommendation of old customers (CSO2)

Increase 5% the performance ration of the PV solar plant(PSO1)Reduce 20% total cycle time (PSO2)

tion andning

Include (at least) a PV module supplier in collaborativerelationship (ILSO1)Increase 10% the degree of anticipation to industrychanges (legislation, technological, etc.) (ILSO2)

Fig. 5. Collaborative enterprise network

Activity 5.3. Compose the unweighted supermatrix withthe accepted priorities from activity 5.2.Activity 5.4. Complete the pairwise comparison matricesamong clusters following the same procedure as activity5.2. Compose the cluster matrix with the priorities of theclusters.Activity 5.5. Obtain the weighted supermatrix by multiply-ing the cells of the cluster matrix and the correspondingcolumns of the unweighted supermatrix. If the resultingmatrix is not stochastic, it has to be normalized in columnsso that it converges when raised to powers.Activity 5.6. Calculate the limit matrix. Raising theweighted supermatrix to powers until it remains stableyields the limit matrix. Then, global or final priorities ofthe factors and objectives are obtained.Activity 5.7. Analyze results.

4. Case study

The case study is carried out in a collaborative enterprise net-work belonging to the renewable energy sector located in Valencia,Spain. During the last years, this sector has gained importancewithin the Spanish economy mainly due to government regula-tions and the necessity to diversify the energy sources. The meth-odology is explained with the application as follows:

Step 1. The enterprise network comprises enterprises with differ-ent functions: raw material suppliers, sub-assembly sup-pliers, engineering enterprise, and promoter enterprise.Most of them work on different business units, mainly,photovoltaic (PV) solar energy, wind energy, and thermo-electric energy. The application is performed at the PVsolar energy business unit dedicated to the design, con-struction, operation and maintenance of PV solar energyplants. This business unit is the most important one forthe collaborative enterprise network.

Step 2. The group of experts is composed by operations, financialand project managers of the enterprises as well as theauthors of this paper acting as consultants.

Step 3. The experts reviewed the factors and concluded that allseventeen factors within the four groups were relevantfor their competitiveness and, therefore, for the sustain-ability of their relationship. Therefore, the factors are iden-tified as follows:

strategy map.

Table 3Coherence matrix among performance objectives.

FSO1 FSO2 CSO1 CSO2 PSO1 PSO2 ILSO1 ILSO2

FSO1 0 0 0 0 0 0 0 0FSO2 0 0 0 0 0 0 0 0CSO1 X X 0 0 0 0 0 0CSO2 X 0 0 0 0 0 0 0PSO1 0 0 0 X 0 0 0 0PSO2 0 0 0 X 0 0 0 0ILSO1 0 0 0 0 0 X 0 XILSO2 0 0 X 0 X 0 0 0

Table 4Pairwise comparison matrix for the FSO1 objective.

SF1 SF2 SF3 SF4 Eigenvector

SF1 1 5 7 1 0.3950SF2 1/5 1 1 1/9 0.0626SF3 1/7 1 1 1/9 0.0570SF4 1 9 9 1 0.4854

CR 0.0123

Table 5Pairwise comparison matrix for the SF2 factor.

FSO1 FSO2 CSO2 PSO2 ILSO1 ILSO2 Eigenvector

FSO1 1 1/3 1 5 5 1 0.1727FSO2 3 1 3 7 7 3 0.042CSO2 1 1/3 1 5 5 3 0.113PSO2 1/5 1/7 1/5 1 1/3 1/3 0.363ILSO1 1/5 1/7 1/5 3 1 1/3 0.535ILSO2 1 1/3 1/3 3 3 1 0.219

M.-J. Verdecho et al. / Expert Systems with Applications 39 (2012) 626–637 631

� Strategic factors:– Joint vision (SF1).– Design of the inter-enterprise supply chain/network

(SF2).– Equity (SF3).– Top management support (SF4).

CR 0.498

Table 6Pairwise comparison matrix for the SF1 factor.

OF1 OF2 OF3 OF4 Eigenvector

� Business process and infrastructure factors:– Process alignment (PF1).– IS/ICTs interoperability (PF2).– Complementary skills (PF3).– Coordination between activities (PF4).

OF1 1 5 7 5 0.259

OF2 1/5 1 5 3 0.229OF3 1/7 1/5 1 1 0.678OF4 1/5 1/3 1 1 0.834

CR 0.769

� Organizational structure factors:– Collaboration leadership (OF1).– Compatibility of management styles (OF2).– Joint decision-making (OF3).– Multidisciplinary teams (OF4).

� Cultural factors:– Trust (CF1).– Commitment (CF2).

Fig. 6. The ANP model for managing inter-enterpr

– Cooperation (CF3).– Information shared (CF4).– Conflict resolution management (CF5).

ise collaboration.

