A Literature Review of Decision-making Models and Approaches for Partner Selection in Agile Supply...

7/18/2019 A Literature Review of Decision-making Models and Approaches for Partner Selection in Agile Supply Chains 2011

http://slidepdf.com/reader/full/a-literature-review-of-decision-making-models-and-approaches-for-partner-selection 1/19

A literature review of decision-making models and approachesfor partner selection in agile supply chainsChong Wu a ,1 , David Barnes b ,n

a School of Management, Xiamen University, Xiamen 361005, PR Chinab Westminster Business School, University of Westminster, London NW1 5LS, UK

a r t i c l e i n f o

Article history:

Received 18 May 2011Received in revised form8 August 2011Accepted 26 September 2011Available online 12 October 2011

Keywords:Literature reviewPartner selectionAgile supply chainDecision-making

a b s t r a c t

The paper reviews the literature on supply partner decision-making published between 2001 and 2011,a period that has seen a signicant increase in work published in this eld. The progress made indeveloping new models and methods that can be applied to this task is assessed in the context of theprevious literature. Particular attention is given to those methods that are especially relevant for use inagile supply chains. The paper uses a classication framework that enables models intended for similarpurposes to be compared and tracked over time. It is also used to identify a number of gaps in theliterature. The ndings highlight an on-going need to develop methods that are able to meet thecombination of qualitative and quantitative objectives that are typically found in partner selectionproblems in practice.

& 2011 Elsevier Ltd. All rights reserved.

1. Introduction

In today’s highly competitive environment, enterprises need totake advantage of any opportunity to improve their performance.There has been growing recognition of the need for a rm to workclosely with its supply chain partners in order to optimize itsbusiness processes. A key step in the formation of any supplychain is that of supply partner selection ( Mikhailov, 2002 ), whichis reected in the growing research interest in this issue in recentyears. De Boer et al. (2001) ’s review of the literature on supplypartner decision-making represented pioneering work in that itclassies supplier selection methods according to different stagesof the supplier selection process. Since then two other literaturereview papers are particularly noteworthy. Aissaoui et al. (2007)adopted De Boer et al. (2001) ’s three-stage framework in theirliterature review. However, their focus was on the nal stage of

the selection process. More recently, Ho et al. (2010) reviewedmulti-criteria decision-making approaches used in supplier eva-luation and selection. However, they do so relatively uncriticallyand without employing any specic framework. As it is nearly adecade since De Boer et al. (2001) ’s paper, it now seems anappropriate time to revisit this issue.

During this time, the concept of the agile supply chain (ASC)has become increasingly important as means of achieving

a competitive edge in rapidly changing business environments(Lin et al., 2006 ). An ASC is a dynamic alliance of member

companies, the formation of which is likely to need to changefrequently in response to fast-changing markets ( Christopher andTowill, 2000 ; Wu and Barnes, in press ). Miles and Snow (1984)were amongst the rst to recognize the importance of supplypartners as rms increasingly adopted vertically disaggregatedforms. Their description of a ‘‘dynamic network’’ as a combinationof independent businesses, each contributing what it does best tothe network as a whole, foreshadowed the type of relationshipsthat are characteristic of ASCs. More recently, in an era of increased outsourcing, Huang et al. (2004) have emphasized theconcept of the virtual enterprise as an effective and viablesolution to the problem of fullling requirements in a globalmarket. In ASCs, companies must align with their supply partnersto streamline their operations, as well as working together to

achieve the necessary levels of agility throughout the entiresupply chain and not just within an individual company. Theincreasing importance of ASCs has focused more attention onsupply partner selection.

In ASCs, decision-making about partner selection is particu-larly challenging, because of the complexity of putting together anetwork under dynamic conditions. Researchers have generallyconcluded that the problem of supplier selection under suchconditions cannot be solved effectively and efciently unless itis broken down into several sub-problems, which can then eachbe addressed and solved individually. For example, Lorange et al.(1992) developed a two-stage supply partner selection approach:rst evaluating the degree of match with a candidate partner and

Contents lists available at SciVerse ScienceDirect

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

Journal of Purchasing & Supply Management

1478-4092/$ - see front matter & 2011 Elsevier Ltd. All rights reserved.

doi: 10.1016/j.pursup.2011.09.002

n Corresponding author. Tel.: þ 44 20 7911 5000x3426; fax: þ 44 20 7911 5703.E-mail addresses: [email protected] (C. Wu) ,

[email protected] (D. Barnes) .1 Tel.: þ 86 5922180776.

Journal of Purchasing & Supply Management 17 (2011) 256–274

http://www.elsevier.com/locate/pursup


http://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.pursup.2011.09.002

mailto:[email protected]











then analyzing the market potential, main competitors andsimulating worst-case scenarios after the formation of the part-nership. De Boer et al. (2001) characterized the supply chainpartner selection process as three main stages, comprising the‘‘criteria formulation’’ and ‘‘qualication’’ stages in which suitablepartners are identied, followed by the ‘‘choice’’ stage in which anal selection is made from amongst suitably qualied partners.Huang et al. (2004) propose a two-stage selection framework

based on the distinction between hard and soft factors in affectthe partner selection process. Stage one identies potentialpartner candidates who can meet the criteria of timeliness,quality and price for the required products or services. Stagetwo focuses on the assessment of their cooperation potential. Che(2010) also developed a two-phase model. In phase 1, suppliersare clustered according to their characteristics for meeting cus-tomer needs on multiple dimensions of cost, quality and time.In phase 2, a multi-criteria optimization mathematical model isconstructed on the basis of these clusters.

The aim of this paper is to review the literature on supplypartner selection decision-making published between 2001 and2011 and to place this in the context of previous work publishedin this eld. Particular attention is given to those methods thatmay be especially relevant for supply partner selection in agilesupply chains. In reviewing the literature published since 2001,the paper will apply the classication framework developed byLuo et al. (2009) and Wu and Barnes (in press) , based on De Boeret al. (2001) , identify any new trends in the literature andhighlight any gaps in the literature that would benet from futureresearch efforts.

Classication in science has properties that enable the repre-sentation of entities and relationships in structures that reectknowledge of the domain under consideration ( Kwasnik, 1999 ).Classication can also be helpful for the processes of knowledgediscovery and creation. In this paper, the classication method isapplied to the literature on partner selection in order to advanceour understanding of this eld of research and to facilitate thediscovery of new knowledge in the subject. In addition, classica-tions can also be used to enhance our ability to discover mean-ingful information in large amounts of literature. Recentdevelopments in our ability to retrieve large amounts of literaturehave stimulated an interest in new ways of exploiting theinformation available to advance the knowledge in this eld.

Subsequent to this Introduction, the ‘‘Methodology’’ sectionexplains how the literature review was conducted and in parti-cular how a phased model for supply partner selection in ASCswas used as the basis for the analysis. This model is then used asthe basis of the structure of the next four sections, which are

dedicated to reviewing the decision models considered appro-priate for each of the four phases. The ‘‘Discussion’’ sectionpresents the development trends of the decision-making modelsand approaches for partner selection in ASCs. The ‘‘Conclusion’’section draws the paper to an end by considering the contributionof the paper and pointing to future research requirements.

2. Methodology

Relevant papers were identied by searching ISI Web of Knowledge using the keywords ‘‘partner selection’’, ‘‘supplierselection’’ and ‘‘vendor selection’’ in the elds of ‘‘OperationsResearch & Management Science’’ and ‘‘Management’’ date from2001 to 2011 (up to 5 May 2011). The search returned onehundred and forty journal articles. These are listed in Appendix 1.

Once identied, the papers were classied using the frame-work developed by Luo et al. (2009) and Wu and Barnes (in press)based on De Boer et al. (2001) . This is depicted in Table 1 andFig. 1, and now described in more detail.

The horizontal axis of the framework categorizes the complex-ity and degree of uncertainty associated with purchasing andsupplier selection decisions. Based on the work of De Boer et al.(2001) and Robinson et al. (1967) , it characterizes three typicalsituations: new task, modied re-buy and straight re-buy. Thenew task situation involves an entirely new product or service. Asthere is no previous experience, this situation carries a high levelof uncertainty. In a modied re-buy, a new product is purchasedfrom a known supplier or a modied product is purchased from anew supplier. Therefore, this has a moderate level of uncertainty.Finally, the straight re-buy has the lowest level of uncertaintyas the buyer has near perfect information about the productspecication and the supplier.

Table 1The phases of partner selection framework (based on De Boer et al., 2001 ).

Phase New task Re-buy

Agile supply chain Modied re-buy Straight re-buy

1. Formulation of criteria No previously used criteria available Previously used criteria available Previously used criteria availableModerate initial set of partners Large set of initial partners Small set of partners

2. Qualication Sorting rather than ranking Sorting as well as ranking Sorting rather than rankingNo historical records available Historical data available Historical data available

3. Final selection Ranking rather than sorting Ranking rather than sorting Evaluation rather selectionMany criteria Fewer criteria Moderate criteriaMuch interaction Less interaction Moderate interactionModel used once Model used again Model used again

4. Application feedback Any new customer demands? Change current supply chain structure? Stronger the relationships?Modifying or rebuild the models used before? The performance of the current supply chain

structure fulls the demands?Any more alternatives?

Criteria formulation

Qualification

Final selection

Information available to purchasing enterprise HighLow

P o

t e n t i a l c o m

b i n a t i o n s

Few

F e e d b a c k

Many

Application feedback

Fig. 1. The phases of the partner selection framework (based on De Boer et al.,2001 ; Luo et al., 2009 ; Wu and Barnes, in press ).

C. Wu, D. Barnes / Journal of Purchasing & Supply Management 17 (2011) 256–274 257



The vertical axis of the framework uses the conceptual modelextended by Luo et al. (2009) and Wu and Barnes (in press) forASCs based on De Boer et al. (2001) ’s work, namely formulation of criteria, qualication, nal selection and application feedback. Useof this step-by-step approach offers an effective means of solvingwhat would otherwise be a highly complex problem. The struc-tured and comprehensive approach is necessary to meet thechallenge of partner selection in dynamic environment, and helps

ensure that successful partnerships will not be threatenedfor reasons of imperfect selection ( Zarvic and Seifert, 2008 ).The choice of these two axes is based on the following

considerations. For the horizontal axis, according to De Boeret al. (2001) , there are three types of rebuy and one new taskdecision-making situations. For simplicity, this paper combinesthe routine items straight rebuy decision-making situation andthe strategic/bottleneck straight re-buy decision-making situationinto one straight re-buy decision-making situation only. For thevertical axis, recent research has emphasized the importance of application feedback ( Wu and Barnes, 2009 ). As Christopher andTowill (2000) pointed out, such a phase is important andnecessary in today’s highly competitive environment. In incor-porating this phase, this framework represents an advance onprevious models of the partner selection process.

Analyzing the relevant papers identied from the literature inaccordance with these two dimensions, enables the variousdecision models associated with partner selection to be locatedwithin the framework. The framework is accordingly used as thebasis for categorization as it enables each model to be associatedwith a specic selection phase and situation. This enables modelsintended for similar purposes to be compared and be tracked overtime. It is also used to help identify the progress associated withparticular selection phases and purchasing situations made in theresearch literature in the last decade to be assessed and comparedto the best known literature published prior to 2001. Similarly,any gaps in the literature can also be identied.

