a multi-criteria approach for managing inter-enterprise collaborative relationships

Omega 40 (2012) 249–263

Contents lists available at ScienceDirect

Omega

0305-04

doi:10.1

n Corr

E-m

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

A multi-criteria approach for managing inter-enterprisecollaborative relationships

Marıa-Jose Verdecho n, Juan-Jose Alfaro-Saiz, Raul Rodriguez-Rodriguez, Angel Ortiz-Bas

Universitat Polit�ecnica de Val�encia, Department of Business Organization, Camino de Vera, s/n 46022 Valencia, Spain

a r t i c l e i n f o

Article history:

Received 11 March 2011

Accepted 7 July 2011Available online 20 July 2011

Keywords:

Decision-making/process

Multicriteria

Management

Case study

83/$ - see front matter & 2011 Elsevier Ltd. A

016/j.omega.2011.07.004

esponding author. Tel.: þ34963877680; fax:

ail address: [email protected] (M.-J. Ve

a b s t r a c t

Collaboration amongst enterprises is a common strategy used to increase competitiveness. Thus,

enterprises that are collaborating need to define and use performance measurement/management

frameworks composed of performance elements (objectives, performance indicators, etc.) that facilitate

the management of their activity, as well as monitor their strategy and processes. There are many

factors, e.g. trust, interoperability of Information Systems, etc. that need to be managed properly in

order to support collaborative success. However, such factors and performance are not usually

managed together. Furthermore, these factors and performance elements are interrelated but these

influences are commonly overlooked. This paper aims to present an approach based on the Analytic

Network Process (ANP) to manage collaborative relationships under an integrated approach by

considering not only the inter-enterprise performance elements, but also the factors that influence

collaboration. The approach is then applied to a collaborative enterprise network belonging to the

renewable energy sector. With this innovative approach, enterprises will obtain significant information

for the decision-making process, regarding which are the factors and performance elements that have

the greatest impact on their competitiveness, and therefore should be prioritized.

& 2011 Elsevier Ltd. All rights reserved.

1. Introduction

The importance of establishing collaboration between enterpriseshas been widely studied in the literature [1–3]. Many of the studieshighlight the benefits of inter-enterprise collaboration, which mainlydeals with the performance improvement/management in key inter-enterprise performance indicators such as faster cycle times, flexiblecustomer response, increased cash-to-cash velocity, etc. [4]. Otherwork has focused on identifying and analyzing factors that influencecollaboration such as trust, commitment, information sharing,etc. [5,6]. This is because collaborative relationships do not alwaysprovide enterprise success. There are various factors that need to beproperly managed in order to achieve an effective collaboration. Ifthese factors are not managed, it is possible that this type ofrelationship can result in problems such as internal and externalconflicts, loss of customer satisfaction and cost increase [7,8]. For thatreason, it is necessary to jointly manage these factors and perfor-mance of the collaborative enterprises.

In the literature there are different frameworks for inter-enterprise performance measurement [9,10]. One of the mostimportant performance measurement frameworks is the BalancedScoreCard (BSC) by Kaplan and Norton [11]. The BSC has been

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þ34963877689.

rdecho).

adapted by different authors for inter-enterprise performancemanagement, examples being the works developed by Brewerand Speh [12], Bititci et al. [13], Folan and Browne [14], Alfaroet al. [15], etc. These performance frameworks present in theirstructure different performance elements (objectives, performanceindicators, etc.) in order to provide a coherent deployment fromthe strategic to the operational level that facilitates performancemanagement. However, these frameworks present some researchgaps in the effective management of collaborative relationships,corresponding to some research questions [9,16,17]: what are therelevant factors of collaborative relationships? How can thesefactors be associated to a performance measurement framework?How are these factors and performance elements linked together?How should both factors and performance elements be measured?,etc. In addition, it is suggested that all the enterprises collaboratingshould adopt a business process approach, as the main goal ofeach business process is to satisfy customer requirements. Thus,the design of performance frameworks for these contexts shouldconsider structures for managing not only the strategic level of thecollaboration but also the inter-enterprise business processes[10,13,18,19]. Developing such a performance framework structureprovides an overall picture of the deployment of the strategy fromthe strategic level to the process level.

The association of factors and performance elements meansthat there is a system of reciprocal influences working betweenboth the aspects (i.e. factors and performance elements) that are

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M.-J. Verdecho et al. / Omega 40 (2012) 249–263250

to be taken into account. Besides, both factors and performanceelements present interrelationships within themselves at theinternal level. This means that some of the factors impact onother factors and some of the performance elements impact onother performance elements, thus configuring a complex networkof influences that has been overlooked in the literature. For thatreason, it is necessary to develop an approach that identifies andmeasures both the factors and the inter-enterprise performanceelements in an integrated manner, and to make explicit theoverall real influences that exist within the system so thataccurate and significant results are obtained.

From a methodological point of view, it is necessary to select amethod to solve this issue. Multi-criteria decision analysis (MCDA)methods are widely used for solving problems involving variouscriteria and multiple agents [20–30]. One of the most commonlyused MCDA methods is the Analytic Network Process (ANP) intro-duced by Saaty [31]. There are three main reasons that suggest usingANP to model and solve the problem of this paper. Firstly, ANPallows modeling complex problems with a network structure,integrating interdependences and feedback among their elements.Secondly, ANP is adequate for solving problems with both qualita-tive and quantitative factors [32]. This is an important characteristicas many of the collaboration factors are qualitative such as culturalfactors, and many of the methods are mainly developed forquantitative measurement. Thirdly, ANP has been used in group-decision problems [33–35] that is the case of a collaborativerelationship.

The main aim of the paper is the development of an approach(methodology) that helps to manage collaborative relationshipsby identifying and measuring, under an integrated approach,both the factors and the inter-enterprise performance elements,considering their reciprocal and internal relationships. Thestructure of this paper is as follows. Firstly, the research metho-dology followed for the development of this research is explained.Secondly, a literature review regarding relevant factors of colla-borative relationships and the application of ANP for performancemeasurement are analyzed. Thirdly, the proposed approach formanaging collaborative relationships is described. Then, theapplication of the methodology to a case study is described.Finally, conclusions and research implications are presented.