Table 7Unweighted supermatrix.

SO SF CF

FSO1 FSO2 CSO1 CSO2 PSO1 PSO2 ILSO1 ILSO2 SF1 SF2 SF3 SF4 . . . CF1 CF2 CF3 CF4 CF5

SO FSO1 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.2379 0.1727 0.1812 0.2577 . . . 0.2261 0.1994 0.1544 0.1414 0.0670FSO2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.3733 0.4042 0.4428 0.3405 . . . 0.3656 0.3507 0.2293 0.3284 0.1904CSO1 0.2500 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0513 0.0000 0.0748 0.1090 . . . 0.1083 0.1109 0.0582 0.0551 0.0842CSO2 0.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1248 0.2113 0.1008 0.1344 . . . 0.1412 0.0886 0.1816 0.1465 0.0772PSO1 0.0000 0.0000 0.0000 0.5000 0.0000 0.0000 0.0000 0.0000 0.0379 0.0000 0.0387 0.0251 . . . 0.0263 0.0452 0.0430 0.0550 0.0311PSO2 0.0000 0.0000 0.0000 0.5000 0.0000 0.0000 0.0000 0.0000 0.0362 0.0363 0.0290 0.0246 . . . 0.0335 0.0383 0.1461 0.0522 0.0637ILSO1 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 1.0000 0.0206 0.0535 0.0320 0.0246 . . . 0.0298 0.0476 0.1249 0.1406 0.0931ILSO2 0.0000 0.0000 1.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.1179 0.1219 0.1008 0.0842 . . . 0.0692 0.1194 0.0627 0.0808 0.3933

SF SF1 0.3950 0.2351 0.2313 0.6050 0.3027 0.3234 0.3603 0.3484 0.0000 0.2789 0.1782 0.7778 . . . 0.2351 0.2494 0.3018 0.1997 0.5482SF2 0.0626 0.0626 0.1315 0.0846 0.0733 0.0742 0.0737 0.1013 0.1111 0.0000 0.0704 0.1111 . . . 0.0627 0.0484 0.0650 0.0871 0.0523SF3 0.0570 0.0626 0.0881 0.0773 0.0570 0.0380 0.0855 0.1013 0.1111 0.0719 0.0000 0.1111 . . . 0.0627 0.0484 0.0608 0.0783 0.0523SF4 0.4854 0.6396 0.5491 0.2332 0.5671 0.5644 0.4805 0.4489 0.7778 0.6491 0.7514 0.0000 . . . 0.6396 0.6538 0.5724 0.6350 0.3473

PF PF1 0.2407 0.2345 0.1639 0.1783 0.2407 0.1170 0.3089 0.2407 0.0864 0.0833 0.1429 0.0000 . . . 0.0000 0.1429 0.1170 0.1783 0.0909PF2 0.1416 0.1231 0.1497 0.1296 0.1416 0.0908 0.1416 0.1416 0.0990 0.0833 0.0000 0.0000 . . . 0.0000 0.0000 0.0908 0.1296 0.0000PF3 0.3089 0.2923 0.4519 0.3889 0.3089 0.3961 0.2407 0.3089 0.3828 0.4167 0.4286 0.5000 . . . 0.5000 0.4286 0.3961 0.3031 0.4545PF4 0.3089 0.3500 0.2345 0.3031 0.3089 0.3961 0.3089 0.3089 0.4319 0.4167 0.4286 0.5000 . . . 0.5000 0.4286 0.3961 0.3890 0.4545

OF OF1 0.6460 0.5803 0.4309 0.5128 0.5128 0.6471 0.6613 0.6255 0.6259 0.6513 0.6570 0.6469 . . . 0.5983 0.6132 0.6396 0.6570 0.6259OF2 0.1790 0.2047 0.3598 0.2261 0.2261 0.1908 0.1532 0.2247 0.2229 0.1193 0.1911 0.2144 . . . 0.1679 0.2085 0.2351 0.1911 0.2229OF3 0.0708 0.0568 0.0577 0.0681 0.0681 0.0590 0.0803 0.0756 0.0678 0.1100 0.0760 0.0728 . . . 0.0899 0.0892 0.0626 0.0760 0.0678OF4 0.1042 0.1582 0.1516 0.1930 0.1930 0.1032 0.1051 0.0742 0.0834 0.1193 0.0760 0.0659 . . . 0.1439 0.0892 0.0626 0.0760 0.0834