For reasons of space, the discussions that follow do not reportin detail from all one hundred and forty journal articles. Ratherthey focus on those articles considered to be the most importantand typical of the decision-making situations and methods/models that they present. The choices of articles singled out formention are inevitably somewhat subjective, but they are inu-enced by considerations, inter alia , of the frequency of theirindividual citations and the prestige of both the publishing journal and the authors.

3. Decision models for the formulation of criteria

The formulation of criteria stage of the supply partner selec-tion process is that of determining what criteria to use insubsequent decision-making. Traditionally, the most importantpurchasing criterion has been that of cost. This has arguablybecome even more important in an era of global competition,when supplies can usually be sourced globally as well as locally.However, focusing on cost alone can betray a tactical rather thanstrategic approach to purchasing, which has led to its exclusionfrom the corporate agenda

In his classic research, Dickson (1966) argued that the vendorselection and evaluation process is multi-objective in nature.There is now widespread agreement that the main categories of partner selection criteria should correspond to the principalmanufacturing performance and competitive priorities of cost,quality, delivery and exibility ( Aksoy and Ozturk, 2011 ), as thesecan be equally or even more important, when supply has a directimpact on competitive performance and corporate strategy, as in

the case of innovative and unique products ( Sarkis et al., 2007 ).

Consequently, the criteria for developing supply chain partner-ships are typically driven by the expectation of quality, costefciency, delivery dependability, volume exibility, informationand customer service ( Rezaei and Davoodi, 2011 ). Thus, partnerselection in ASCs can be viewed as a multi-criteria decisionmaking problem that involves assessing trade-offs between con-icting tangible and intangible criteria ( Crispim and de Sousa,2009 ). Many authors have highlighted the importance of adopting

a broad set of criteria that encompass a long-term perspective(Dulmin and Mininno, 2003 ), which might include the ability of the partner to provide design and technological capabilities to thecustomer, expertise with the use of alloys, acceptance of smallorders, or product ranges ( van der Rhee et al., 2009 ).

Including a broad range of criteria, however, makes partnerselection decisions complex ( Weber et al., 1991 ). Tracey and Tan(2001) developed partner selection criteria, including quality,delivery, reliability, performance and price, and assessed custo-mer satisfaction based on price, quality, variety and delivery.Kannan and Tan’s (2002) partner selection method is based oncriteria of commitment, needs, capability, t and honesty. Theyhave also developed a partner evaluation system based on criteriaof delivery, quality, responsiveness and information sharing. Linet al. (2006) developed a fuzzy agility index, comprising attribute’ratings and corresponding weights, and is aggregated by a fuzzyweighted average. Xia and Wu (2007) proposed an integratedapproach to simultaneously determine the number of suppliers toemploy and the order quantity allocated to these suppliers, withmultiple criteria and with supplier’s capacity constraints. More so,as criteria may have quantitative as well as qualitative dimen-sions, and may also be conicting. Preference for a given partneris generally assumed to depend on an assessment, case-by-case,of the quality, price, delivery and service it offers. The number andthe set of the assessment criteria involved should depend on theproduct/service in question. However, considering a large numberof criteria can make supplier selection excessively complex andproblematic ( Zeydan et al., 2011 ). Consequently, researchers haveput much effort into methods that aim to develop a smaller, morecustomized set of attributes by determining the relative impor-tance of the selection criteria in various procurement situations.

There are relatively fewer examples in the literature of methods aimed at identifying the best criteria for partner selec-tion. Lewis (2002) models the supply chain partner selectionproblem by proposing a qualitative approach. Several criteriawere suggested, such as value added to products, operationsand technologies strengthening, and improvement in marketaccess, to measure the appropriateness of a particular strategicalliance for a rm. Lin and Chen (2004) proposed a systematicmethod to build a general set of criteria that can then be modiedfor a specic industry. Huang and Keskar (2007) present anintegration mechanism that takes into account product type,supplier type, and supplier integration level criteria, to producea set of comprehensive and congurable criteria for partnerselection by original equipment manufacturers. Wu and Barnes(2010) draw on Dempster-Shafer theories and optimization indeveloping a method for formulating criteria to use in partnerselection decision-making in ASCs. Their model offers a way of solving this problem under conditions of resource constraints.

In summary, the 23 criteria presented by Dickson (1966) canstill be used to classify the majority of the criteria for supplypartner selection presented in the more recent literature. Simi-larly, the result of Weber et al.’s (1991) review of 74 papersshowed that price, delivery, quality and production capacity andlocation were the most commonly cited criteria. However, itshould be acknowledged that an evolving competitive environ-ment might modify the relative importance of the criteria.

Furthermore, it is worth noting that most existing approaches

C. Wu, D. Barnes / Journal of Purchasing & Supply Management 17 (2011) 256–274258



do not take into account the dynamic interrelation betweenpartner selection and supply chain performances. Table 2 pre-sents a summary of representative studies on evaluation andformulation partner selection criteria in chronological order.

4. Decision models for qualication

The qualication stage involves reducing the set of all possiblepartners to a smaller set of acceptable suppliers ( De Boer et al.,2001 ). Sarkar and Mohapatra (2006) demonstrated that a reducedsolution space for partner selection is a prerequisite for con-structing closer relationship with partners. Therefore, qualica-tion is a sorting process rather than a ranking process. The rststep of this process always consists of dening and determiningthe set of acceptable partners while possible subsequent stepsserve to reduce the number of partners to consider.

The methods used for the qualication can be classied asfollows:

4.1. Data envelopment analysis models

Data envelopment analysis (DEA) is built on the concept of the efciency of a decision alternative. Weber et al. (1991, 1998)discussed the application of DEA in partner selection some yearsago. More recently, Wu and Blackhurst (2009) pointed out thatselecting suppliers is an essential part of effectively managingtoday’s dynamic supply chains. They proposed a supplier evalua-tion and selection methodology based on an extension of DEA,which they call augmented DEA. Through the incorporation of arange of virtual standards, the methodology enhances discrimina-tory power over basic DEA models. One of the advantages of theirmodel is that the weight constraints are used to reduce thepossibility of having inappropriate input and output factor weights.Wu (2009) presented a hybrid model using DEA, decision trees andneural networks to assess supplier performance. The model appliesDEA to classify suppliers into efcient and inefcient clusters basedon the resulting efciency scores and yield a favorable classica-

tion and prediction accuracy rate. Wu and Olson (2010) presented

the development and current status of enterprise risk managementand developed a DEA VaR model to conduct risk management invendor selection. Their models provided means to quantitativelyimprove decision making with respect to risk. Saen (2010) alsoproposed a DEA-based methodology for supplier selection. Thestrong point of her/his model is that it considers both undesirableoutputs and imprecise data simultaneously. To increase the sup-plier selection and evaluation quality, Zeydan et al. (2011) con-sidered both qualitative and quantitative variables in evaluatingperformance for selection of suppliers based on efciency andeffectiveness. In their model, qualitative variables are transformedinto a quantitative variable for using in DEA.

4.2. Cluster analysis models

Cluster analysis is a basic method from statistics. It uses aclassication algorithm to group a number of items which aredescribed by a set of numerical attribute scores into a number of clusters such that the differences between items within a clusterare minimal and the differences between items from differentclusters are maximal ( De Boer et al., 2001 ). Cluster analysis can beapplied to a group of partners that are described by scores onsome criteria too. The result is a classication of partners inclusters of comparable partners. Hinkle et al. (1969) were one of the rst researchers on adopt this approach. Ha and Krishnan(2008) introduced a hybrid method which incorporates multipletechniques into the partner evaluation process, in order to selectthe most competitive one(s) in supply chains. The proposedmodel is exible enough to allow the decision maker to do singlesourcing and multiple sourcing by calculating a combined sup-plier score. Furthermore, this method can draw the supplier mapto position suppliers within the qualitative and quantitativedimensions of performance efciency by performing a clusteranalysis. To effectively segment and select suppliers, Che (2010)developed a genetic simulated annealing k-means algorithm. Byusing the algorithm, the suppliers were clustered according to thecharacteristics for customer needs including multiple dimensionsproduct cost, product quality and manufacturing time. It is found

that select suppliers after cluster analysis, unwanted candidate

Table 2A summary of representative studies on evaluation and formulation criteria.

Researchers Respondents/empirical cases Measurement approach Main evaluation criteria

Dulmin andMininno(2003)

A mid-sized Italian public road and railrm

The PROMETHEE approach 1. Make-up; 2. Processing time; 3. Prototyping time; 4. Quality system;5. Co-design; 6. Technological levels

Lin and Chen(2004)

A international personal computercompany

Fuzzy framework 1. Finance; 2. HR management; 3. Industrial characteristic; 4. Knowledge/technology management; 5. Marketing; 6. Organizational competitiveness; 7.Product development and logistics; 8. Relationship building and coordination

Wang et al.(2004)

A hypothetical car manufacturerproducing various functionalcomponents

AHP (pairwisecomparisons)

1. Delivery performance; 2. Fill rate; 3. Lead time; 4. Perfect order fullment; 5.Supply chain response time; 6. Production exibility; 7. Total logisticsmanagement cost; 8. Value-added productivity; 10 warranty cost or returnsprocessing cost; 11. Case-to-cash cycle time; 12. Inventory days of supply; 14Asset turns

Lin et al.(2006)

A Taiwan based international ITproducts company

Fuzzy logic and aggregatefuzzy ratings and weights

1. Collaborative relationships; 2. Process integration; 3. Information integration;4. Customer/marketing sensitivity

Xia and Wu(2007)

Literatures and numerical examples AHP (pairwisecomparisons)

1. Price; 2. Technical level; 3. Defects; 4. Reliability; 5. On-time delivery; 6. Supplycapacity; 7. Repair turnaround time; 8. Warranty period

Kannan andHaq (2007)

A company southern India Interpretive structuralmodeling

1. Quality; 2. Delivery; 3. Production capability; 4. Service; 5. Engineering/technical capability; 6. Business structure; 7. Price

van der Rheeet al. (2009)

200 respondents from Germany, theUK, Italy and France

Discrete choice analysis 1. Flexibility; 2. Cost; 3. Delivery; 4. Value-added support; 5. Value-added service

Wu andBarnes(2010)

Literatures and interviews withoperations managers

Dempster-Shafer andoptimization theory

1. General Hierarchy Criteria; 2. Industry-oriented Hierarchy Criteria; 3. OptimalHierarchy Criteria




suppliers could be effectively eliminated, and the resultingsupplier combination could still meet customer needs. Two majordrawbacks exist in these cluster methods. Firstly, only global-scaled clusters have been veried. Secondly, the relationshipbetween global and local perspectives on cluster detection hasnot been explored.

4.3. Categorical models

On the whole, categorical methods belong to qualitative models.Partners are evaluated on a set of criteria based on historical dataand/or the enterprise’s own experience. The evaluation processconsists of categorizing the potential partner’s performance on acriterion as either ‘‘positive’’, ‘‘neutral’’ or ‘‘negative’’. After a poten-tial partner has been rated on all selected criteria, the decisionmakers give an overall rating by ticking one of the three optionsagain. In this way, potential partners are sorted into these threecategories. During this decade, there is few research using catego-rical approaches as quantitative methods dominating the area.