2. Research methodology

This research follows a constructivist research methodology,which is founded on problem solving by building organizationalprocedures or models [36–38]. The constructivist methodologycomprises different phases depending on the research nature.Generally, it consists of the following phases [37]: find a problemwith relevant practical application and obtain a general andcomplete understanding of the main topic; develop the proposal;apply and show that the proposal works; and finally, showtheoretical connections, the proposal contribution and futureresearch lines. Firstly in this paper, the relevant literature isanalyzed to obtain a sound understanding of the factors influen-cing collaborative relationships as well as their relationships.These factors are structured in a coherent manner by building aconceptual framework that would expand their identification,description, organization and analysis. Additionally, the literatureregarding the use of ANP in performance measurement/manage-ment applications for inter-enterprise contexts is reviewed inorder to find out which are the main research gaps for integratingthe management of collaborative factors and performance undera solid proposal. Based upon the findings of the research, anapproach has been developed, which can be used to managecollaborative relationships. The approach consists of five phases

(see Section 4). This approach was then applied to a collaborativenetwork in the renewable energy sector in Spain, which iscurrently a sector of high potential growth and interest. This casestudy provided a real opportunity to validate the proposal as wellas getting relevant feedback to the initial proposal. The applica-tion was done over a set of eight sessions/meetings wheremanagers of all the collaborative enterprises from the networkparticipated in a group of experts. The objectives of the meetingswere:

1.
To understand the main phases of the approach, its mainbenefits and the milestones for its application.
2.
To provide the team with a place and time to reflect on what isimportant for their collaboration considering the viewpoints ofall the members, following a structured approach.
3.
To analyze and discuss the priority factors and performanceelements resulting from the application.
In all sessions, the authors of this paper acted in consultantand moderator roles. The planning of each session consisted ofexplaining the main objectives of each session, describing themain aspects, providing training on the terminology used and theprocedure to be followed for the application development. Duringthe sessions, the managers discussed the main topic of the sessionand responses were annotated. The last session consisted ofpresenting results, their validation and the drawing of generalconclusions. Finally, overall conclusions where developed, whichwere gathered both during the period of development andmaturation of the theoretical proposal, and its application to thecollaborative enterprise network.

3. Literature review

3.1. Relevant factors of collaborative relationships

There are numerous works within the literature that deal withidentifying the main factors of inter-enterprise relationships.Some of these works present classifications of inter-enterpriseenvironments according to the level of maturity reached indifferent aspects of their relationships, i.e. they present supplychain evolutionary models (from lower to higher level ofcollaboration). Sabath and Fontanella [39] present a supply chainclassification depending on two main aspects: strategic value ofthe relationship, and the technology used to support it. From aprocess perspective, Lockamy and McCormack [40] develop amodel to classify supply chains based on the maturity of theirprocesses. Each level is characterized according to differentfactors such as alignment of processes, organizational structure,cooperation, process performance and trust. Lejeune andYakova [41] suggest a typology for supply chain characterizationrelated to social relationships theory and the interdependenceconcept.

Other works aim at identifying the main factors that impacton partnerships. Mohr and Spekman [42] identify, from anempirical study, the factors that contribute to successful partner-ships: relationship attributes (coordination, commitment, andtrust), communication behavior and joint problem solving tech-niques. Boddy et al. [43] identified seven factors for partneringcontexts: business processes, people, trust, technology, structure,financial resources and culture. Fig. 1 presents the ConceptualFramework of Collaboration (or Collaboration Pyramid) devel-oped by the authors. This framework structures and classifiesthe relevant factors that influence collaborative relationshipsbased on the literature review. The four-group classification isstructured by adapting the main blocks of the Massachusetts

COLLABORATION

Balance

STRATEGY

• Joint vision• Design of the inter-enterprise

supply chain/network• Equity• Top management support

CULTURE

• Trust• Commitment• Cooperationn• Information shared• Conflict management

BUSINESS PROCESSES

ORGANIZATIONALSTRUCTURE

• Collaboration leadership• Compatibility of management styles

ANDINFRASTRUCTURE

• Process alignment• IS/ICTs interoperability • Complementary skills

• Joint decision-making• Multidisciplinary teams

• Coordination between activities

Fig. 1. Conceptual framework of collaboration.

M.-J. Verdecho et al. / Omega 40 (2012) 249–263 251

Institute of Technology (MIT) framework developed in 1990s,known as MIT’90s [44]. Despite the fact that the MIT’90sframework was developed for individual enterprise contexts, ifwe conceptualize a collaborative inter-enterprise context asan organization that pursues common objectives, its applicationis justified. The pyramid means that if collaboration is to beeffective and sustainable, it is necessary to manage and balancefour groups of factors: strategic, organizational structure, busi-ness process and infrastructure, and cultural factors. In addition,all groups of factors are interrelated reciprocally. Thus, a changein one factor causes an effect in the rest of factors linked to itthat have to adjust themselves in order to maintain the balanceor sustainability of the collaboration relationship. In addition, ithas to be noted that the strategic factors are located in anupper level due to the fact that strategic aspects (such as theneed of being competitive) are the origin of the collabora-tion relationship as well as the drivers of collaboration amongenterprises.

Collaboration strategy defines the strategic aspects of therelationship with the purpose of providing a common under-standing of what is desired to be achieved in the medium andlong term by the collaborative association, identifying the func-tion and role of each enterprise, defining the contribution, risksand share of benefits and formalizing the commitment of topmanagement in the relationship. This group comprises the factors[43,45,46]:

�
Joint vision. � Design of the inter-enterprise supply chain/network. � Equity. � Top management support.
Business process (BP) and infrastructure define the neces-sary requirements for adequate process development, as well asthe infrastructure of support needed to execute them. This groupconsists of the factors [39–51]:

�
Process alignment. � Information System/Information and Communications Technology
(IS/ICT) interoperability.

�
Complementary skills. � Coordination between activities.
Organizational structure is the hierarchical structure of thecollaborative organization, which comprises the definition ofauthorities, responsibilities, roles and tasks assigned to eachmember. Although each enterprise keeps its own organizationalstructure, it is necessary in some cases to define an inter-enterprise structure that not only allows fast decision-making,but needs to be capable of managing the complexity of thecollaborative association. It comprises the following factors[40,47,52–55]:

�
Collaboration leadership. � Compatibility of management styles.
�
Joint decision-making. � Multidisciplinary teams.
Culture is the ‘system of shared values and beliefs thatprovides the rules of behavior’ [56]. This group consists of thefollowing factors [1,45–48,55,57–59]:

�
Trust. � Commitment. � Cooperation. � Information shared. � Conflict management.
3.2. ANP for performance management

ANP has recently been applied to performance managementapplications at both intra-enterprise and inter-enterprise con-texts. At the intra-enterprise level, Talluri and Sarkis [60] developan ANP model integrated with traditional quality control inmanufacturing. The approach consists of a system to monitorthe performance of a manufacturing enterprise at the strategic,tactical and operational levels. Yurdakul [61] presents an ANPmodel to select those areas of higher success (priority areas)within a company, depending on the competitive strategy


(innovation, customization, cost reduction, etc.). Other applica-tions are described in [62,63]. Ravi et al. [64] defined an ANPmodel to evaluate different alternatives of reverse logistics forcomputer manufacturers using the four perspectives of the BSC.Other models use these four perspectives to evaluate the perfor-mance of a manager [65] or to select key performance indicatorsdepending upon the strategy [66].