CF CF1 0.1859 0.3603 0.1844 0.1924 0.2988 0.2663 0.3280 0.2181 0.3180 0.0800 0.3528 0.4218 . . . 0.0000 0.5294 0.3031 0.4874 0.5769CF2 0.2877 0.2198 0.1844 0.2425 0.2102 0.4495 0.2949 0.3452 0.3180 0.3523 0.3216 0.2537 . . . 0.0736 0.0000 0.1296 0.1182 0.1807CF3 0.2528 0.1463 0.2621 0.2425 0.2402 0.1163 0.2287 0.0919 0.2154 0.3523 0.1384 0.1622 . . . 0.2845 0.1377 0.0000 0.2762 0.1263CF4 0.1837 0.2296 0.2106 0.1924 0.1807 0.1274 0.0894 0.2970 0.0743 0.1680 0.1072 0.1037 . . . 0.3210 0.2122 0.3889 0.0000 0.1161CF5 0.0899 0.0440 0.1585 0.1302 0.0701 0.0404 0.0590 0.0477 0.0743 0.0475 0.0801 0.0587 . . . 0.3210 0.1207 0.1783 0.1182 0.0000

632M

.-J.Verdecho

etal./Expert

Systems

with

Applications

39(2012)

626–637

Table 8Pairwise comparison matrix for the PF cluster.

SO SF PF OF OC Eigenvector

SO 1 1/3 1/3 1 1/3 0.0864SF 3 1 3 3 3 0.4118PF 3 1/3 1 1 1/3 0.1424OF 1 1/3 1 1 1/3 0.0983OC 3 1/3 3 3 1 0.2611

CR 0.0635

Table 9Cluster matrix.

SO SF PF OF CF

SO 0.4458 0.1097 0.0864 0.3494 0.2858SF 0.2230 0.2183 0.4118 0.1993 0.2701PF 0.0452 0.1796 0.1424 0.1031 0.0752OF 0.1180 0.1834 0.0983 0.1227 0.1514CF 0.1680 0.3090 0.2611 0.2255 0.2175

M.-J. Verdecho et al. / Expert Systems with Applications 39 (2012) 626–637 633

Step 4. The mission and vision of the PV solar business unit wasdetermined by the experts. Specifically, the vision states‘‘Becoming national leader in number of PV solar plantsand high energetic performance while decreasing theircost’’.

Then, the stakeholder’s requirements were defined. For exam-ple, the customer’s requirements state ‘‘the acquisition of PV solarplants with high guaranteed profitability’’. After that, performanceobjectives are defined for the financial, customer, process andinnovation and learning perspectives (Table 2).

The enterprise network strategy map is determined to show therelationships among performance objectives (Fig. 5). In the strat-egy map, the directions of the arrows have been adapted to becoherent with the ANP method. Then, the directions have a specificmeaning. For example, the arrow that joins the objectives of theinnovation and learning perspective means that the objective ‘‘in-clude (at least) a PV module supplier in collaborative relationship’’impacts on the objective ‘‘Increase 10% the degree of anticipationto industry changes (legislation, technological, etc.)’’. This criterionis coherent with the ANP method that will be applied on step 5. Ta-ble 3 shows the coherence matrix among performance objectives.As can be observed, objectives of lower perspectives support theachievement of the objectives located in the upper perspectivesmeaning that the objectives have a high degree of coherence.

Step 1. Fig. 6 shows the BSC-ANP model for managing inter-enter-prise collaboration. The network consists of five clustersgrouping the different elements previously identified.Arrows show the inner and outer dependences amongtheir elements. Pairwise comparison matrices wereobtained from the questionnaire filled in by the expertteam. Table 4 shows the pairwise comparison matrixamong the elements of the strategic factor cluster withrespect to the ‘‘increase 10% turnover’’ (FSO1) objective.The eigenvector indicates the importance of each factor,and it can be observed that the ‘‘Top management sup-port’’ (SF4) factor holds the highest eigenvector weightwith 0.4854. Then, ‘‘Joint vision’’ (SF1), ‘‘design of theinter-enterprise network’’ (SF2) and ‘‘Equity’’ (SF3) areweighted 0.3950. 0.0626 and 0.0570, respectively. In addi-tion, the consistency ratio (CR) is 0.0123, which meansthat the experts were consistent when making theirjudgments.

Regarding the impact of the performance objectives on the col-laborative factors, Table 5 shows the pairwise comparison matrixamong the elements of the performance objectives cluster with re-spect to the ‘‘design of the inter-enterprise network’’ (SF2) factor.The eigenvector indicates the importance of each objective, andit can be observed that the ‘‘increase 5% profitability’’ (FSO2) objec-tive has the higher weight with 0.4042. Other objective with highimpact on SF2 is ‘‘increase 10% the number of new customers byrecommendation of old customers’’ (CSO2). In this case, the CR is0.0498, thus, judgments are consistent.