4.4. Articial intelligence models

Articial intelligence models are based on computer-aidedsystems which in one way or another can be ‘‘trained’’ by expertsor historical data. Subsequently, when non-experts who face similarbut new decision situations, they can consult the system models.Humphreys et al. (2003) presented a framework of environmentalcriteria that a company can consider during their supplier selectionprocess as environmental pressure increases. A knowledge-baseddecision support system which integrated both quantitative andqualitative environmental criteria into the supplier selection processwas built within the framework. Yet, it is very difcult to set anappropriate and reasonable acceptance threshold value.

Lee and Ou-Yang (2009) propose an articial neural network-based model to provide support and recommendations to buyersinvolved in partner selection negotiations. The authors have shown

that the ANN approach offers an adaptive negotiation support toolfor use in sophisticated and challenging negotiations that can helpachieve the buyer’s objective. However, the limitations of modelinclude an inadequate number of input factors and its predicationobjective (i.e. the bid price only). Luo et al. (2009) developed amodel that helps overcome the information processing difcultiesinherent in screening a large number of potential suppliers in theearly stages of the selection process. Based on radial basis functionarticial neural network, their model enables potential suppliers tobe assessed against multiple criteria using both quantitative and

qualitative measures. Aksoy and Ozturk (2011) presented an ANN-based supplier selection and supplier performance evaluation sys-tems to aid JIT manufacturers in selecting the most appropriatesuppliers and in evaluating supplier performance. One of advantagesof their ANN model is that decision-makers can see the points thatneed to be developed in the output value of the ANN model.

Another AI-technology used in supplier evaluation is expertsystems. Choy et al. (2002) present an intelligent partner relation-

ship management system using hybrid case based reasoning andANN techniques to select and benchmark potential partners. Yiginet al. (2007) also developed an expert system for partner selectionbased on six rules and fourteen criteria which are grouped step bystep. As the general characteristics of expert system, their methodcould never fully capture the expertise used in difcult situationsthat is common in ASCs partner selection.

Case-based-reasoning systems also belong to articial intelli-gence approach. Primarily, a case-based-reasoning system is asoftware-driven database that provides a decision-maker withuseful information and experiences from similar, previous deci-sion situations. Choy et al. (2004) used supplier relationshipmanagement system to integrate supplier rating system andproduct coding system by case-based-reasoning technique, toselect preferred suppliers during the new product developmentprocess. It is found that the outsource cycle time could be reducedand manufacturers can identied preferred suppliers to form asupply network effectively. Faez et al. (2009) combined integerprogramming, fuzzy set theories and CBR method for the venderselection program. Their model improved the covential CBR systems by covering the fuzzy parameters. Moreover, a mixedinteger programming model was applied to simultaneously con-sider suitable vendor selection and order allocation. Based on datamining techniques, Zhao and Yu (2011) mined the data in multidata resources in case-based-reasoning system to improve theautoimmunization level of knowledge acquisition, performance of the system, and expedite the exploring period of the intelligentsystem. However, the main problem with these models is com-plexity. In particular, as the numbers of cases increases, theefciency of decision-makings decreases very quickly.

Table 3 provides a summary of the models in the literature onthe qualication.

5. Decision models for nal selection

Final selection models involve selecting which of the qualiedpartners to use for specic purchases. Initial research in this area

Table 3A summary of representative studies on qualication.

Methods/

models

Key concept Representative works Strong/weakness points

DEA Efciency ¼ the ratio of the weighted sumof its outputs to the weighted sum of itsinputs

Weber et al. (1991, 1998) , Wu and Olson (2008), Saen(2009), Wu (2009) , Wu and Blackhurst (2009) , Azadeh andAlem (2010), Wu and Olson (2010) , Saen (2010) , Zeydanet al. (2011)

Te weight constraints are used to reduce thepossibility of having inappropriate input andoutput factor weights

Clusteranalysis

Differences between items within acluster are minimal; Differences betweenitems from different clusters are maximal

Hinkle et al. (1969) , Ha and Krishnan (2008) , Che (2010) Only global-scaled clusters have been veried.Relationship between global-local perspectiveson cluster detection has not been explored

Categoricalmodels

Potential partners are sorted into‘‘positive’’, ‘‘neutral’’ or ‘‘negative’’categories

None found Cannot be applied to a complex problem, suchas that represented by a hierarchical structureof decision attributes

Articialintelligence

‘‘Trained’’ computer-aided systems whichdo not require formalization of thedecision-making process

Humphreys et al. (2003) , Choy et al. (2002, 2004 ), Yiginet al. (2007) , Guo et al. (2009), Faez et al. (2009) , Lee andOu-Yang (2009) , Luo et al. (2009) , Montazer et al. (2009),Aksoy and Ozturk (2011) , Zhao and Yu (2011)

Can cope better with complexity anduncertainty than traditional models as itdesigned to operate in a similar way to human judgement




mainly dealt with single business process, single-objective andsingle-product problems. However, subsequent studies haveincreasingly focused on multiple business processes, multi-cri-teria, multi-products cases. The overwhelming majority of supplypartner decision models apply to the nal selection phase. Partnerselection models can be distinguished in according to whetherthey are for single or multiple deals/products, and whether or notthey involve inventory management (see Table 4 ).

As Table 4 illustrates, almost two thirds of models identied inthe literature can be characterized as ‘‘single deal/product’’. Thesemodels consider the selection of a partner for a single one product orgroup of items. ‘‘Multiple deals/products’’ models, on the other hand,take into account situations involving different products in productgroups. Furthermore, most of the existing literature does notconsider inventory management of the items purchased. Therelevant literature on each of these techniques is discussed as below.

5.1. Linear weighting models

In linear weighting models, different weights are given todifferent criteria, with the biggest weight indicating the highestimportance. Plenty of adaptations have been suggested for thesake of making linear weighting models better capable of dealingwith the uncertainty and imprecision which inevitably surroundspartner selection in real business practice. Ko et al. (2001)suggested an idea for selecting partners in a distributed dynamicmanufacturing environment, which enables companies to sharetheir machine capacities. They proposed a model to minimize thesum of the operation and transportation costs based on alter-native process plans considering several kinds of operationcharacteristics. Amid et al. (2006) established a fuzzy multi-objective linear model to solve the partner selection problem ina supply chain by applying an asymmetric fuzzy-decision makingtechnique. Jarimo and Salo (2009) applied a mixed-integer linearmodel to assist the selection of partners in a virtual organization.Their model extends the xed and variable costs to includeaccommodate transportation costs, capacity risk measures, andinter-organizational dependencies such as the success of pastcollaboration. Ng (2008) constructed a weighted linear programfor the multi-criteria supplier selection problem by using a

transformation technique that could solve the problem without

applying an optimizer. The benet of the model is that it does notrequire the user to learn any optimization technique.

5.2. Mathematical programming models

Geoffrion and Graves (1974) undertook early mathematicalwork in the area of supply chain design. They proposed a multi-commodity logistics network design model for optimizing pro-duct ows through the whole supply chain, which involved all thenodes from raw material vendors to producers to distributioncenters, and nally to customers. Subsequently, a number of

different mathematical programming models have been pro-posed. They can be classied into the following three sub-categories.

5.2.1. Goal programming Hajidimitriou and Georgiou (2002) employed a goal program-

ming (GP) technique for the supply partner selection problemthat was able to achieve multiple goals for different levels of performance of the corresponding attributes. However, thismethod did not consider the combination of potential partnersthat may results in better solutions for the whole supply chaincomparing with only one candidate being identied. Basnet andLeung (2005) solved the supplier selection problem in a multi-period inventory lot-sizing scenario. Their work gave an answerto the question about what products to order in what quantitieswith which suppliers in which periods. Comparing with theenumerative search algorithm they proposed, the heuristic thatbased on the traditional lot sizing based heuristic algorithm is fastenough for practical problems. Ravindran et al. (2010) developedtwo types of risk models, value-at-risk and miss-the-target, forthe partner selection problem that has been modeled as a multi-criteria optimization problem. The researchers solved the pro-blem in two separate steps, named qualication and orderquantities allocation step, by using the goal programmingapproach. Vanteddu et al. (2011) considered inventory costs andthe supply chain ‘‘cycle time’’ reduction costs and proposed aprogramming model for focus dependent supplier selection pro-blem. Yet, the model does not involve any qualitative factors such

as quality, supplier’s reputation, cultural match, etc.

Table 4The classication of partner selection models.

No inventory management over time Inventory management over time

Single deal/single product Multiple deals/multiple products

Linear weighting Mathematical programming Fuzzy set theory AHP/ANP Mathematical programming

De Boer et al.

(1998), Ko et al.(2001) , Amidet al. (2006) , Ng(2008) , Jarimoand Salo (2009)

Weber et al. (1998) , Hajidimitriou

and Georgiou (2002) , Talluri andBaker (2002) , Cakravastia andTakahashi (2004) , Sha and Che(2005) , Choi and Chang (2006), Caoand Wang (2007) , Chen et al.(2007), Stadtler (2007), Glickmanand White (2008), Guneri et al.(2009), Kheljani et al. (2009), Nepalet al. (2009), Xu and Nozick (2009),Hsu et al. (2010), Sawik (2010),Ravindran et al. (2010) , Zhang andZhang (2011) , Amin et al. (2011),Hadi-Vencheh (2011), Sawik(2011)

Ghodsypour. and O’Brien (2001),

Lin and Chen (2004) , Kumar et al.(2006) , Bevilacqua et al. (2006) ,Chen et al. (2006), Haq and Kannan(2006) , Humphreys et al. (2006),Sarkar and Mohapatra (2006) , Chouet al. (2007) , Bayrak et al. (2007) , Jain et al. (2007), Buyukozkan et al.(2008) , Chan et al. (2008), Chouand Cha ng (2008) , Amid et al.(2009), Amin and Razmi (2009),Boran et al. (2009), Guneri andKuzu (2009), Lee et al. (2009a),Shen and Yu (2009), Wu (2009) ,Feng et al. (2010), Keskin et al.(2010) , Osman and Demirli (2010),Sevkli (2010), Sanayei et al. (2010) ,Ye (2010), Dalalah et al. (2011),

Yucel and Guneri (2011)

Tam and Tummala (2001) ,

Mikhailov (2002) , Chan (2003) , Liuand Hai (2005) , Sha and Che(2006) , Sarkis et al. (2007) ,Demirtas and Ustun (2008) , Sariet al. (2008) , Wu et al. (2009) , Wuet al. (2009a) , Chamodrakas et al.(2010), Lin et al. (2010, 2011),Buyukozkan and Cici (2011)

Basnet and Leung (2005) , Hong

et al. (2005), Wadhwa andRavindran (2007) , Liao andRittscher (2007a) , Ustun andDemirtas (2008), Wu et al. (2009) ,Huang et al. (2010), Mendoza andVentura (2010), Keskin et al.(2010) , Kara (2011), Rezaei andDavoodi (2011) , Vanteddu et al.(2011)