Regarding the application of ANP for inter-enterprise perfor-mance management, various works aim to select the best alternativefor obtaining the highest supply chain performance [67–71]. Never-theless, few of these works consider relevant collaborative factorswithin their models, e.g. Agarwal et al. [70] consider the collabora-tion across each partner’s core business process. It also must benoted that thus far there is no model at the inter-enterprise contextable to manage both factors and performance elements whilstconsidering the relationships between them. In addition, the ANPmodels already developed that use BSC for managing performanceare only applied at the individual enterprise level (intra-enterpriselevel). Therefore, it is possible to affirm that there is not yet an ANPmodel developed for inter-enterprise collaboration managementthat integrates both collaboration factors, within a collaborationframework, and inter-enterprise performance under a structuredperformance framework such as BSC. Also, the ANP models that useBSC for intra-enterprise management only apply it at the strategiclevel. It is thus still necessary to connect the process level to thestrategic level in order to integrate and manage both levels together.For these reasons, this paper proposes a novel approach to fill thisresearch gap. Therefore, collaborating enterprises will obtain rele-vant information for aiding the decision-making process thatidentifies the factors and inter-enterprise performance elementsthat generate the highest impact, and thus will give greater priorityto those to increase competitiveness.

4. The proposed approach

The purpose of the approach is to identify and measure relevantcollaborative factors and inter-enterprise performance elements

Fig. 2. Inter-enterprise collaborative relationship management: elements and relationsh

referred to the web version of this article).

(at the strategic and process levels), considering their reciprocalimpact as well as their inner dependences. Fig. 2 shows how bothtypes of elements (factors and performance elements) are structuredas well as the relationships between them. Then, four types ofcollaboration factors are observed, as described in the Conceptualframework of collaboration. These factors present inner depen-dences (blue lines within blue circles) as well as outer dependences(blue arrows represent influences among types of factors and purpledouble-sense arrow represents influences among both factors andperformance elements). Two groups of performance elements (stra-tegic and process level elements) are also observed. Both groupshave inner dependences among the performance elements (greenlines within green diamonds) within each level as well as outerdependences (the blue arrow represents the influence of processlevel performance elements on strategic level performance elementsand the purple double-sense arrow represents influences amongboth factors and performance elements (as previously stated)).

The proposed methodology is composed of the following fivesteps (Fig. 3):

Step 1. Characterize the collaborative context. This step aimsat obtaining a general overview of the inter-enterprise envir-onment and, specifically the depth and width of the collabora-tion relationships amongst their members.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 process profilesare people from all the enterprises that are collaborating andhave expertise in strategic and process issues. The consultantcan be either internal or external to the collaborative enter-prises and will act as organizer and moderator of the meetings.Step 3. Analyze and synthesize the relevant factors in colla-borative relationships. In this step, this study discusses theconceptual framework of collaboration reviewed in Section 3and the opinions of the expert team. As previously stated, theframework comprises factors grouped into four main blocks:strategic, business process and infrastructure, organizationalstructure, and cultural factors. The members of the group of

ips. (For interpretation of the references to color in this figure legend, the reader is

Step 1. Characterize the collaborative context

Step 3. Analyze and synthesize the Step 4. Definition of the inter-enterprise

Step 2. Establishment of the Group of Experts

relevant factors on collaborative relationships

Validation of Conceptual Framework of Collaboration

performance elements

Development and validation of the Inter-enterprise Performance Objective Map

Step 5. Obtain the priorities of the relevant factors on collaborative relationships and inter- enterprise performance elements

• Prioritization of Factors and Inter-enterprise performance objectives

• Analysis of results and sensitivity analysis

Fig. 3. Methodology: steps.


experts are responsible for reviewing the factors identified andvalidating that these factors are relevant for its specific colla-borative context. In addition, they can add any other factor thatthey consider relevant, e.g. specific factors within an industry.Step 4. Definition of the inter-enterprise performance ele-ments. This step consists of the definition of the performanceelements for the collaborative enterprises as well as theirinterdependences. This step comprises six activities.

Activity 4.1. Define the joint mission and vision of thecollaborative enterprises.

Activity 4.2. Define the stakeholder requirements (share-holders, suppliers, customers, staff and community) of the colla-borative enterprises.

Activity 4.3. Establish the performance objectives of thestrategic framework. These objectives should be in coherencewith the previous elements (mission and vision). The objectivesare defined for the four perspectives of the BSC: financial,customer, process, and innovation and learning.

Activity 4.4. Establish the performance objectives of the processframework. Once the strategic framework is obtained, the processframework, which contains the objectives of the key inter-enterpriseprocesses, can be developed. It is thus necessary that the group ofexperts identify the key processes in order to establish their perfor-mance objectives, which will be in coherence with the strategicperformance objectives and will be also set for the four perspectivesof the BSC: financial, customer, process, and innovation and learning.

Activity 4.5. Identify and represent influences amongst objectives.The objectives of the four perspectives present cause—effect relation-ships [72,73]. Thus, objectives are linked in a manner that theachievement of one objective influences the achievement ofthe objectives that are linked to it. In order to gain insight into theserelationships, it has been distinguished among four types ofinfluences, which jointly represented compose the Inter-enterprisePerformance Objective Map (Fig. 4). This map is structured into foursub-maps and each sub-map represents one type of influences asfollows:

1.
Influences amongst performance objectives belonging to thestrategic framework: sub-map of the Strategic FrameworkPerformance Objectives.
2.
Influences amongst performance objectives belonging to eachprocess of the process framework: sub-map of Process Perfor-mance Objectives.
3.
Influences amongst performance objectives of the processesbelonging to the process framework: sub-map of the ProcessFramework Performance Objectives.
4.
Influences amongst performance objectives of the strategicframework and process framework: sub-map of the StrategicFramework-Process Framework Performance Objectives.
Activity 4.6. Verify consistency among objectives. In thisactivity we check that objectives of lower perspectives supportthe objectives of upper perspectives [72], as well as that objec-tives of the process framework support the objectives of thestrategic framework [15].

Step 5. Obtain the priorities of the relevant factors in colla-borative relationships and inter-enterprise performance ele-ments. This final step aims at composing, solving and analyzingthe results of the ANP model. This step is composed of thefollowing activities:

Activity 5.1. Build the ANP model. Following the ANP method [31],factors and performance objectives are elements that can be struc-tured into clusters. There will be one cluster containing the perfor-mance objectives of the strategic framework. In addition, there willbe one cluster containing the objectives of each process within theprocess framework. Regarding the factors, there will be four clusterscontaining the four types of factors. The model is structured in thismanner to compare amongst 772 elements following the limitationof human capability to process information [74].