Regarding the influences among factors, Table 6 shows the pair-wise comparison matrix among the elements of the organizationalstructure cluster with respect to the ‘‘joint vision’’ (SF1) factor. Theeigenvector indicates that the ‘‘collaboration leadership’’ (OF1) and‘‘compatibility of management styles’’ (OF2) factors have the high-er impact with 0.6259 and 0.2229, respectively. CR is 0.0769, andtherefore, judgments are consistent.

The summary of all weights from the pairwise comparisonmatrices is shown in the unweighted supermatrix (Table 7).

Next, we obtain the cluster matrix that shows the prioritiesamong clusters. Table 8 shows the pairwise comparison matrixamong the clusters with respect to the business process and infra-structure cluster (PF). As can be observed, the cluster that maintaina higher impact on the PF cluster is the SF cluster (0.4118) followedby the cultural factor cluster (0.2611). According to the consistencyratio, the judgments are consistent (CR = 0.0635).

The summary of weights among clusters is shown in the clustermatrix (Table 9).

By multiplying the unweighted supermatrix and the cluster ma-trix, we obtain the weighted supermatrix (Table 10). Then, it is nor-malized and raised to powers until it converges, obtaining the limitsupermatrix. Table 11 shows the limit priorities (LP) of objectivesand factors.

4.1. Analysis of results

In order to analyze the obtained results, limit priorities are nor-malized for each type of element (Tables 12 and 13): objectivesand factors. Then, we obtain the normalized limit priority (NLP)and the accumulated normalized limit priority (ANLP). In the ta-bles, the last column indicates the type of element: critical (C),medium (M) or low (L) importance. The critical elements are thosecomprising around 50% of ANPL. These elements are the mostimportant for two reasons. On the one hand, those elements havethe highest weights so that they are the most influential ones with-in the network. On the other hand, those elements accumulatearound the 50% of the global weight. The medium importance ele-ments are those that remain between 0.5 and 0.8 of the ANPL. Fi-nally, the low importance elements are between 0.8 and 1 ofANPL. These cut-off values were established based on the expertiseof the group of experts. It has to be noted that the limit among clas-ses is determined on the element that is closest to 0.5 and 0.8,respectively. In Table 12, it is observed that the critical factorsare ‘‘top management support’’, ‘‘joint vision’’, ‘‘collaboration lead-ership’’ and ‘‘trust’’. Two of the critical factors belong to the strate-gic cluster, one factor to the organizational structure cluster andone factor to the cultural cluster. The critical performance objec-tives are (Table 13): ‘‘increase 5% profitability’’ belonging to thefinancial perspective, ‘‘increase 10% the degree of anticipation toindustry changes (legislation, technological, etc.)’’ belonging tothe innovation and learning perspective and ‘‘increase 15% thepower installed by customer’’ within the customer perspective.Therefore, the analysis shows how objectives that are not belong-ing to the financial perspective achieve high priority because theyhave a large influence on other performance objectives or factors.

Table 10Weighted supermatrix

SO SF CF

FSO1 FSO2 CSO1 CSO2 PSO1 PSO2 ILSO1 ILSO2 SF1 SF2 SF3 SF4 . . . CF1 CF2 CF3 CF4 CF5

SO FSO1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0261 0.0189 0.0199 0.0283 . . . 0.0646 0.0570 0.0441 0.0404 0.0191FSO2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0409 0.0443 0.0486 0.0373 . . . 0.1045 0.1002 0.0655 0.0938 0.0544CSO1 0.1114 0.4458 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0056 0.0000 0.0082 0.0120 . . . 0.0309 0.0317 0.0166 0.0157 0.0241CSO2 0.3344 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0137 0.0232 0.0111 0.0147 . . . 0.0404 0.0253 0.0519 0.0419 0.0221PSO1 0.0000 0.0000 0.0000 0.2229 0.0000 0.0000 0.0000 0.0000 0.0042 0.0000 0.0042 0.0028 . . . 0.0075 0.0129 0.0123 0.0157 0.0089PSO2 0.0000 0.0000 0.0000 0.2229 0.0000 0.0000 0.0000 0.0000 0.0040 0.0040 0.0032 0.0027 . . . 0.0096 0.0109 0.0418 0.0149 0.0182ILSO1 0.0000 0.0000 0.0000 0.0000 0.0000 0.4458 0.0000 0.4458 0.0023 0.0059 0.0035 0.0027 . . . 0.0085 0.0136 0.0357 0.0402 0.0266ILSO2 0.0000 0.0000 0.4458 0.0000 0.4458 0.0000 0.0000 0.0000 0.0129 0.0134 0.0111 0.0092 . . . 0.0198 0.0341 0.0179 0.0231 0.1124