5.2.2. Multi-objective programming Cakravastia and Takahashi (2004) proposed a multi-objective

non-linear model for the negotiation process by generating a set of effective alternatives in each negotiation period. As an initialattempt, they applied the interactive weighted Tchebycheff method and Benders decomposition method to generate the setof effective alternatives before the order volume allocating to eachselected supplier. Zhao et al. (2006) argue that the virtual enter-

prise is a basic organization form to achieve agile manufacturing inenterprise. As such, selecting the most appropriate supply partnersis a key success factor. Based on the concept of the inefcientcandidate, they constructed a multi-objective optimization modelwhile applying both fuzzy factors-based rules and the geneticalgorithm during the selection phases. Wadhwa and Ravindran(2007) considered price, lead-time and quality as three conictingcriteria that have to be minimized simultaneously in a multi-objective optimization model. They also present and comparedseveral multi-objective optimization methods, including weightedobjective, goal programming and compromise programming, forsolving the multi-objective optimization problem. Cao and Wang(2007) proposed a two-stage vendor selection framework in out-sourcing. The rst stage helps the client to nd the best matchbetween the vendor and the outsourced project. In the secondstage, employs the chosen vendor for the full implementation.Their work pointed out that the selection of vendors for the rststage testing is more about creating a good vendor portfolio thansimply picking the frontrunners. Recently, Wu et al. (2010)presented a fuzzy multi-objective programming model to decideon supplier selection taking risk factors into consideration. Theauthors modeled a supply chain consisting of three levels and usedsimulated historical quantitative and qualitative data to measurethe fuzzy events into the fuzzy multi-objective programmingmodels. Furthermore, Rezaei and Davoodi (2011) developed twomulti-objective mixed integer non-linear models for multi-periodlot sizing problems involving multiple products and multiplesuppliers. By comparing the outputs of these two models, theauthors pointed out that buyers are better able to optimize theirobjectives in a backordering situation.

5.2.3. Integer programming Talluri and Baker (2002) proposed a three-phase MP approach

for the partner selection by combining the pair-wise efciencygame model with integer and linear programming. Although thismodel overcomes the limitations of unrestricted weight exibil-ity, it risks producing a sub-optimal solution as the lter phasemight lter the optimal one. Sha and Che (2005) pointed out thatvirtual integration offers a way to make manufacturing systemsmore agile and competitive, and then the problem of partnerselection is the essential and the most important issue. Based onAHP, multi-attribute utility theory and integer programming, theydeveloped a partner selection and production-distribution plan-ning model. They also provided a Branch & Bound algorithm tosolve the model. In addition to the typical costs associated withvendor selection and delivery, Keskin et al. (2010) considered theinventory-related costs and decisions of the stores. The authorsemphasized the relationship between facility locations and pro-posed an integrated vendor selection and inventory optimizationmodel. Zhang and Zhang (2011) developed a mixed integerprogramming model to minimize the total cost, including selec-tion, purchase, holding and shortage costs. Yet, their modelneglects the supply risk and price discount based on the orderquantity. Sawik (2011) considered the risk-neutral and risk-averse objective functions separately and simultaneously in abi-objective optimization problem. Based on mixed integer pro-

gramming models, this approach provides the decision-maker

with a simple tool for evaluating the relationship betweenexpected and worst-case costs.

5.3. Analytic hierarchy/network process models

Tam and Tummala (2001) proposed and applied an AHP-basedmodel to a real case study in selecting a vendor for a telecommu-nications system. The use of the proposed model proves that it

can improve the group decision making and reduce the timetaken to select a vendor. Mikhailov (2002) applied AHP to copewith the fuzziness that occurs when a decision-maker comparesthe relative importance of different attributes. Contrary to theother interval prioritization methods, this method can derivecrisp priorities from inconsistent interval pairwise comparisonmatrices. However, this method ignores the effects resulted frominterdependent attributes. Chan (2003) proposed a Chain of Interaction method to solve the problems associated with thedynamic nature of supply chain management by using subjectivehuman judgment in determining the relative importance of thetangible selection factors. In this method, an Interactive SelectionModel is suggested to systemize the earlier steps rstly, followedby the implementation of the AHP and the multi-criteriondecision making software Expert Choice. As the nal outcome of the Interactive Selection Model greatly depends on the quality of the data collected, a systematic data collection method is requiredwhilst applying their interaction method and model.

Liu and Hai (2005) developed a voting AHP method, whichcombines DEA and AHP methodology. After determining theweights in a selected rank, their method selects partners bycomparing the weighted sum of the selection number of rank vote.Sevkli et al. (2007) apply a hybrid method of supplier selection,namely data envelopment analytic hierarchy process (DEAHP), to awell-known Turkish company operating in the appliance industry.In this method, the DEA approach is embedded into AHP metho-dology. The criteria the model used reect closer to the realoptimum of the decision made. Sari et al. (2008) proposed anAHP model to contribute in the selection of the partner companiesin the dynamic environment. Their AHP model was linked with ageneric multi-criteria analysis model, and provided a means of structuring the decision problem and estimating importanceweights for the objectives of the various stakeholder groups. Ingeneral, the methods proposed by using AHP only consider one-way hierarchical relationships between the factors. This is asimplistic assumption that does not consider the many possiblerelationships. Moreover, AHP does not explicitly consider theinteractions between the various factors/clusters.

To overcome the disadvantages of the previous AHP modelsproposed, Sarkis et al. (2007) built a strategic model for partnerselection by using analytical network process (ANP) methodology.The ANP, also introduced by Saaty, is a new generalization of theAHP (Saaty, 1996 ). Whereas AHP represents a framework with auni-directional hierarchical relationship, ANP allows for morecomplex interrelationships among decision levels and attributes.Therefore, a hierarchical structure with a linear top-to-bottomform is not applicable for a complex system. Sarkis et al. (2007) ’sANP model effectively overcomes the problem of rank reversalwhich is also a limitation of AHP. Yet, as the authors acknowl-edged, without incorporating secondary criteria, the nal solu-tions may not be clearly dened.

Considering both tangible and intangible factors, Demirtas andUstun (2008) integrated ANP and multi-objective mixed integerlinear programming approach to answer two questions: (1) whichsuppliers are the best, and (2) how much should be purchasedfrom each selected supplier if anyone supplier could not fulll thewhole demand? The special characteristic of the model is that it

could include the decision makers’ preferences. Wu et al. (2009)




proposed a two-stage approach, based on the application of ananalytic network process-mixed integer multi-objective program-ming (ANP-MIMOP) model, to solve the problem of partnerselection in ASCs. In their rst stage, an ANP methodology isapplied to calculate the priorities of different criteria for partnerselection. Secondly, using these priorities, a MIMOP method isused to determine the supply chain structure and optimize theallocation of order quantities. Buyukozkan and Cifci (2011)

developed a fuzzy ANP approach within multi-person decision-making schema under incomplete preference relations for sus-tainable suppliers’ selection. These ANP models can overcome theshortcomings of AHP approaches but cannot solve the detailedlot-sizing problem.

5.4. Fuzzy sets models

A number of authors suggest using fuzzy sets theory (FST) tomodel uncertainty and imprecision in partner selection situations.Sarkar and Mohapatra (2006) used a fuzzy set approach tomeasure the imprecision of these two dimensions to rank andreduce the number of potential partners, by focusing on theirperformance and capability. However, there is a compensationproblem with this method, in that a potential partner scoringhighly in one dimension may compensate for a low score in someother. Using fuzzy analytical hierarchy process and a geneticalgorithm, Haq and Kannan (2006) developed an integratedsupplier selection and multi-echelon distribution inventorymodel in a built-to-order supply chain environment. Kumaret al. (2006) combined the multi-objective integer programmingand fuzzy set theories for vendor selection. In their model, variousinput parameters have been treated as vague with a linearmembership function. The proposed model provides a tool thatfacilitates the vendor selection and their quota allocation underdifferent degrees of information vagueness. Bevilacqua et al.(2006) proposed a fuzzy quality function deployment (QFD)approach to support supply partner selection. This approach usesboth internal and external variables to rank the potential part-ners. The advantage of this method lays in its ability to transform-ing decision makers’ verbal assessments to linguistic variables,which are more accurate than other non-fuzzy methods. How-ever, it is used to rank potential partners, which is not the mainobjective in the early phase of partner selection.

Chou et al. (2007) utilized the supplier positioning matrix tolink the capability of potential suppliers with the requirements of the customers. Then, their research identied the strategy-alignedcriteria for vendor selection in a modied re-buy situation.Finally, based on the type of components required by thecustomers, a fuzzy factor rating system was used to evaluatethe potential vendors. Bayrak et al. (2007) also proposed a fuzzyapproach method for partner selection by assessing delivery,quality, exibility, and service criteria. However, it is a puresubjective method that will inevitably depend heavily on experts’experiences. Buyukozkan et al. (2008) proposed a fuzzy AHP andfuzzy Technique for Order Preference by Similarity to IdealSolution approach to rank partners under conditions of uncer-tainty and complexity. To avoid the single decision maker’s bias,it would be benecial to extend the model in a group decision-making environment. As different enterprises have differentmotivations for establishing supply partners, the identicationof universal criteria weights for use in any situation will not beappropriate. Based on fuzzy sets theory and VIKOR method,Sanayei et al. (2010) applied linguistic values to assess the ratingsand weights for the established criteria, and built a hierarchymultiple criteria decision making model to deal with the supplierselection problems in the supply chain system. The VIKOR

method in their model is developed to solve multiple criteria

decision making problems with conicting and non-commensur-able criteria. Yucel and Guneri (2011) developed a weightedadditive fuzzy programming approach for multi-criteria supplierselection. Their model has not very computational procedure, so itcan deal with the rating of factors effectively.

5.5. Genetic algorithms models

There are a number of studies that try to use genetic algo-rithms to solve the partner selection problem. Ip et al. (2003)pointed out that dynamic alliances are essential components of global manufacturing. Based on the concept of the inefcientcandidate, they built a risk-based partner selection model byusing genetic algorithm (GA) to minimize the risk in partnerselection. However, they failed to simultaneously consider bothqualitative and quantitative evaluation attributes. Sha and Che(2006) proposed an approach which is based on the GA, AHP andthe multi-attribute utility theory to satisfy simultaneously thepreferences of the suppliers and the customers at each level in thenetwork. This approach seems likely to outperform that of thesingle-phase genetic algorithm in supplier selection.

Liao and Rittscher (2007) constructed a multi-objective sup-

plier selection model under stochastic demand conditions. Theyextended the measurement of supplier exibility to considerdemand quantity and timing uncertainties comprehensively.Moreover, they proposed a problem specic genetic algorithmto handle the combinatorial optimization problem. Their solutionalternatives and objective trade-offs are valuable for the nalsupplier selection. Wang et al. (2009) emphasized that partnerselection is a key step in organizing a well-designed dynamicsupply network. They carefully analyzed various collaborationpatterns between distributed partners with the correspondingevaluation criteria for collaboration time and cost, and thenproposed a genetic algorithm solution for collaboration costoptimization-oriented partner selection. Yeh and Chuang (2011)also developed an optimum mathematical planning model for

green partner selection by adopting two multi-objective geneticalgorithms to nd the set of Pareto-optimal solutions. However,the main drawback of GA is that it requires users to have a level of specialized knowledge that is likely to be well beyond thatpossessed by most managers and organizational decision makers.Also a severe drawback is that some feasible solutions cannot begenerated by crossover operation.