In order to determine inner and outer dependences both theInter-enterprise Performance Objective Map and the Matrix ofInfluences among elements are used. In Activity 4.5, influencesamong objectives are defined by building the Inter-enterprisePerformance Objective Map. For the rest of influences, the Matrixof Influences among elements is used in activity 5.1. This matrix isfilled in by the group of experts. This matrix is a Bnxn matrixwhere n¼performance objectives and collaboration factors. Therows of the matrix are the dominant elements and columns of thematrix are the dominated elements. Thus, if the element eijinfluences on element ekl, the cell (bij,kl) that intersects therow and column of both elements is filled in with a X. In othercase, there is not an influence, and the cell is filled in with a 0.In the case of the performance objectives, influences amongst alltypes of objectives where determined in Activity 4.5 by buildingthe Inter-enterprise Performance Objective Map. These influencesare easily translated to the Matrix of Influences among Elementsby introducing an X in the cells where influences were identified.

esirpretne--retnIPerformance Objective

Map

Sub-map of the Strategic FrameworkPerformance Objectives Sub-map of Process Performance Objectives

Sub-map of the Process FrameworkPerformance Objectives

Sub-map of the Strategic Framework-Process Framework Performance Objectives

Fig. 4. Inter-enterprise Performance Objective Map.


In the literature, there are matrices with similar characteristics foridentifying influences among elements such as the Inter-factorialDominance Matrix used in [75,76].

Activity 5.2. Complete the pairwise comparison matricesamong elements using the fundamental scale of Saaty [77]. Ingroup-decision making, Saaty [78] suggests using one of thefollowing three procedures: consensus (the members discussand negotiate the importance of the elements and provide aunique judgment), voting, or aggregation (when the membersparticipate in the evaluation on an individual basis). In order tofacilitate the comparison process, a questionnaire is provided tothe experts and afterwards responses are translated into thenumerical scale. Then, for each pairwise comparison matrix, theeigenvector (priority vector) is calculated and consistencychecked [78].

Activity 5.3. Compose the unweighted supermatrix with theaccepted priorities from activity 5.2.

Activity 5.4. Complete the pairwise comparison matricesbetween clusters following the same procedure as activity 5.2.Compose the cluster matrix with the priorities of the clusters.

Activity 5.5. Obtain the weighted supermatrix by multiplyingthe cells of the cluster matrix and the corresponding columns ofthe unweighted supermatrix. If the resulting matrix is notstochastic, it has to be normalized in columns so that it convergeswhen raised to powers.

Activity 5.6. Calculate the limit matrix. Raising the weightedsupermatrix to powers until it remains stable yields the limitmatrix. Then, global or final priorities of the factors and perfor-mance objectives are obtained.

Activity 5.7. Analyze results and perform sensitivity analysis tocheck if the solution obtained is robust enough.

5. Case study

The case study was carried out in a collaborative enterprisenetwork belonging to the renewable energy sector located inValencia, Spain. During recent years, this sector has gainedimportance within the Spanish economy mainly due to govern-ment regulations and the necessity to diversify energy sources.The methodology is explained with the application as follows:

Step 1. The enterprise network comprises enterprises withdifferent functions: raw material suppliers, sub-assemblysuppliers, engineering enterprise and promoter enterprise.Most of them work within different business sectors, in themain as follows; photovoltaic (PV) solar energy, wind energy,and thermoelectric energy. The application was performed atthe PV solar energy business unit dedicated to the design,construction, operation and maintenance of PV solar energyplants. This business unit is the most important one for thecollaborative enterprise network.Step 2. The group of experts includes people within three typesof skills: strategic, process and consultant. Strategic and processprofiles are operations, financial and project managers of theenterprises. There are two experts per enterprise: one expert hasthe strategic role (expert in strategy definition) and the otherexpert has the process role (expert in processes definition and


operation). The consultant can be either internal or external tothe collaborative enterprises and will act as organizer andmoderator of the meetings. The main author of the paper actedas consultant guiding the experts of the enterprises in how toapply the methodology. The experts from the enterprises haveactively participated in steps 3–5.Step 3. The experts reviewed the factors and concluded that allseventeen factors within the four groups were relevant to theircompetitiveness and therefore for the sustainability of theirrelationship. Table 1 shows the factor identifications.Step 4. Both the mission and vision of the PV solar business unitare determined by the experts. Specifically, the vision states‘‘Becoming national leader in the number of PV solar plants andhigh energetic performance, whilst decreasing costs’’.

The stakeholder’s requirements are then identified. For example,the customer’s requirements state ‘‘The acquisition of PV solarplants with high guaranteed profitability’’. After that, performanceobjectives of the strategic framework are defined for the financial,customer, process and innovation and learning perspectives(Table 2).

The three key inter-enterprise processes of the collaborativeenterprise network are: definition and feasibility study of theproject (P1), project execution (P2) and customer and other

Table 2Performance objectives of the strategic framework.

Perspective Performance Objective

Finance Increase 10% turnover (FSO1)

Increase 5% profitability (FSO2)

Customer Increase 15% the power installed by customer (CSO1)

Increase 10% the number of new customers by

recommendation of old customers (CSO2)

Process Increase 5% the performance ratio of the PV solar plant

(PSO1)

Reduce 20% total cycle time (PSO2)

Innovation and

learning

Include (at least) a PV module supplier in collaborative

relationship (ILSO1)

Increase 10% the degree of anticipation to industry

changes (legislation. technological. etc.) (ILSO2)

Table 1Relevant factors for the collaborative enterprise network.

Group Factor

Strategic factors Joint vision (SF1)

Design of the inter-enterprise supply chain/

network (SF2)

Equity (SF3)

Top management support (SF4)

Business process and

infrastructure factors

Process alignment (PF1)

IS/ICTs interoperability (PF2)

Complementary skills (PF3)

Coordination between activities (PF4)

Organizational structure factors Collaboration leadership (OF1)

Compatibility of management styles (OF2)

Joint decision-making (OF3)

Multidisciplinary teams (OF4)

Cultural factors Trust (CF1)

Commitment (CF2)

Cooperation (CF3)

Information shared (CF4)

Conflict resolution management (CF5)

stakeholders relationship management (P3). These processes havebeen modeled in BPMN notation in order to identify the activitiesof each partner. Fig. 5 shows the BPMN diagram of P1. Table 3shows the performance objectives for each process of the processframework defined for the financial, customer, process andinnovation and learning perspectives.

Next, the Inter-enterprise Performance Objective Map is devel-oped to show the relationships amongst the performance objec-tives. Fig. 6 shows the sub-map of the strategic frameworkperformance objectives. As can be observed, the objectives ofthe lower perspectives support the achievement of the upperperspective objectives. The objectives that receive the highestnumber of influences are ‘‘Increase turnover’’, belonging to thefinancial perspective and ‘‘Increase the number of new customersby recommendation of old customers’’, belonging to the customerperspective.