SF SF1 0.0881 0.0524 0.0516 0.1349 0.0675 0.0721 0.1450 0.0777 0.0000 0.0609 0.0389 0.1698 . . . 0.0635 0.0674 0.0815 0.0539 0.1481SF2 0.0140 0.0140 0.0293 0.0189 0.0163 0.0165 0.0297 0.0226 0.0243 0.0000 0.0154 0.0243 . . . 0.0169 0.0131 0.0176 0.0235 0.0141SF3 0.0127 0.0140 0.0196 0.0172 0.0127 0.0085 0.0344 0.0226 0.0243 0.0157 0.0000 0.0243 . . . 0.0169 0.0131 0.0164 0.0211 0.0141SF4 0.1082 0.1426 0.1224 0.0520 0.1264 0.1259 0.1933 0.1001 0.1698 0.1417 0.1641 0.0000 . . . 0.1727 0.1766 0.1546 0.1715 0.0938

PF PF1 0.0109 0.0106 0.0074 0.0081 0.0109 0.0053 0.0252 0.0109 0.0155 0.0150 0.0257 0.0000 . . . 0.0000 0.0107 0.0088 0.0134 0.0068PF2 0.0064 0.0056 0.0068 0.0059 0.0064 0.0041 0.0115 0.0064 0.0178 0.0150 0.0000 0.0000 . . . 0.0000 0.0000 0.0068 0.0097 0.0000PF3 0.0140 0.0132 0.0204 0.0176 0.0140 0.0179 0.0196 0.0140 0.0687 0.0748 0.0770 0.0898 . . . 0.0376 0.0322 0.0298 0.0228 0.0342PF4 0.0140 0.0158 0.0106 0.0137 0.0140 0.0179 0.0252 0.0140 0.0776 0.0748 0.0770 0.0898 . . . 0.0376 0.0322 0.0298 0.0292 0.0342

OF OF1 0.0762 0.0685 0.0509 0.0605 0.0605 0.0764 0.1408 0.0738 0.1148 0.1194 0.1204 0.1186 . . . 0.0906 0.0928 0.0968 0.0994 0.0947OF2 0.0211 0.0242 0.0425 0.0267 0.0267 0.0225 0.0326 0.0265 0.0409 0.0219 0.0350 0.0393 . . . 0.0254 0.0316 0.0356 0.0289 0.0337OF3 0.0084 0.0067 0.0068 0.0080 0.0080 0.0070 0.0171 0.0089 0.0124 0.0202 0.0139 0.0133 . . . 0.0136 0.0135 0.0095 0.0115 0.0103OF4 0.0123 0.0187 0.0179 0.0228 0.0228 0.0122 0.0224 0.0088 0.0153 0.0219 0.0139 0.0121 . . . 0.0218 0.0135 0.0095 0.0115 0.0126

CF CF1 0.0312 0.0605 0.0310 0.0323 0.0502 0.0447 0.0994 0.0366 0.0983 0.0247 0.1090 0.1303 . . . 0.0000 0.1152 0.0660 0.1061 0.1255CF2 0.0483 0.0369 0.0310 0.0407 0.0353 0.0755 0.0894 0.0580 0.0983 0.1089 0.0994 0.0784 . . . 0.0160 0.0000 0.0282 0.0257 0.0393CF3 0.0425 0.0246 0.0440 0.0407 0.0403 0.0195 0.0693 0.0154 0.0666 0.1089 0.0428 0.0501 . . . 0.0619 0.0300 0.0000 0.0601 0.0275CF4 0.0309 0.0386 0.0354 0.0323 0.0304 0.0214 0.0271 0.0499 0.0230 0.0519 0.0331 0.0320 . . . 0.0698 0.0462 0.0846 0.0000 0.0253CF5 0.0151 0.0074 0.0266 0.0219 0.0118 0.0068 0.0179 0.0080 0.0230 0.0147 0.0247 0.0181 . . . 0.0698 0.0263 0.0388 0.0257 0.0000

634M

.-J.Verdecho

etal./Expert

Systems

with

Applications

39(2012)

626–637

Table 11Limit priorities.

Performance objectives Factors

FSO1 0.0289 SF1 0.0835 OF1 0.0805 CF5 0.0255FSO2 0.0539 SF2 0.0238 OF2 0.0278CSO1 0.0382 SF3 0.0259 OF3 0.0150CSO2 0.0303 SF4 0.1229 OF4 0.0212PSO1 0.0111 PF1 0.0146 CF1 0.0666PSO2 0.0153 PF2 0.0091 CF2 0.0498ILSO1 0.0349 PF3 0.0404 CF3 0.0472ILSO2 0.0411 PF4 0.0427 CF4 0.0498

M.-J. Verdecho et al. / Expert Systems with Applications 39 (2012) 626–637 635

A second type of analysis is performed to check the critical nat-ure of the clusters depending if we consider: (a) all the elements(general composition), (b) the elements of critical and medium pri-ority or (c) only critical elements. Fig. 7 shows graphically the threecompositions for the factors. In the general composition, the

Table 12Classification of factors.