5.6. Other models designed for dynamic decision-making situation

Besides the models and methods for ASCs mentioned above,there are other several models and methods which do not belongto any above categories. These models and methods consider thedynamic decision-making situation, like ASCs. They are reviewedas below.

Recognizing that virtual enterprises and agile supply chainsare becoming a growing trend, Lau and Wong (2001) make use of the technologies such as MRPII, CAD, CAPP, DNC Link, to addressthe problem of selection and management in dynamic networks.Their paper provides insights into the issues raised by managingdispersed production networks using electronic media. Valluriand Croson (2005) applied agent-based modeling approach toimprove the small numbers outsourcing model, which displays acomplicated reward and punishment prole under incompleteinformation and dynamic decision-making condition. Moreover,their research shows that it is better for a buyer to transact withrelatively few suppliers. Yet, in their model, the authors hadallowed only relative quality evaluation while assuming the

relative ranking to be 100% accurate.




Sucky (2007) proposed a dynamic decision making approach forstrategic vendor selection based on the principles of hierarchicalplanning. This approach considered the interdependencies in timearising from investment costs of selecting a new vendor and costsof switching from an existing vendor to a new one. Chen andHuang (2007) built a logic model to describe the relationshipsamong the manufacturing capabilities of virtual enterprises andthe manufacturing requirements of clients in the formation of

dynamic virtual enterprise. Based upon the logic model, threesearch algorithms were developed for three different optimal goals,respectively. Fulga (2007) dealt with the partner selection problemwhich considers the bid cost and the bid completion time of subprojects, the due date and the budget constraint, in the fastchanging business environment which potential partners dispersedgeographically and had different core competencies. They gave twoalgorithms to solve their model. Zarvic and Seifert (2008) describedan approach for the partner selection process, which is based ontask-resource dependencies, with related constraints and priorities.Their dependency concepts between resources and tasks stemmingfrom coordination theory have proved to be a helpful instrumentfor the purpose of partner selection in ASCs.

The partner selection problem was modeled as a nonlinearinteger programming problem by Cheng et al. (2009) . Also,they gave an Ant Colony Optimization (ACO) algorithm withembedded project scheduling to solve the problem with the leadtime, subproject cost and risk factor constraints to solve theirmodel in the dynamic environment. Comparing with the GA andenumeration algorithm, the effectiveness of the ACO algorithmwas shown. Darwish (2009) built a model that integrates thesingle-vendor single-buyer problem with the process mean selec-tion problem. The integrated model allows the vendor to deliverthe produced lot to buyer in a number of unequal-sized ship-ments and reduces the processing cost. Ye and Li (2009) con-structed two MADM (multi-attribute decision model) methods forgroup decision making with interval values to solve partnerselection problem under incomplete information in dynamicbusiness situation. Based on deviation degree, the rst methodis a technique for order preference by similarity to ideal solution(TOPSIS) for group decision making. The second method is aTOPSIS for group decision-making based on risk factor. These two

methods for group decision making can not only be applied tosolve the partner selection problem, but also be utilized in othersimilar elds, such as investment and subcontractor selection.

Stoica and Ghilic-Micu (2009) introduced a new paradigm of the dynamic organization named the cybernetic economic system.Their multi-dimensional algorithm for dynamic organization part-ner selection is much depended on the technical and economicevaluation criteria. Crispim and de Sousa (2009, 2010) found that

partner selection in ASCs, in general, is a very complex problemdue to the dynamic topology of the network, the large number of alternatives and the different types of criteria. They proposed anexploratory process to help the decision makers to obtain knowl-edge about the network in order to identify the criteria and thepotential partners that best suit the needs of each particularproject. The processes they proposed involves a multi-objectivetabu search meta-heuristic and a fuzzy TOPSIS algorithm.

Table 5 summarizes some representative approaches in recentliterature on supply partner selection.

6. Decision models for application feedback

Luo et al. (2009) , Wu and Barnes (2009) and Wu and Barnes(in press) added a further stage to the supply partner selectionprocess, namely that of application feedback, which to date has notbeen adopted by other researchers. They argue that such a stage isimportant and necessary in today’s highly competitive environ-ment ( Christopher and Towill, 2000 ). By applying principles of continuous improvement and organizational learning, this stage isdesigned to provide feedback so that the process of supplierselection process in ASCs can be continuously improved. Theirmodel seeks to capitalize on the increased number of applicationsof the supplier section process that are inherent in the moredynamic conditions that prevail in environments in which ASCsare likely to be best suited. Its aim is to support organizationaldecision-makers in their efforts to optimize the performance of thesupply chain by ensuring that the most appropriate suppliers areselected at all times. Their test within two simulation groupsshowed that participants found the model was likely to havesignicant benets when used in practice.

Table 5Representative approaches in supply partner selection literature.

Methods/modelscategories

Author(s) and researchpublication years

Method/model types Structure of criteria

Types of criteria Criteria aggregation Assignment of weights

Mathematicprogramming

Hajidimitriou andGeorgiou (2002)

Goal programming Three levels Quant itat ive Linear aggregat ion By users

Rezaei and Davoodi(2011)

Multi objectiveprogramming

Flat Quantitative Non-linear programming Generate by geneticalgorithm

Sha and Che (2005) Integer programming Hierarchical Quantitative Linear aggregation Buyer’s subjectivepreference

Analytical hierarchical/network process

Mikhailov (2002) Fuzzy AHP Hierarchical Quantitative andQualitative

Linear aggregation Fuzzy algorithm

Sarkis et al. (2007) Analytical networkprocess (ANP)

Network Quantitative andQualitative

Supermatrix Pairwise compari sons

Fuzzy set approach Lin and Chen (2004) Hierarchical fuzzy rules Hierarchical Quantitative andQualitative

Fuzzy relationshiphierarchy

Fuzzy favourability

Bayrak et al. (2007) Fuzzy set approach Flat Quant itat ive Fuzzy weighted meanoperators

By users and fuzzyalgorithm

Combined methods Wu and Barnes (2010) Dempster-Shafer andoptimization theories

Hierarchical Quantitative andQualitative

Dempster-Shafer evidencecombination theory

By decision-makers

Luo et al. (2009) RBF-ANN Hierarchical Quantitative andQualitative

RBF function By network training

Wu et al. (2009) ANP-MIMOP Network Quantitative andQualitative

Mixed-integer multi-objective programming

Pairwise comparisonsby decision-makers

Wu and Barnes (2009) PDCA model and statisticsanalysis

Hierarchical Quantitative andQualitative

Statistics analysis By users and statisticsmethods




7. Discussion

The distribution of the one hundred and forty papers identiedin this research by their publishing year is shown in Fig. 2. Thisclearly illustrates the growing academic interest in the issue of

supply partner selection over the last decade, especially in the lastve years. With eighteen papers already published in the rst fourmonths of 2011, the numbers show no sign of diminishing just yet.The distribution of these papers by journal is shown in Fig. 3.As can be seen, most papers have been published in journals withstrong quantitative traditions, as might be expected from theOperations Research and Management Science (OR/MS) eld.Experts Systems with Applications (46), International Journal of

Production Research (25) and International Journal of ProductionEconomics (21) are the most frequent outlets. (NB, the nine papersshown in Fig. 2 from the Journal of Purchasing and SupplyManagement (JPSM) were identied from a search of ScienceDirect.These are listed in Appendix 2. These are included for comparisonpurposed only, as they lie outside of the method used to nd theone hundred and forty papers that form the basis of this paper’sliterature review.) The distribution of papers by their authors’afliations, as shown in Fig. 4, clearly shows the global interest inthe issue of supply partner selection over the last decade,especially the greater China area and the USA. As to the researchinstitutions of the authors ( Fig. 5), we can see that there are three‘‘research centers’’ around the greater China area and the world.

52

4 5 68

19

13

3426

18

0

5

10

15

20

25

30

35

40

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Fig. 2. Numbers of papers on supply partner selection in international journals since2001. (NB: the numbers of paper in 2011 is not a full year, only up to May. 5th).

0

10

20

30

40

50 46

2521

9 9 9 8 85 5

2 2

Fig. 3. The sources of supply partner selection papers (date from January 1, 2001to May 5, 2011).

0 5 10 15 20 25 30

Taiwan DistrictMainland China &Hong Kong

USATurkey

IranCanada

EnglandIndia

South KoreaGermany

FranceIceland

Netherlands

GreeceIndonesia

Portugal

2826

2217

1311

107

65

44

3

222

Fig. 4. The origin of authors of supply partner selection papers (date from January 1, 2001 to May 5, 2011).

0

1

2

3

4

5

6

7

Fig. 5. The institution of authors of supply partner selection papers (rst sixteeninstitutions; date from January 1, 2001 to May 5, 2011).




The biggest one is located in Taiwan (17) including NATL TAIPEIUNIV TECHNOL, NATL TAIWAN UNIV SCI & TECHNOL, CHUNGHUA UNIV, NATL CHENG KUNG UNIV, and NATL CHIAO TUNGUNIV. The second research center is located in Hong Kong (10),which includes HONG KONG POLYTECH UNIV and CITY UNIVHONG KONG. The last one is located in the mainland China (6),including HARBIN INST TECHNOL and S CHINA UNIV TECHNOL.

It is possible to identify several main approaches used for nal

phase partner selection: linear weighting, mathematic program-ming, analytical hierarchical/network process and fuzzy setapproach. Although each has its own specic merits, each alsohas its own shortcomings. First of all, linear weighting is a verysimple method, but it depends heavily on human judgment. Assuch, different weights could be given to the various attributesaccording to the decision-makers’ subjective judgment, However,as Bevilacqua et al. (2006, p. 16) note, all linear weightingtechniques are fully compensatory. Secondly, given an appropri-ate decision setting, mathematic programming allows the deci-sion-makers to formulate the decision problem in terms of amathematical objective function. It may be argued that mathe-matic programming models are more objective than ratingmodels because they force the decision-maker to explicitly statethe objective function. At the other hand, mathematic program-ming models often only consider the more quantitative criteriaand this may cause a signicant problem in considering qualita-tive factors. Furthermore, they also require arbitrary aspirationlevels and cannot accommodate subjective attributes. Thirdly,AHP does not consider the interactions among the various factorsand also cannot effectively take into account risk and uncertainty

in estimating the partner’s performance, because it presumes thatthe relative importance of attributes to evaluate partner perfor-mance is known with a high degree of certainty ( Saaty, 1996 ).ANP can overcome the shortcomings of AHP but cannot solve thedetailed sourcing problem. Finally, fuzzy set theoretic analysisdoes allow simultaneous treatment of precise and imprecisevariables. However, fuzzy set theory is complex and it would bedifcult for the users to comprehend and understand the ratio-

nale for the output results.As Huang and Keskar (2007) have noted, there appears to besomething of a dichotomy between the quantitative and qualita-tive approaches to partner selection, which typically betrays theacademic backgrounds of the researchers. On the one hand,engineering scholars, who typically operate within the OR/MSparadigm, have mostly treated partner selection as an optimiza-tion problem. On the other hand, business school scholars oftenemphasize philosophical issues and focus on developing qualita-tive principles to guide management decision making. However,strategic thinking cannot provide practical solutions. Neither willa mathematically optimization solution have any meaning if itdoes not match the business strategy. Consequently, effective andefcient decision-making for partner selection seems to requirethat approaches based on qualitative strategic thinking be com-bined with those of quantitative optimization.