Step 5. Fig. 7 shows the BSC–ANP model for managingcollaborative relationships. The network consists of eight clustersgrouping the different elements previously identified. Arrowsshow the inner and outer dependences amongst their elements.Pairwise comparison matrices were obtained from the question-naire filled in by the expert team (by consensus).

Table 4 shows the pairwise comparison matrix of the elementsof the strategic factor cluster with respect to the ‘‘Increase 10%turnover’’ (FSO1) objective. The eigenvector indicates the impor-tance of each factor, and it can be observed that the‘‘Top management support’’ (SF4) factor holds the highest eigen-vector weight with 0.4854. Then, ‘‘Joint vision’’ (SF1), ‘‘Design ofthe inter-enterprise network’’ (SF2) and ‘‘Equity’’ (SF3) areweighted 0.3950, 0.0626 and 0.0570, respectively. In addition,the consistency ratio (CR) is 0.0123, which means that the expertswere consistent when making their judgments. The summary ofall weights from the pairwise comparison matrices is shown inthe unweighted supermatrix (Table 5).

Next, we obtain the cluster matrix that shows the prioritiesbetween clusters. The summary of weights between clusters isshown in the cluster matrix (Table 6).

By multiplying the unweighted supermatrix and the clustermatrix, we obtain the weighted supermatrix (Table 7). This isthen normalized and raised to powers until it converges, thusobtaining the limit supermatrix. Table 8 shows the limit priorities(LP) of factors and performance objectives.

5.1. Analysis of results

In order to analyze the obtained results, limit priorities arenormalized for each type of element (Tables 9 and 10): factorsand objectives. We then obtain the normalized limit priority(NLP) and the accumulated normalized limit priority (ANLP).In the tables, the last column indicates the type of element:critical (C), medium (M) or low (L) importance. The criticalelements are those comprising around 50% of ANPL. Theseelements are the most important for two reasons. On the onehand, those elements have the highest weights so they are themost influential ones within the network. On the other hand,those elements accumulate around 50% of the global weight. Themedium importance elements are those that remain between0.5 and 0.8 of the ANPL. Finally, the low importance elements arevalued between 0.8 and 1 of ANPL. These cut-off values wereestablished based on the expertise of the group of experts. It hasto be noted that the limit amongst classes is determined from theelement that is closest to 0.5 and 0.8 respectively. In Table 9, itcan be observed that the critical factors are ‘‘top managementsupport’’, ‘‘collaboration leadership’’, ‘‘joint vision’’, ‘‘trust’’ and‘‘commitment’’. Two of the critical factors belong to the strategiccluster, one factor to the organizational structure cluster and two

Fig. 5. BPMN diagram for the process: definition and feasibility study of the project (P1).


factors to the cultural cluster. The critical performance objectivesare (Table 10): ‘‘Increase 5% profitability’’, ‘‘Reduce 20% executioncosts’’, ‘‘Increase 10% turnover’’, ‘‘Increase 5% turnover throughstakeholder initiatives’’ belonging to the financial perspective,‘‘Increase 15% the power installed by customer’’ and ‘‘Increase 10%the number of new customers by recommendation of old custo-mers’’ belonging to the customer perspective, and ‘‘Increase 10% thedegree of anticipation to industry changes (legislation, technological,etc.)’’ and ‘‘Include (at least) a PV module supplier in collaborativerelationship’’ within the innovation and learning perspective. There-fore, the analysis shows how objectives that do not belong to thefinancial perspective achieve high priority because they have a largeinfluence on other performance objectives or factors.

5.2. Sensitivity analysis

It is important to analyze the sensitivity of the results obtainedin order to know if they are robust enough. Thus, a set ofsimulations has been performed over the eigenvectors obtained

from the pairwise comparison matrices amongst clusters.Table 11 shows a summary of the simulations performed. It hasto be noted that high and low weight perturbation clusters havebeen selected in order to cover all the types of results obtained.For every simulation, the following procedure is followed. Thefirst task is to select a cluster to be analyzed (select a column ofthe cluster matrix). Second, the cluster that is the origin of theperturbation is chosen (select a row of the cluster matrix) and itspriority is set from 0.05 to 0.995 (as priorities may be between0 and 1). Specifically, new priorities are 0.05, 0.2, 0.4, 0.6, 0.8 and0.995. In some cases, it is necessary to consider intermediatevalues in order to gain accuracy in the analysis. By selecting allthe range of priorities, the aim is to obtain not only the pointwhere the solution changes, but also the general trend of thevalues of the solution when large perturbations occur. Then, thepriority values of the rest of clusters on the same cluster analyzed(column cluster) are obtained so that the ratio among weights ofthe original solution is maintained. Next, the ANP model with thenew priorities is re-executed obtaining the new limit matrix.

Table 3Performance Objectives of the process framework.

Perspective Performance Objective

Process: definition and feasibility study of the project (P1)

Finance Increase 5% turnover per installed m2 (FPO11)

Reduce 7% construction costs by design (FPO12)

Customer Increase 20% customer service (CPO11)

Increase 30% the number of projects that include monitoring system by the customer (CPO12)

Process Increase 25% the number of projects that include high efficiency technologies (PPO11)

Reduce 30% the time of definition and feasibility study of the project (PPO12)

Innovation and learning Establish contrast meetings with PV module supplier every 6 months (ILPO11)

Implement a Knowledge Management System (ILPO12)

Process: project execution (P2)

Finance Reduce 20% execution costs (FPO21)

Customer Increase 10% customer satisfaction (CPO21)

Reduce 40% the number of customer modifications in on-going projects (CPO22)

Process Reduce 5% the time to obtain licenses (PPO21)

Reduce 30% the time of construction (PPO22)

Innovation and learning Establish a standard procedure of execution process (ILPO21)

Process: customer and other stakeholders relationship management (P3)

Finance Increase 5% turnover through stakeholder initiatives (FPO31)

Reduce 10% license costs (FPO32)

Customer Increase 20% stakeholder satisfaction (CPO31)

Process Increase 30% the supplier quality warrants (PPO31)

Increase 15% the number of approved initiatives (PPO32)

Innovation and learning Improve public administration relationships (ILPO31)

Build work teams with (at least) three suppliers (ILPO32)

Fig. 6. Sub-map of the Strategic Framework Performance Objectives.


After that, the new classification of factors and objectives isestablished, considering the same cut-off values. Finally, thenew results are discussed in comparison with the original ones.