Rank Factors

1 SF4 Top management support2 SF1 Joint vision3 OF1 Collaboration leadership4 CF1 Trust5 CF2 Commitment6 CF4 Information shared7 CF3 Cooperation8 PF4 Coordination between activities9 PF3 Complementary skills

10 OF2 Compatibility of management styles11 SF3 Equity12 CF5 Conflict management13 SF2 Design of the inter-enterprise supply chain/networ14 OF4 Multidisciplinary teams15 OF3 Joint decision-making16 PF1 Process alignment17 PF2 IS/ICTs interoperability

Table 13Classification of performance objectives.

Rank Performance objectives

1 FSO2 Increase 5% profitability2 ILSO2 Increase 10% the degree of anticipation to industry changes (le3 CSO1 Increase 15% the power installed by customer4 ILSO1 Include (at least) a PV module supplier in collaborative relatio5 CSO2 Increase 10% the number of new customers by recommendatio6 FSO1 Increase 10% turnover7 PSO2 Reduce 20% total cycle time8 PSO1 Increase 5% the performance ration of the PV solar plant

Fig. 7. Cluster composition of factors: (a) global composition, (b)

priority of the clusters is: strategic cluster (SF) (34.3%), culturecluster (CF) (32%), organizational structure cluster (OF) (19.4%)and business process and infrastructure cluster (PF) (14.3%). If weconsider the critical and medium priority factors, the compositionchange to CF (35.19%), SF (34.03%), OF (17.07%) and PF (13.7%).Therefore, there is a little change on the two high priority clusters.However, if we consider only the critical factors, the compositionof clusters is: SF (57.7%), OF (23.1%) and CF (19.1%). As can be ob-served, the CF cluster decreases its importance significantly andthe PF disappears while SF and OF increases their importance. Infact, SF cluster accumulates more than 50% of the priority.

In the case of the performance objectives, as there is only onecluster, it is interesting to analyze the performance perspectiveof the objectives. Fig. 8 shows graphically the three compositionsfor the performance objectives. In the general composition, the pri-ority of the clusters is: financial perspective (FP) (32.6%), innova-tion and learning perspective (ILP) (30%), customer perspective

LP NLP ANLP (%) Class

0.1229 0.1647 16.470.0835 0.1118 27.65 C0.0805 0.1079 38.440.0666 0.0892 47.350.0498 0.0667 54.030.0498 0.0667 60.700.0472 0.0633 67.03 M0.0427 0.0572 72.760.0404 0.0541 78.160.0278 0.0373 81.890.0259 0.0347 85.360.0255 0.0342 88.78

k 0.0238 0.0318 91.96 L0.0212 0.0285 94.810.0150 0.0202 96.820.0146 0.0196 98.780.0091 0.0122 100.00

LP NLP ANLP (%) Class

0.0539 0.2124 21.24gislation, technological, etc.) 0.0411 0.1622 37.46 C

0.0382 0.1505 52.51nship 0.0349 0.1375 66.27 Mn of old customers 0.0303 0.1194 78.20

0.0289 0.1139 89.590.0153 0.0604 95.63 L0.0111 0.0437 100.00

critical and medium priority factors and (c) critical factors.

Fig. 8. Perspective composition of performance objectives: (a) global composition, (b) critical and medium priority objectives and (c) critical objectives.

636 M.-J. Verdecho et al. / Expert Systems with Applications 39 (2012) 626–637

(CP) (27%) and business process perspective (PP) (10.4%). If we con-sider the critical and medium priority objectives, the compositionchange to ILP (38.3%), CP (34.5%), and FP (27.2%). This is due tothe number of medium objectives belonging to the CP and ILP per-spectives. However, if we consider only the critical objectives, thecomposition of clusters is: FP (40.5%), ILP (30.9%) and CP (28.7%).As can be observed, the PF perspective accumulates more that40% of the priority followed by the ILP and CP perspectives. Theobjectives within the PP perspective are not critical for the collab-orative enterprise network.

To sum-up, these two types of analysis provide a global or par-tial overview of the results obtained aiding to focus on the factorsand performance elements that are most important, and thus, havehighest priority for the competitiveness of the enterprise network.

5. Conclusions and research implications

This study has developed a methodology for managing relevantfactors on collaborative relationships and performance elementsunder an integrated approach. This approach solves the limitationsof the literature that deal with collaboration relationships where itis observed a lack of mechanisms that allow managing the com-plexity of these contexts. Also, it is necessary to jointly manage fac-tors and performance elements considering their reciprocal impactas well as their internal dependences as the overall existing influ-ences may affect final results. In the literature, various works sug-gest that both factors and performance elements are linkedtogether. In other words, changes in one of the factors cause vari-ations on other factors and performance elements. The same casehappens if changes occur in one of the performance elements.However, there is not an approach developed that aims to managecollaborative relationships and makes explicit this global networkof influences.