The foregoing extensive literature analysis is summarized inTable 6 , which categorizes the supply partner selection processliterature using the framework outlined in the Methodology of this article and depicted in Table 1 and Fig. 1. This enables thefollowing observations to be drawn.

Table 6An analysis of the supply partner selection literature using the four-phase framework.

Phase New task Modied re-buy Straight re-buy

1. Formulationof criteria

Lin and Chen (2004) , Kannan and Haq (2007), Xiaand Wu (2007) , Wu and Barnes (2010) , Kara(2011)

Tracey and Tan (2001), Lewis (2002) , Lee et al.(2003), Lin et al. (2006) , Huang and Keskar(2007) , Sen et al. (2008), Lee et al. (2009b), Changet al. (2011)

Lee et al. (2001), Kannan and Tan (2002) ,Dulmin and Mininno (2003) , Pearn et al.(2004), Chen and Chen (2006), van der Rheeet al. (2009) , Punniyamoorthy et al. (2011),Zolghadri et al. (2011)

2. Qualication Choy et al. (2004) , Luo et al . (2009) Humphreys et al . (2003) , Lin and Chen (2004) , Niet al. (2007), Yigin et al. (2007) , Ha and Krishnan(2008) , Lee and Ou-Yang (2009) , Montazer et al.(2009), Zhang et al. (2009), Zeydan et al. (2011)

Wu and Olson (2008), Faez et al. (2009) , Guoet al. (2009), Saen (2009), Wu and Blackhurst(2009) , Wu and Olson (2010) , Aksoy andOzturk (2011) , Zhao and Yu (2011)

3. Finalselection

Lau and Wong (2001) , Chen et al. (2007), Fulga(2007) , Sarkis et al. (2007) , Sucky (2007) , Zarvicand Seifert (2008) , Cheng et al. (2009) , Ye and Li(2009) , Stoica and Ghilic-Micu (2009) , Crispimand de Sousa (2009, 2010) , Wu et al. (2009) , Tsaiet al. (2010), Ye (2010), Zhang and Zhang (2011)

Ghodsypour and O’Brien (2001), Talluri andBaker (2002) , Tempelmeier (2002), Chan (2003) ,Basnet and Leung (2005) , Deng and Elmaghraby(2005), Liu and Hai (2005) , Tang et al. (2005),Bevilacqua et al. (2006) , Chen et al. (2006), Choiand Chang (2006), Haq and Kannan (2006) ,Humphreys et al. (2006), Sarkar and Mohapatra(2006) , Bayrak et al. (2007) , Chou et al. (2007) ,Ernst et al. (2007), Liao and Rittscher (2007a) ,Chan et al. (2008), Chou and Chang (2008),

Demirtas and Ustun (2008) , Glickman and White(2008), Ng (2008) , Sari et al. (2008) , Ustun andDemirtas (2008), Amid et al. (2009), Boran et al.(2009), Darwish (2009) , Guneri et al. (2009), Lee(2009), Lee et al. (2009a), Nepal et al. (2009),Onut et al. (2009), Shen and Yu (2009), Wang andYang (2009), Wu et al. (2009a) , Xu and Nozick(2009), Baum et al. (2010), Chamodrakas et al.(2010), Feng et al. (2010), Hsu et al. (2010),Huang et al. (2010), Keskin et al. (2010) , Kuoet al. (2010), Mendoza and Ventura (2010),Osman and Demirli (2010), Sevkli (2010), Sanayeiet al. (2010) , Amin et al. (2011), Vinodh et al.(2011), Yeh and Chuang (2011) , Rezaei andDavoodi (2011) , Vanteddu et al. (2011)

Ko et al. (2001) , Tam and Tummala (2001) ,Hajidimitriou and Georgiou (2002) , Mikhailov(2002) , Ip et al. (2003) , Cakravastia andTakahashi (2004) , Onesime et al. (2004), Dinget al. (2005), Hong et al. (2005), Sha and Che(2005, 2006) , Amid et al. (2006) , Kumar et al.(2006) , Cao and Wang (2007) , Chen et al.(2007), Jain et al. (2007), Stadtler (2007), Wangand Che (2007), Wadhwa and Ravindran(2007) , Buyukozkan et al.(2008) , Wang (2008),

Amin and Razmi (2009), Guneri and Kuzu(2009), Jarimo and Salo (2009) , Kheljani et al.(2009), Wu (2009) , Wu et al. (2009b) , Azadehand Alem (2010), Keskin et al. (2010) , Lin et al.(2010, 2011), Ravindran et al. (2010) , Sawik(2010), Buyukozkan and Cici (2011), Dalalahet al. (2011), Hadi-Vencheh (2011), Liu andZhang (2011), Yucel and Guneri (2011) , Sawik(2011)

4. Applicationfeedback

Wu and Barnes (2009, in press )




Firstly, to date, most attention has been given to the nalselection phase, the supply partner selection process. The phasesthat precede and follow this (i.e. criteria formulation, qualicationand application feedback) have received far less attention.Although nal selection is often the most visible phase in theprocess, its quality largely depends on the quality of the otherphases. It seems clear that these phases need more attention.Secondly, in Table 6 , the research works lled in new task decision-

making situation is still much less than the research works whichlled in the re-buy decision-making situation. This nding indi-cates that researchers have so far paid less attention to partnerselection in the agile supply chain environment. However, this isthe most complex and challenging of the three situations. A newtask can arise due to a new market requirement, which will createa need to construct a new ASC in order to meet a new customerdemand effectively and efciently. ASCs offer new opportunities tocompanies operating with a growing number of participants(consumers, vendors, partners and others) in a global businessenvironment ( Crispim and de Sousa, 2010 ). The success of ASCs isstrongly dependent on its composition, and the selection of partners therefore becomes a crucial issue. Further research isrequired to address this problem. Thirdly, the framework showsthat not all the methods and models used in partner selection areequally useful in every possible situation. Rather they seem to becontextually specic. Yet, the existing literature seems not toadequately address this issue. The framework indicates that moreconsideration needs to be given to the situational characteristics inorder to determine the most suitable method or model.

Finally yet importantly, the combination and integrated modelsand approaches are summarized within Table 7 and Fig. 6. FromTable 7 , we can see that the most famous combined approaches arethe models that include mathematical programming, AHP/ANP or

fuzzy set approach. On the contrary, the articial intelligenceapproach, including both ANN and CBR, has fewer examples tointegrate with other approaches, such as AHP or ANP. As articialintelligence approaches have the potential ability to overcome theinformation processing difculties, there are plenty opportunitiesfor further research in this area.

8. Conclusions

Construction of an effective and efcient partner selection modelis one of the most important issues before a partnership can be built.This research is based on a literature review of the decision-makingmodels and approaches for partner selection from 2001 to 2011.This paper reviews these papers based on the framework used byDe Boer et al. (2001) , Luo et al. (2009) and Wu and Barnes (in press) .Based on the extensive review, this research then presents severalobservations and recent trends on the development of the decision-making models and approaches for partner selection in ASCs. Thispaper also advances De Boer et al. (2001) ’s groundbreaking work byconsidering the various combinations of methods applied and thegeographic origin of supplier selection research. The foregoinganalysis provides some useful information and enabled us to ndsome gaps in the past decade’s literature.

Firstly, most of the existing research proposes decision-makingmodels for the nal selection phase but very few works consider thestages that precede or follow it. There has been no signicant changeto this situation over the last decade in comparison to the literaturepublished before 2001. Further work is still needed to bridge this gap,for as many researchers have continued to argue, the decision qualityof the previous stage determines the decision quality of the followingstages ( De Boer et al., 2001 ; Aissaoui et al., 2007 ; Luo et al., 2009 ;

Table 7Review of literatures based on integrated methods/models for supplier/partner selection problem.

ArticialIntelligence(ANN)

Mathematical Programming AHP/ANP Fuzzy Set Genetic Algorithms

DEA Wu (2009) Wu and Olson (2008) Liu and Hai(2005) , Sevkli(2007), Kuo et al.(2010), Zeydanet al. (2011)

Azadeh and Alem (2010), Zeydan et al.(2011)

Mathematicalprogramming

Tang et al. (2005), Kumar et al. (2006) ,Zhao et al. (2006) , Amid et al. (2009),Guneri et al. (2009), Lee (2009), Lee et al.(2009a), Hsu et al. (2010), Sanayei et al.(2010) , Amin et al. (2011), Kara (2011),Yucel and Guneri (2011)

Ip et al. (2003) , Ding et al. (2005),Liao and Rittscher (2007) , Liao andRittscher (2007a) , Wang et al.(2009) , Wu et al. (2010) , Yeh andChuang (2011)

AHP/ANP Onesime et al. (2004), Sha and Che(2005) , Demirtas and Ustun (2008) ,Ustun and Demirtas (2008), Wu et al.(2009) , Wu et al. (2009b) , Lin et al.(2011)

Mikhailov (2002) , Haq and Kannan(2006) , Chan et al. (2008), Buyukozkanet al. (2008) , Onut et al. (2009), Wangand Yang (2009), Chamodrakas et al.(2010), Buyukozkan and Cici (2011),Vinodh et al. (2011)

Sha and Che (2006)

Fuzzy set Wang and Che (2007), Wang(2008)

Clusteranalysis

Ha and Krishnan(2008)

Keskin et al. (2010) Che (2010)

Articialintelligence(CBR)

Choy et al.(2002,2004 ), Zhaoand Yu(2011)

Faez et al. (2009)

Linearweighting

Ko et al. (2001) , Jarimo and Salo(2009) , Ng (2008)

Amid et al. (2006)




Wu and Barnes, in press ). One of the possible ways to overcome thisgap would be for more multi disciplinary research, including descrip-tive empirical studies. Secondly, the success of any ASCs is stronglydependent on its construction, and the selection of partners thereforebecomes a crucial issue. However, very few researchers have paidattention to this special and important decision-making environment.Further research is required to address this problem as the businessenvironment becomes increasingly dynamic in nature ( Wu et al.,2009 ). Thirdly, given the recent developments in service operations,the vast majority of the publications found in this literature reviewseem to have been written in the context of selecting partners for thepurchase of raw materials and nished products in the manufactur-ing environment. More attention needs to be given to partnerselection in the service operations context. Fourthly, much like DeBoer et al. (2001) ’s nding, there is still very little research on partnerselection in public procurement. Fifthly, there is an important trendin the eld of purchasing and supply management which was lessprominent at the time of De Boer et al. (2001) ’s work, namelyelectronic reverse auctions (ERA). There seems to have been a dearthof research into the impact of such practices on partner selectionover the last ten years. Finally, the above summary of existingapproaches to partner selection highlights the need to adopt andmeet a combination of qualitative and quantitative objectives. There-fore, no single methodology is likely to be able to solve the partnerselection problem, especially when different organizations havedifferent qualitative requirements. Further research is needed towork towards developing a new more mature combination of methods and models.