The most restrictive case is simulation number 10. Thecolumns of Table 12 show the new eigenvectors for the CF clusteras well as the weight variation between the new weight and theoriginal weight ((wSF0 ,CF�wSF,CF)/wSF,CF)n100). After executing themodel, Table 13 shows the resulting limit priorities and classifica-tion for the factors when wSF0 ,CF equals to 0.05 and 0.2. In thesensitivity analysis, three types of changes may occur: composi-tion, rank and value. A composition change means that theelements change of classification among critical, medium and

low importance. These changes are the most relevant ones.A ranking change means that an element moves to a lower orhigher position in the ranking but remains within its class. Finally,a value change means that the only change is the priority value ofthe element but rank and class are kept. If we compare the newresults with the original ones illustrated in Table 10, we observetwo composition changes: CPO31, that was medium, changes to alow priority objective, and PPO32 that was low priority moves tomedium objective. These changes occur for both variations10.43% (wSF0 ,CF¼0.2) and 120.86% (wSF0 ,CF¼0.4) with respect tothe original priority 0.1811 (see Table 12). However, these arechanges that occur on the borders of the classes. It is therefore

FSO1FSO2CSO1

SO

CSO1CSO2PSO1PSO2ILSO1ILSO2

P1 P3

FPO31FPO11

P2

FPO21FPO32CPO31CPO31PPO32ILPO31ILPO32

FPO12CPO11CPO12PPO11PPO12ILPO11

CPO21CPO22PPO21PPO22ILPO21

SF PF OF CF

ILPO12

SF1SF2SF3SF4

PF1PF2PF3PF4

OF1OF2OF3OF4

CF1CF2CF3CF4CF5

Fig. 7. BSC–ANP model for managing collaboration relationships.

Table 4Pairwise comparison matrix for the FSO1 objective.

SF1 SF2 SF3 SF4 Eigenvector

SF1 1 5 7 1 0.3950

SF2 1/5 1 1 1/9 0.0626

SF3 1/7 1 1 1/9 0.0570

SF4 1 9 9 1 0.4854

CR 0.0123


necessary to analyze the rest of the priority changes. In addition,we see that there two rank changes between ILSO2 and FPO21 aswell as CSO2 and ILSO1 within the critical objectives when wemove from wSF0 ,CF¼0.2 to wSF0 ,CF¼0.4.

Fig. 8 shows the representation of the priorities trend of thecritical and medium objectives when the SF cluster increases itspriority. The values obtained in the analysis have been normal-ized to show them in a 0–1 scale. When the SF cluster priorityincreases, the SO cluster decreases its priority while the P2 clusterincreases it, mainly after 0.4. The biggest changes occur in theFPO21 and PPO22 critical objectives, which increase their priorityconsiderably. When the SF cluster priority decreases (up to 0.05),there are no changes in the original composition and classificationof objectives. On the other hand, when the SF cluster priorityincreases, some changes occur on the borders (as previouslyindicated regarding CPO31 and PPO32). Therefore, our suggestionis to monitor not only the critical and medium objectives but alsothe objective PPO32, as it tends to become a medium factor.However, we have to remind ourselves that it is an objectivelocated on the border of the classification ‘medium-low’. For thatreason it is not surprising that when the SF cluster priorityreaches the weight of 0.995 (almost the limit), PPO32 becomesa low priority objective again. A similar analysis is performed onfactors, changes of rank or composition that happen at higher SF

cluster priority than in the objective case and are also focused onfactors located on the border.

To sum-up, in the most restrictive case a change of 10.43%(at least) on the cluster priority is necessary to initiate composi-tion changes of objectives and factors located on the border.Taking into account that the weight difference of two consecutiveobjectives or factors located on the border is lower that 0.02%,the system is considered stable and the obtained solution isrobust.

From the case study, we can highlight four main points. Firstly,the group of experts from the enterprises acknowledged that theimplementation of this approach has provided detailed insightinto the definition, understanding, linkages and analysis of all thefactors and performance elements that are to be managed for thesustainability of their enterprise network. This is a key issue, asmany times a structured approached is not used, thus resulting inrelevant aspects being forgotten. Secondly, the application of thisapproach is founded on consensus achievement among the groupof experts of the different collaborative enterprises. This some-times presents conflicts of interest (for example when definingthe common performance objectives) that have to be managedwithin the meetings. That is one of the reasons why the con-sultant role is important, so that the experts that take part in thegroup can negotiate until consensus is reached. Thirdly, from aresource point of view, the group of experts have noted that theeight sessions (and a total of 25 hours) needed to apply theapproach are considered satisfactory due to the relevance of thepoints treated and the results obtained, as they directly affectstrategic aspects and the improvement of the efficiency andeffectiveness of the network. Fourthly, it has to be noted thatthe collaborative network had not implemented a performancemanagement framework. They used performance objectives andindicators, but not coherently defined under a framework. Now,with the implementation of this approach, they are interested infollowing its definition, evolution and management.

Table 5Unweighted supermatrix.

SO y CF

FSO1 FSO2 CSO1 CSO2 y CF1 CF2 CF3 CF4 CF5

SO FSO1 0.0000 0.0000 0.0000 0.0000 y 0.2261 0.1994 0.1544 0.1414 0.0670

FSO2 0.0000 0.0000 0.0000 0.0000 y 0.3656 0.3507 0.2293 0.3284 0.1904

CSO1 0.2500 1.0000 0.0000 0.0000 y 0.1083 0.1109 0.0582 0.0551 0.0842

CSO2 0.7500 0.0000 0.0000 0.0000 y 0.1412 0.0886 0.1816 0.1465 0.0772

y y y y y y y y y y y y

CF CF1 0.1859 0.3603 0.1844 0.1924 y 0.0000 0.5294 0.3031 0.4874 0.5769

CF2 0.2877 0.2198 0.1844 0.2425 y 0.0736 0.0000 0.1296 0.1182 0.1807

CF3 0.2528 0.1463 0.2621 0.2425 y 0.2845 0.1377 0.0000 0.2762 0.1263

CF4 0.1837 0.2296 0.2106 0.1924 y 0.3210 0.2122 0.3889 0.0000 0.1161

CF5 0.0899 0.0440 0.1585 0.1302 y 0.3210 0.1207 0.1783 0.1182 0.0000

Table 6Cluster matrix.

SO P1 P2 P3 SF PF OF CF

SO 0.2822 0.0000 0.0000 0.0000 0.1165 0.0966 0.2559 0.2640

P1 0.0670 0.2893 0.0547 0.0540 0.0253 0.0267 0.0339 0.0363

P2 0.1701 0.0850 0.0910 0.0923 0.1238 0.1259 0.0901 0.0750

P3 0.0926 0.0989 0.0843 0.1323 0.0883 0.0667 0.0555 0.1101

SF 0.1461 0.2416 0.2680 0.1380 0.1557 0.2597 0.1687 0.1811

PF 0.0362 0.0959 0.0897 0.1298 0.1335 0.1317 0.0974 0.0708

OF 0.0905 0.0685 0.0935 0.1754 0.1495 0.0972 0.1327 0.1056

CF 0.1153 0.1208 0.3188 0.2782 0.2074 0.1955 0.1659 0.1572

Table 7Weighted supermatrix.