Thus, this proposal, on the one hand, structures those factorsunder a solid framework and, on the other hand, it associates thisframework to a balanced and coherent performance structure suchas the BSC for inter-enterprise contexts.

In addition, from a methodological point of view, enterprisesneed tools to aid decision-making that introduce these aspects aswell as their implementation. Therefore, it is developed a method-ology that guide this process by structuring, in an ordered manner,the steps to be followed for an adequate and efficient definition ofall the elements. Also, it provides two types of analysis for examin-ing final results within a global or partial overview focusing theattention on the factor and performance elements that have a highpriority for the collaborative context.

The methodology is applicable to all types of inter-enterpriseassociations taking into account that the performance elementsdefinition will change depending on the specific context analyzed.In addition, some specific collaborative relationships may consider

other factors, e.g. industry specific factors. Modification and adap-tations to be performed for these two reasons will enable to usethis approach in other collaborative relationships.

The information coming from this study may be used for othermanaging purposes such as budget allocations depending on prior-ity of performance objectives and factors. Other interestingresearch line is to integrate the collaborative performance objec-tives with the individual enterprise objectives within an overallmodel. Therefore, future studies will extend further this work.

Acknowledgments

This work has been developed within the framework of a re-search project partially funded by the Polytechnic University ofValencia, titled ‘‘Design and Implementation of Performance Mea-surement Systems within Collaborative Contexts for aiding theDecision-making Process’’, reference PAID-06-08-3206.

References

Alfaro, J. J., Ortiz, A., & Rodríguez, R. (2007). Performance measurement system forenterprise networks. International Journal of Productivity and PerformanceManagement, 56(4), 305–334.

Alfaro, J. J., Rodríguez, R., Verdecho, M. J., & Ortiz, A. (2009). Business processinteroperability and collaborative performance measurement. InternationalJournal of Computer Integrated Manufacturing, 22(9), 877–889.

Barratt, M. (2004). Understanding the meaning of collaboration in the supply chain.Supply Chain Management: An International Journal, 9(1), 30–42.

Bititci, U. S., Mendibil, K., Martinez, V., & Albores, P. (2005). Measuring andmanaging performance in extended enterprises. International Journal ofOperations & Production Management, 25(4), 333–353.

Bititci, U. S., Turner, T., Mackay, D., Kearney, D., Parung, J., & Walters, D. (2007).Managing synergy in collaborative enterprises. Production Planning & Control,18(6), 454–465.

Boddy, D., Macbeth, D., & Wagner, B. (2000). Implementing collaboration betweenorganizations: An empirical study of supply chain partnering. Journal ofManagement Studies, 37(7), 1003–1018.

Brewer, P. C., & Speh, T. W. (2000). Using the balanced scorecard to measure supplychain performance. Journal of Business Logistics, 21(1), 75–93.

Burgess, K., & Singh, P. (2006). A proposed integrated framework for analysingsupply chains. Supply Chain Management: An International Journal, 11(4),337–344.

Busi, M., & Bititci, U. S. (2006). Collaborative performance management: Presentgaps and future research. International Journal of Productivity and PerformanceManagement, 55(1), 7–25.

Camarinha-Matos, L. M., Afsarmanesh, H., Galeano, N., & Molina, A. (2009).Collaborative networked organizations – Concepts and practice inmanufacturing enterprises. Computers & Industrial Engineering, 57, 46–60.

Chen, S. H., Lee, H. T., & Wu, Y. F. (2008). Applying ANP approach to partner selectionfor strategic alliance. Management Decision, 46(3), 449–465.

Chopra, S., & Meindl, P. (2004). Supply chain management: Strategy. Planning andoperations (2nd ed., ). New Yersey: Prentice-Hall.

Erdogmus�, S., Kapanoglu, M., & Koç, E. (2005). Evaluating high-tech alternatives byusing analytic network process with BOCR and multiactors. Evaluation andProgram Planning, 28(4), 391–399.

Fawcett, S. E., Magnan, G. M., & McCarter, M. W. (2008). Benefits, barriers, andbridges to effective supply chain management. Supply Chain Management: AnInternational Journal., 13(1), 35–48.

Folan, P., & Browne, J. (2005). Development of an extended enterprise performancemeasurement system. Production Planning and Control, 16(6), 531–544.

M.-J. Verdecho et al. / Expert Systems with Applications 39 (2012) 626–637 637

Giannakis, M. (2007). Performance measurement of supplier relationships. SupplyChain Management: An International Journal, 12(6), 400–411.