Acknowledgment

This work was nancially supported by ‘the Fundamental

Research Funds for the Central Universities’ (no. 2010221027),

and ‘the Social Science Foundation of Fujian Province of China’2010 (no. 2010C015).

Appendix 1. Articles on supply partner selection published2001–2011 (identied from ISI Web of Knowledge)

Aissaoui, N., Haouari, M., Hassini, E., 2007. Supplier selectionand order lot sizing modeling: a review. Computers & OperationsResearch 34 (12), 3516–3540.

Aksoy, A., Ozturk, N., 2011. Supplier selection and performanceevaluation in just-in-time production environments. ExpertSystems with Applications 38 (5), 6351–6359.

Amid, A., Ghodsypour, S.H., O’Brien, C., 2006. Fuzzy multi-objective linear model for supplier selection in a supply chain.International Journal of Production Economics 104 (2), 394–407.

Amid, A., Ghodsypour, S.H., O’Brien, C., 2009. A weightedadditive fuzzy multiobjective model for the supplier selectionproblem under price breaks in a supply chain. International Journal of Production Economics 121 (2), 323–332.

Amin, S.H., Razmi, J., 2009. An integrated fuzzy model forsupplier management: a case study of ISP selection and evalua-tion. Expert Systems with Applications 36 (4), 8639–8648.

Amin, S.H., Razmi, J., Zhang, G.Q., 2011. Supplier selection andorder allocation based on fuzzy SWOT analysis and fuzzy linearprogramming. Expert Systems with Applications 38 (1), 334–342.

Awasthi, A., Chauhan, S.S., Goyal, S.K., Proth, J.M., 2009.Supplier selection problem for a single manufacturing unit understochastic demand. International Journal of Production Economics117 (1), 229–233.

Azadeh, A., Alem, S.M., 2010. A exible deterministic, stochas-tic and fuzzy Data Envelopment Analysis approach for supplychain risk and vendor selection problem: simulation analysis.

Expert Systems with Applications 37 (12), 7438–7448.

Partner selectionmodels and approaches

Singleapproaches

Integratedapproaches

DEA+ANNor AHP/ANP

ANN+CBR

LW + MPor Fuzzy Set

MP + GAor Fuzzy Set

AHP/ANP+ MP

Cluster Analysis+ AHP/ANP

IntergerProgramming

Multi-objectiveProgramming

GoalProgramming

NeuralNetwork

ExpertSustem

Case-Based-Reasoning

MathematicalProgramming

GeneticAlgorithms

FuzzySet

ANP/ AHP

LinearWeighting

Approaches forfinal choice

Approaches forqualification

Approaches forformulation of criteria

Fuzzy agilityindex

0-1Programming

Dempster-Shafer theory

DataEnvelopment

Analysis

ClusterAnalysis

Categoricalapproaches

ArtificialIntelligence

Fig. 6. The decision-making models and approaches for partner selection.




Bai, C., Sarkis, J., 2010. Integrating sustainability into supplierselection with grey system and rough set methodologies. Inter-national Journal of Production Economics 124 (1), 252–264.

Basnet, C., Leung, J.M.Y., 2005. Inventory lot-sizing withsupplier selection. Computers & Operations Research 32 (1), 1–14.

Baum, J.A.C., Cowan, R., Jonard, N., 2010. Network-independentpartner selection and the evolution of innovation networks.Management Science 56 (11), 2094–2110.

Bayrak, M.Y., Celebi, N., Taskin, H., 2007. A fuzzy approachmethod for supplier selection. Production Planning & Control18 (1), 54–63.

Boran, F.E., Genc, S., Kurt, M., Akay, D., 2009. A multi-criteriaintuitionistic fuzzy group decision making for supplier selectionwith TOPSIS method. Expert Systems with Applications 36 (8),11363–11368.

Buyukozkan, G., Feyzioglu, O., Nebol, E., 2008. Selection of thestrategic alliance partner in logistics value chain. International Journal of Production Economics 113 (1), 148–158.

Cakravastia, A., Takahashi, K., 2004. Integrated model for supplierselection and negotiation in a make-to-order environment. Interna-tional Journal of Production Research 42 (21), 4457–4474.

Cao, Q., Wang, Q., 2007. Optimizing vendor selection in a two-stage outsourcing process. Computers & Operations Research34 (12), 3757–3768.

Chamodrakas, I., Batis, D., Martakos, D., 2010. Supplier selec-tion in electronic marketplaces using satiscing and fuzzy AHP.Expert Systems with Applications 37 (1), 490–498.

Chan, F.T.S., 2003. Interactive selection model for supplierselection process: an analytical hierarchy process approach.International Journal of Production Research 41 (15), 3549–3579.

Chan, F.T.S., Kumar, N., Tiwari, M.K., Lau, H.C.W., Choy, K.L.,2008. Global supplier selection: a fuzzy-AHP approach. Interna-tional Journal of Production Research 46 (14), 3825–3857.

Chang, B., Chang, C.W., Wu, C.H., 2011. Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systemswith Applications 38 (3), 1850–1858.

Chang, B., Hung, H.F., 2010. A study of using RST to createthe supplier selection model and decision-making rules. ExpertSystems with Applications 37 (12), 8284–8295.

Che, Z.H., 2010. A genetic algorithm-based model for solvingmulti-period supplier selection problem with assembly sequence.International Journal of Production Research 48 (15), 4355–4377.

Chen, C.T., Lin, C.T., Huang, S.F., 2006. A fuzzy approach forsupplier evaluation and selection in supply chain management.International Journal of Production Economics 102 (2), 289–301.

Chen, K.S., Chen, K.L., 2006. Supplier selection by testing theprocess incapability index. International Journal of ProductionResearch 44 (3), 589–600.

Chen, Q.X., Chen, X., Lee, W.B., 2007. Qualitative search algo-rithms for partner selection and task allocation in the formulationof virtual enterprise. International Journal of Computer IntegratedManufacturing 20 (2–3), 115–126.

Choi, J.H., Chang, Y.S., 2006. A two-phased semantic optimiza-tion modeling approach on supplier selection in eProcurement.Expert Systems with Applications 31 (1), 137–144.

Chou, S.Y., Chang, Y.H., 2008. A decision support system forsupplier selection based on a strategy-aligned fuzzy SMARTapproach. Expert Systems with Applications 34 (4), 2241–2253.

Chou, S.Y., Shen, C.Y., Chang, Y.H., 2007. Vendor selection in amodied re-buy situation using a strategy-aligned fuzzyapproach. International Journal of Production Research 45 (14),3113–3133.

Choy, K.L., Lee, W.B., Lau, H.C.W., So, S.C.K., 2004. An enterprisecollaborative management system: a case study of supplierselection in new product development. International Journal of

Technology Management 28 (2), 206–226.

Crispim, J.A., de Sousa, J.P., 2009. Partner selection in virtualenterprises: a multi-criteria decision support approach. Interna-tional Journal of Production Research 47 (17), 4791–4812.

Crispim, J.A., de Sousa, J.P., 2010. Partner selection in virtualenterprises. International Journal of Production Research 48 (3),683–707.

Dalalah, D., Hayajneh, M., Batieha, F., 2011. A fuzzy multi-criteria decision making model for supplier selection. Expert

Systems with Applications 38 (7), 8384–8391.Darwish, M.A., 2009. Economic selection of process mean forsingle-vendor single-buyer supply chain. European Journal of Operational Research 199 (1), 162–169.

Demirtas, E.A., Ustun, O., 2008. An integrated multiobjectivedecision making process for supplier selection and order alloca-tion. Omega —International Journal of Management Science36 (1), 76–90.

Deng, S.J., Elmaghraby, W., 2005. Supplier selection via tourna-ments. Production and Operations Management 14 (2), 252–267.

Ding, H.W., Benyoucef, L., Xie, X.L., 2005. A simulation optimi-zation methodology for supplier selection problem. International Journal of Computer Integrated Manufacturing 18 (2–3), 210–224.

Ernst, R., Kamrad, B., Ord, K., 2007. Delivery performance invendor selection decisions. European Journal of OperationalResearch 176 (1), 534–541.

Faez, F., Ghodsypour, S.H., O’Brien, C., 2009. Vendor selectionand order allocation using an integrated fuzzy case-based reason-ing and mathematical programming model. International Journalof Production Economics 121 (2), 395–408.

Feng, B., Fan, Z.P., Ma, J., 2010. A method for partner selectionof codevelopment alliances using individual and collaborativeutilities. International Journal of Production Economics 124 (1),159–170.

Ghodsypour, S.H., O’Brien, C., 2001. The total cost of logistics insupplier selection, under conditions of multiple sourcing, multiplecriteria and capacity constraint. International Journal of Produc-tion Economics 73 (1), 15–27.

Glickman, T.S., White, S.C., 2008. Optimal vendor selection in amultiproduct supply chain with truckload discounts. Transporta-tion Research, Part E —Logistics and Transportation Review 44 (5),684–695.

Guneri, A.F., Kuzu, A., 2009. Supplier selection by using a fuzzyapproach in just-in-time: a case study. International Journal of Computer Integrated Manufacturing 22 (8), 774–783.

Guneri, A.F., Yucel, A., Ayyildiz, G., 2009. An integrated fuzzy-lpapproach for a supplier selection problem in supply chain man-agement. Expert Systems with Applications 36 (5), 9223–9228.

Guo, X.S., Yuan, Z.P., Tian, B.J., 2009. Supplier selection basedon hierarchical potential support vector machine. Expert Systemswith Applications 36 (3), 6978–6985.

Ha, S.H., Krishnan, R., 2008. A hybrid approach to supplierselection for the maintenance of a competitive supply chain.Expert Systems with Applications 34 (2), 1303–1311.

Hadi-Vencheh, A., 2011. A new nonlinear model for multiplecriteria supplier-selection problem. International Journal of Com-puter Integrated Manufacturing 24 (1), 32–39.

Hajidimitriou, Y.A., Georgiou, A.C., 2002. A goal programmingmodel for partner selection decisions in international joint ven-tures. European Journal of Operational Research 138 (3), 649–662.

Haq, A.N., Kannan, G., 2006. Design of an integrated supplierselection and multi-echelon distribution inventory model in abuilt-to-order supply chain environment. International Journal of Production Research 44 (10), 1963–1985.

Ho, W., Xu, X.W., Dey, P.K., 2010. Multi-criteria decisionmaking approaches for supplier evaluation and selection: aliterature review. European Journal of Operational Research

202 (1), 16–24.




Hong, G.H., Park, S.C., Jang, D.S., Rho, H.M., 2005. An effectivesupplier selection method for constructing a competitive supply-relationship. Expert Systems with Applications 28 (4), 629–639.

Hsu, B.M., Chiang, C.Y., Shu, M.H., 2010. Supplier selectionusing fuzzy quality data and their applications to touch screen.Expert Systems with Applications 37 (9), 6192–6200.