SO y CF

FSO1 FSO2 CSO1 CSO2 y CF1 CF2 CF3 CF4 CF5

SO FSO1 0.0000 0.0000 0.0000 0.0000 y 0.0597 0.0526 0.0407 0.0373 0.0177

FSO2 0.0000 0.0000 0.0000 0.0000 y 0.0965 0.0926 0.0605 0.0867 0.0503

CSO1 0.0705 0.2822 0.0000 0.0000 y 0.0286 0.0293 0.0154 0.0145 0.0222

CSO2 0.2116 0.0000 0.0000 0.0000 y 0.0373 0.0234 0.0479 0.0387 0.0204

y y y y y y y y y y y y

CF CF1 0.0214 0.0416 0.0288 0.0222 y 0.0000 0.0832 0.0476 0.0766 0.0907

CF2 0.0332 0.0254 0.0288 0.0280 y 0.0116 0.0000 0.0204 0.0186 0.0284

CF3 0.0292 0.0169 0.0410 0.0280 y 0.0447 0.0216 0.0000 0.0434 0.0198

CF4 0.0212 0.0265 0.0330 0.0222 y 0.0504 0.0334 0.0611 0.0000 0.0182

CF5 0.0104 0.0051 0.0248 0.0150 y 0.0504 0.0190 0.0280 0.0186 0.0000

Table 8Limit priorities.

Factors

SF1 0.0654 PF1 0.0140 OF1 0.0681 CF1 0.0540

SF2 0.0176 PF2 0.0097 OF2 0.0230 CF2 0.0444

SF3 0.0179 PF3 0.0367 OF3 0.0135 CF3 0.0388

SF4 0.0950 PF4 0.0395 OF4 0.0195 CF4 0.0413

CF5 0.0205

Performance objectives

FSO1 0.0217 FPO11 0.0070 FPO21 0.0226 FPO31 0.0185

FSO2 0.0399 FPO12 0.0050 CPO21 0.0164 FPO32 0.0076

CSO1 0.0209 CPO11 0.0054 CPO22 0.0112 CPO31 0.0098

CSO2 0.0199 CPO12 0.0017 PPO21 0.0137 CPO31 0.0056

PSO1 0.0061 PPO11 0.0065 PPO22 0.0143 PPO32 0.0098

PSO2 0.0093 PPO12 0.0054 ILPO21 0.0115 ILPO31 0.0169

ILSO1 0.0186 ILPO11 0.0129 ILPO32 0.0149

ILSO2 0.0245 ILPO12 0.0035


6. Conclusions and research implications

This study has developed an approach for managing relevantfactors in collaborative relationships and performance elementsunder an integrated approach. This approach solves the limita-tions of the literature that deal with collaboration relationshipswhere it can be observed that there is a lack of mechanismsthat allow the management of the complexity of these contexts.It is necessary to jointly manage factors and performance elementsconsidering their reciprocal impact as well as their internaldependences, as the overall existing influences may affect to thefinal results. Thus, this proposal, on the one hand, structures thosefactors under a solid framework and, on the other hand, it associatesthis framework to a balanced and coherent performance structure,such as BSC, for inter-enterprise contexts.

Table 10Classification of performance objectives.

Rank Performance objectives LP NLP ANLP (%) Class

1 FSO2 Increase 5% profitability 0.0399 0.1047 10.47

2 ILSO2 Increase 10% the degree of anticipation to industry changes 0.0245 0.0643 16.89

3 FPO21 Reduce 20% execution costs 0.0226 0.0593 22.82

4 FSO1 Increase 10% turnover 0.0217 0.0569 28.51

5 CSO1 Increase 15% the power installed by customer 0.0209 0.0548 33.99 C

6 CSO2 Increase 10% the number of new customers by recommendation of old customers 0.0199 0.0521 39.21

7 ILSO1 Include (at least) a PV module supplier in collaborative relationship 0.0186 0.0488 44.09

8 FPO31 Increase 5% turnover through stakeholder initiatives 0.0185 0.0487 48.95

9 ILPO31 Improve Public Administration relationships 0.0169 0.0443 53.38 M

10 CPO21 Increase 10% customer satisfaction 0.0164 0.0431 57.68

11 ILPO32 Build work teams with (at least) three suppliers 0.0149 0.0390 61.58

12 PPO22 Reduce 30% the time of construction 0.0143 0.0376 65.35

13 PPO21 Reduce 5% the time to obtain licenses 0.0137 0.0359 68.94

14 ILPO11 Establish contrast meetings with PV module supplier each 6 months 0.0129 0.0338 72.32

15 ILPO21 Establish a standard procedure of execution process 0.0115 0.0302 75.34

16 CPO22 Reduce 40% the nr. of customer modifications in on-going projects 0.0112 0.0293 78.27

17 CPO31 Increase 20% stakeholder satisfaction 0.0098 0.0258 80.84

18 PPO32 Increase 15% the number of approved initiatives 0.0098 0.0256 83.41

19 PSO2 Reduce 20% total cycle time 0.0093 0.0245 85.86

20 FPO32 Reduce 10% license costs 0.0076 0.0199 87.84

21 FPO11 Increase 5% turnover per installed m2 0.0070 0.0183 89.68

22 PPO11 Increase 25% the nr of projects with high efficiency technologies 0.0065 0.0171 91.38

23 PSO1 Increase 5% the performance ratio 0.0061 0.0161 92.99

24 PPO31 Increase 30% the supplier quality warrants 0.0056 0.0148 94.47 L

25 PPO12 Reduce 30% the time of definition and feasibility study of the project 0.0054 0.0143 95.89

26 CPO11 Increase 20% customer service 0.0054 0.0142 97.32

27 FPO12 Reduce 7% construction costs by design 0.0050 0.0130 98.62

28 ILPO12 Implement a Knowledge Management System 0.0035 0.0092 99.54

29 CPO12 Increase 30% the nr of projects with monitoring system 0.0017 0.0046 100.00

Table 11Summary of simulations.

Simulation Nr.

1 2 3 4 5 6 7 8 9 10 11

Cluster origin SF PF OF CF P1 P2 P2 P1 P2 SF OF

Cluster analyzed SO SO SO SO PF SF CF SO P2 CF PF

Table 9Classification of factors.