Handfield, R. B., & Bechtel, C. (2004). Trust, power, dependence, and economics: CanSCM research borrow paradigms? International Journal of Integrated SupplyChain Management, 1(1), 3–32.

Kampstra, R. P., Ashayeri, J., & Gattorna, J. L. (2006). Realities of supply chaincollaboration. The International Journal of Logistics Management, 17(3), 312–330.

Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard – Measures that driveperformance. Harvard Business Review, 70(1), 71–79.

Kaplan, R. S., & Norton, D. P. (2004). Strategy maps: Converting intangible assets intotangible outcomes. Boston: Harvard Business School Publishing.

Kirytopoulos, K., Leopoulos, V., & Voulgaridou, D. (2008). Supplier selection inpharmaceutical industry. An analytic network process approach. Benchmarking:An International Journal, 15(4), 494–516.

Lam, P. K., & Chin, K. S. (2005). Identifying and prioritizing critical success factors forconflict management in collaborative new product development. IndustrialMarketing Management, 34, 761–772.

Lejeune, M. A., & Yakova, N. (2005). On characterizing the 4 C’s in supply chainmanagement. Journal of Operations Management, 23(1), 81–100.

Levy, J. K., & Taji, K. (2007). Group decision support for hazards planning andemergency management: A group analytic network process (GANP) approach.Mathematical and Computer Modelling, 46, 906–917.

Lockamy, A., & McCormack, K. (2004). The development of a supply chainmanagement process maturity model using the concepts of business processorientation. Supply Chain Management: An International Journal, 9(4), 272–278.

Matopoulos, A., Vlachopoulou, M., Manthou, V., & Manos, B. (2007). A conceptualframework for supply chain collaboration: empirical evidence from the agri-food industry. Supply Chain Management: An International Journal, 12(3),177–186.

Mentzer, J. (2001). Supply chain management. Thousand oaks. California: SagePublications, Inc., pp. 83–84.

Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits onour capacity for processing information. Psychological Review, 63, 81–97.

Min, S., Roath, A. S., Daugherty, P. J., Genchev, S. E., Chen, H., & Arndt, A. D. (2005).Supply chain collaboration: What’s happening? The International Journal ofLogistics Management, 16(2), 237–256.

Mohr, J. J., & Spekman, R. E. (1996). Perfecting partnerships. Marketing Management,4(4), 34–43.

Peniwati, K. (2007). Criteria for evaluating group decision-making methods.Mathematical and Computer Modelling, 46(7/8), 935–947.

Saaty, TL. (1980). The analytic hierarchy process. Pittsburgh, PA: RWS Publications.Scott-Morton, M. S. (Ed.). (1991). The corporation of the 1990s: Information

technology and organizational transformation. New York: Oxford UniversityPress.

Saaty, T. L. (1996). The analytic network process: Decision making with dependenceand feedback. Pittsburgh, PA: RWS Publications.

Saaty, T. L. (2001). Decision making with dependence and feedback: The analyticnetwork process. Pittsburgh, PA: RWS Publications.

Sabath, R. E., & Fontanella, J. (2002). The unfulfilled promise of supply chaincollaboration. Supply Chain Management Review, July/August.

Simatupang, T. M., & Sridharan, R. (2002). The collaborative supply chain. TheInternational Journal of Logistics Management, 13(1), 15–30.

Simatupang, T. M., & Sridharan, R. (2005). An integrative framework for supplychain collaboration. The International Journal of Logistics Management, 16(2),257–274.

Spekman, R. B., Kamauff, J. W., Jr., & Myhr, N. (1998). An empirical investigation intosupply chain management. A perspective on partnerships. International Journalof Physical Distribution & Logistics Management, 28(8), 630–650.

Suwignjo, P., Bititci, U. S., & Carrie, A. S. (2000). Quantitative models for performancemeasurement system. International Journal of Production Economics, 64(1/3),231–241.

Talluri, S., & Sarkis, J. (2002). A methodology for monitoring systems performance.International Journal of Production Research, 40(7), 1567–1582.

Verdecho, M. J., Alfaro, J. J., & Rodriguez-Rodriguez, R. (2009). Foundations forcollaborative performance measurement. Production Planning and Control, 20(3),193–205.

Wu, W. Y., Shih, H. A., & Chan, H. C. (2009). The analytic network process for partnerselection criteria in strategic alliances. Expert Systems with Applications, 36,4646–4653.

Yuksel, I., & Dagdeviren, M. (2010). Using the fuzzy analytic network process (ANP)for balanced scorecard (BSC): A case study for a manufacturing firm. ExpertSystems with Applications, 37, 1270–1278.

Yurdakul, M. (2003). Measuring long-term performance of a manufacturing firmusing the analytic network process (ANP) approach. International Journal ofProduction Research, 41(11), 2501–2529.