Rank Factors LP NLP ANLP (%) Class

1 SF4 Top management support 0.0950 0.1536 15.36

2 OF1 Collaboration leadership 0.0681 0.1099 26.35

3 SF1 Joint vision 0.0654 0.1056 36.91 C

4 CF1 Trust 0.0540 0.0872 45.63

5 CF2 Commitment 0.0444 0.0717 52.80

6 CF4 Information shared 0.0413 0.0668 59.48

7 PF4 Coordination between activities 0.0395 0.0639 65.87

8 CF3 Cooperation 0.0388 0.0627 72.13 M

9 PF3 Complementary skills 0.0367 0.0593 78.06

10 OF2 Compatibility of management styles 0.0230 0.0372 81.78

11 CF5 Conflict management 0.0205 0.0332 85.10

12 OF4 Multidisciplinary teams 0.0195 0.0316 88.25

13 SF3 Equity 0.0179 0.0289 91.14

14 SF2 Design of the inter-enterprise supply chain/network 0.0176 0.0284 93.99 L

15 PF1 Process alignment 0.0140 0.0226 96.25

16 OF3 Joint decision-making 0.0135 0.0219 98.43

17 PF2 IS/ICTs interoperability 0.0097 0.0157 100

Table 12Eigenvectors for CF cluster.

SO 0.3062 0.2579 0.1934 0.1289 0.0645 0.0016

P1 0.0421 0.0354 0.0266 0.0177 0.0089 0.0002

P2 0.0870 0.0733 0.0549 0.0366 0.0183 0.0005

P3 0.1278 0.1076 0.0807 0.0538 0.0269 0.0007

SF’ 0.050 0.200 0.400 0.600 0.800 0.995

PF 0.0821 0.0692 0.0519 0.0346 0.0173 0.0004

OF 0.1225 0.1031 0.0774 0.0516 0.0258 0.0006

CF 0.1823 0.1535 0.1151 0.0768 0.0384 0.0010

Var. (%) �72.39 10.43 120.86 231.28 341.71 449.38


In addition, from a methodological point of view, enterprisesneed tools to improve decision-making processes, which intro-duce these aspects as well as their implementation. Therefore, a

methodological approach has been developed that guides thisprocess by structuring, in an ordered manner, the steps to befollowed for an adequate and efficient definition of all the

Table 13Results of sensitivity analysis for the performance objectives.

Rank Objective LP NLP ANLP (%) Class Objective LP NLP ANLP (%) Class

1 FSO2 0.0396 0.1045 0.1045 C FSO2 0.0363 0.1025 0.1025 C

2 ILSO2 0.0243 0.0641 0.1686 FPO21 0.0233 0.0657 0.1683

3 FPO21 0.0226 0.0598 0.2284 ILSO2 0.0219 0.0618 0.2301

4 FSO1 0.0215 0.0568 0.2852 FSO1 0.0199 0.0563 0.2864

5 CSO1 0.0207 0.0547 0.3399 CSO1 0.0188 0.0531 0.3395

6 CSO2 0.0197 0.0520 0.3919 ILSO1 0.0180 0.0507 0.3902

7 ILSO1 0.0185 0.0488 0.4407 CSO2 0.0179 0.0506 0.4408

8 FPO31 0.0184 0.0486 0.4892 FPO31 0.0161 0.0456 0.4865

9 ILPO31 0.0168 0.0443 0.5335 M ILPO31 0.0157 0.0443 0.5308 M

10 CPO21 0.0163 0.0431 0.5766 CPO21 0.0154 0.0434 0.5742

11 ILPO32 0.0147 0.0389 0.6155 PPO22 0.0142 0.0400 0.6142

12 PPO22 0.0143 0.0378 0.6533 PPO21 0.0135 0.0382 0.6524

13 PPO21 0.0137 0.0361 0.6894 ILPO32 0.0133 0.0374 0.6898

14 ILPO11 0.0128 0.0337 0.7232 ILPO11 0.0116 0.0328 0.7226

15 ILPO21 0.0114 0.0302 0.7533 CPO22 0.0109 0.0307 0.7533

16 CPO22 0.0111 0.0294 0.7827 ILPO21 0.0107 0.0303 0.7836

17 CPO31 0.0097 0.0257 0.8085 PPO32 0.0091 0.0258 0.8094

18 PPO32 0.0097 0.0257 0.8341 L CPO31 0.0090 0.0254 0.8348 L

19 PSO2 0.0092 0.0244 0.8585 PSO2 0.0081 0.0230 0.8578

20 FPO32 0.0076 0.0199 0.8784 FPO32 0.0074 0.0210 0.8788

21 FPO11 0.0070 0.0184 0.8968 FPO11 0.0067 0.0189 0.8977

22 PPO11 0.0065 0.0171 0.9139 PPO11 0.0060 0.0170 0.9148

23 PSO1 0.0060 0.0160 0.9299 PSO1 0.0053 0.0151 0.9299

24 PPO31 0.0056 0.0148 0.9446 PPO31 0.0052 0.0147 0.9446

25 PPO12 0.0054 0.0143 0.9589 CPO11 0.0050 0.0140 0.9586

26 CPO11 0.0054 0.0142 0.9731 PPO12 0.0050 0.0140 0.9726

27 FPO12 0.0049 0.0131 0.9862 FPO12 0.0048 0.0136 0.9862

28 ILPO12 0.0035 0.0092 0.9954 ILPO12 0.0032 0.0092 0.9953

29 CPO12 0.0017 0.0046 1.0000 CPO12 0.0016 0.0047 1.0000

wSF0 ,CF 0.2 wSF0 ,CF 0.4

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Lim

it pr

iorit

ies

(L.P

.)

CF Cluster Sensitivity Analysis. Origin of Perturbation on SFCluster

0.0

0.1

0.05Experiments

0.2 0.4 0.6 0.8 0.995

Fig. 8. Sensitivity of critical and medium performance objectives under perturba-

tions of SF cluster on CF cluster.


elements. It also provides a structure for examining and classify-ing final results, focusing the attention on the factor and perfor-mance elements that have a high priority for the collaborativecontext. Also, a sensitivity analysis procedure based on simula-tions is presented to check if the final results are robust enough.

The approach is applicable to all types of collaborative inter-enterprise associations taking into account that the performanceelements definition will change depending on the specific contextanalyzed. In addition, some specific collaborative relationships mayconsider other factors, e.g. industry specific factors. Modification and

adaptations to be performed for these two reasons will enable theuse of this approach in other collaborative relationships.

The information coming from this study may be used for othermanaging purposes such as resource allocations depending onpriority of performance objectives and factors. Another interestingresearch line is to integrate the collaborative performance objectiveswith the individual enterprise objectives within an overall model.Therefore, future studies will further extend this work.

Acknowledgments

This work has been developed within the framework of aresearch project partially funded by the Polytechnic University ofValencia, titled ‘‘Design and Implementation of PerformanceMeasurement Systems within Collaborative Contexts for aidingthe Decision-making Process’’, reference PAID-06–08-3206.

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