Huang, J.J., Chen, C.Y., Liu, H.H., Tzeng, G.H., 2010. A multi-objective programming model for partner selection —perspectives

of objective synergies and resource allocations. Expert Systemswith Applications 37 (5), 3530–3536.Huang, S.H., Keskar, H., 2007. Comprehensive and congurable

metrics for supplier selection. International Journal of ProductionEconomics 105 (2), 510–523.

Huang, X.G., Wong, Y.S., Wang, J.G., 2004. A two-stage manu-facturing partner selection framework for virtual enterprises.International Journal of Computer Integrated Manufacturing17 (4), 294–304.

Humphreys, P., McCloskey, A., McIvory, R., Maguire, L., Glackin,C., 2006. Employing dynamic fuzzy membership functions to assessenvironmental performance in the supplier selection process.International Journal of Production Research 44 (12), 2379–2419.

Ip, W.H., Huang, M., Yung, K.L., Wang, D.W., 2003. Geneticalgorithm solution for a risk-based partner selection problem ina virtual enterprise. Computers & Operations Research 30 (2),213–231.

Jain, V., Wadhwa, S., Deshmukh, S.G., 2007. Supplier selectionusing fuzzy association rules mining approach. International Journal of Production Research 45 (6), 1323–1353.

Kannan, G., Haq, A.N., 2007. Analysis of interactions of criteriaand sub-criteria for the selection of supplier in the built-in-ordersupply chain environment. International Journal of ProductionResearch 45 (17), 3831–3852.

Kara, S.S., 2011. Supplier selection with an integrated metho-dology in unknown environment. Expert Systems with Applica-tions 38 (3), 2133–2139.

Keskin, B.B., Uster, H., Cetinkaya, S., 2010. Integrationof strategic and tactical decisions for vendor selection undercapacity constraints. Computers & Operations Research 37 (12),2182–2191.

Keskin, G.A., Ilhan, S., Ozkan, C., 2010. The Fuzzy ART algo-rithm: a categorization method for supplier evaluation andselection. Expert Systems with Applications 37 (2), 1235–1240.

Kheljani, J.G., Ghodsypour, S.H., O’Brien, C., 2009. Optimizingwhole supply chain benet versus buyer’s benet through sup-plier selection. International Journal of Production Economics121 (2), 482–493.

Ko, C.S., Kim, T., Hwang, H., 2001. External partner selectionusing tabu search heuristics in distributed manufacturing. Inter-national Journal of Production Research 39 (17), 3959–3974.

Kumar, M., Vrat, P., Shankar, R., 2006. A fuzzy programmingapproach for vendor selection problem in a supply chain. Inter-national Journal of Production Economics 101 (2), 273–285.

Kuo, R.J., Lee, L.Y., Hu, T.L., 2010. Developing a supplierselection system through integrating fuzzy AHP and fuzzy DEA:a case study on an auto lighting system company in Taiwan.Production Planning & Control 21 (5), 468–484.

Lau, H.C.W., Wong, E.T.T., 2001. Partner selection and informa-tion infrastructure of a virtual enterprise network. International Journal of Computer Integrated Manufacturing 14 (2), 186–193.

Lee, A.H.I., 2009. A fuzzy supplier selection model with theconsideration of benets, opportunities, costs and risks. ExpertSystems with Applications 36 (2), 2879–2893.

Lee, A.H.I., Kang, H.Y., Chang, C.T., 2009. Fuzzy multiple goalprogramming applied to TFT-LCD supplier selection by down-stream manufacturers. Expert Systems with Applications 36 (3),

6318–6325.

Lee, A.H.I., Kang, H.Y., Hsu, C.F., Hung, H.C., 2009. A greensupplier selection model for high-tech industry. Expert Systemswith Applications 36 (4), 7917–7927.

Lee, C.C., Ou-Yang, C., 2008. Development and evaluation of theinteractive bidding strategies for a demander and its suppliers insupplier selection auction market. International Journal of Pro-duction Research 46 (17), 4827–4848.

Lee, C.C., Ou-Yang, C., 2009. A neural networks approach for

forecasting the supplier’s bid prices in supplier selection negotia-tion process. Expert Systems with Applications 36 (2), 2961–2970.Lee, E.K., Ha, S., Kim, S.K., 2001. Supplier selection and manage-

ment system considering relationships in supply chain management.IEEE Transactions on Engineering Management 48 (3), 307–318.

Lee, M.S., Lee, Y.H., Jeong, C.S., 2003. A high-quality-supplierselection model for supply chain management and ISO 9001system. Production Planning & Control 14 (3), 225–232.

Liao, Z.Y., Rittscher, J., 2007. Integration of supplier selection,procurement lot sizing and carrier selection under dynamicdemand conditions. International Journal of Production Economics107 (2), 502–510.

Liao, Z.Y., Rittscher, J., 2007. A multi-objective supplier selec-tion model under stochastic demand conditions. International Journal of Production Economics 105 (1), 150–159.

Lin, C.T., Chen, C.B., Ting, Y.C., 2011. An ERP model for supplierselection in electronics industry. Expert Systems with Applica-tions 38 (3), 1760–1765.

Lin, Y.T., Lin, C.L., Yu, H.C., Tzeng, G.H., 2010. A novel hybridMCDM approach for outsourcing vendor selection: a case studyfor a semiconductor company in Taiwan. Expert Systems withApplications 37 (7), 4796–4804.

Liu, P.D., Zhang, X., 2011. Research on the supplier selection of a supply chain based on entropy weight and improved ELECTRE-III method. International Journal of Production Research 49 (3),637–646.

Mendoza, A., Ventura, J.A., 2010. A serial inventory systemwith supplier selection and order quantity allocation. European Journal of Operational Research 207 (3), 1304–1315.

Montazer, G.A., Saremi, H.Q., Ramezani, M., 2009. Design a newmixed expert decision aiding system using fuzzy ELECTRE IIImethod for vendor selection. Expert Systems with Applications36 (8), 10837–10847.

Nepal, B., Lassan, G., Drow, B., Chelst, K., 2009. A set-coveringmodel for optimizing selection of portfolio of microcontrollers inan automotive supplier company. European Journal of Opera-tional Research 193 (1), 272–281.

Ng, W.L., 2008. An efcient and simple model for multiplecriteria supplier selection problem. European Journal of Opera-tional Research 186 (3), 1059–1067.

Ni, M., Xu, X., Deng, S., 2007. Extended QFD and data-mining-based methods for supplier selection in mass customization.International Journal of Computer Integrated Manufacturing20 (2–3), 280–291.

Onesime, O.C.T., Xu, X.F., Zhan, D.C., 2004. A decision supportsystem for supplier selection process. International Journal of Information Technology & Decision Making 3 (3), 453–470.

Onut, S., Kara, S.S., Isik, E., 2009. Long term supplier selectionusing a combined fuzzy MCDM approach: a case study for atelecommunication company. Expert Systems with Applications36 (2), 3887–3895.

Osman, H., Demirli, K., 2010. A bilinear goal programmingmodel and a modied Benders decomposition algorithm forsupply chain reconguration and supplier selection. International Journal of Production Economics 124 (1), 97–105.

Pearn, W.L., Hung, H.N., Cheng, Y.C., 2009. Supplier selectionfor one-sided processes with unequal sample sizes. European

Journal of Operational Research 195 (2), 381–393.




Pearn, W.L., Wu, C.W., Lin, H.C., 2004. Procedure for supplierselection based on C-pm applied to super twisted nematic liquidcrystal display processes. International Journal of ProductionResearch 42 (13), 2719–2734.

Punniyamoorthy, M., Mathiyalagan, P., Parthiban, P., 2011. Astrategic model using structural equation modeling and fuzzylogic in supplier selection. Expert Systems with Applications38 (1), 458–474.

Qu, Z.H., Brocklehurst, M., 2003. What will it take for China tobecome a competitive force in offshore outsourcing? An analysisof the role of transaction costs in supplier selection. Journal of Information Technology 18 (1), 53–67.

Ravindran, A.R., Bilsel, R.U., Wadhwa, V., Yang, T., 2010. Riskadjusted multicriteria supplier selection models with applications.International Journal of Production Research 48 (2), 405–424.

Rezaei, J., Davoodi, M., 2011. Multi-objective models for lot-sizing with supplier selection. International Journal of ProductionEconomics 130 (1), 77–86.

Saen, R.F., 2009. Supplier selection by the pair of nondiscre-tionary factors-imprecise data envelopment analysis models. Journal of the Operational Research Society 60 (11), 1575–1582.

Sanayei, A., Mousavi, S.F., Yazdankhah, A., 2010. Group decisionmaking process for supplier selection with VIKOR under fuzzyenvironment. Expert Systems with Applications 37 (1), 24–30.

Sawik, T., 2010. Single vs. multiple objective supplier selectionin a make to order environment. Omega —International Journal of Management Science 38 (3–4), 203–212.

Sen, S., Basligil, H., Sen, C.G., Baracli, H., 2008. A framework fordening both qualitative and quantitative supplier selectioncriteria considering the buyer-supplier integration strategies.International Journal of Production Research 46 (7), 1825–1845.

Sevkli, M., 2010. An application of the fuzzy ELECTRE methodfor supplier selection. International Journal of ProductionResearch 48 (12), 3393–3405.

Sevkli, M., Koh, S.C.L., Zaim, S., Demirbag, M., Tatoglu, E., 2007.An application of data envelopment analytic hierarchy process forsupplier selection: a case study of BEKO in Turkey. International Journal of Production Research 45 (9), 1973–2003.

Sha, D.Y., Che, Z.H., 2006. Supply chain network design:partner selection and production/distribution planning using asystematic model. Journal of the Operational Research Society57 (1), 52–62.

Shen, C.Y., Yu, K.T., 2009. Enhancing the efcacy of supplierselection decision-making on the initial stage of new productdevelopment: a hybrid fuzzy approach considering the strategicand operational factors simultaneously. Expert Systems withApplications 36 (8), 11271–11281.

Stadtler, H., 2007. A general quantity discount and supplierselection mixed integer programming model. OR Spectrum 29 (4),723–744.

Sucky, E., 2007. A model for dynamic strategic vendor selec-tion. Computers & Operations Research 34 (12), 3638–3651.

Tam, M.C.Y., Tummala, V.M.R., 2001. An application of theAHP in vendor selection of a telecommunications system.Omega —International Journal of Management Science 29 (2),171–182.

Tang, J.F., Zhang, Y.E., Tu, Y.L., Chen, Y.Z., Dong, Y., 2005.Synthesis, evaluation, and selection of parts design scheme insupplier involved product development. Concurrent Engineering-Research and Applications 13 (4), 277–289.

Tempelmeier, H., 2002. A simple heuristic for dynamic ordersizing and supplier selection with time-varying data. Productionand Operations Management 11 (4), 499–515.

Tsai, Y.L., Yang, Y.J., Lin, C.H., 2010. A dynamic decisionapproach for supplier selection using ant colony system. Expert

Systems with Applications 37 (12), 8313–8321.

Ustun, O., Demirtas, E.A., 2008. An integrated multi-objectivedecision-making process for multi-period lot-sizing with supplierselection. Omega —International Journal of Management Science36 (4), 509–521.

Valluri, A., Croson, D.C., 2005. Agent learning in supplierselection models. Decision Support Systems 39 (2), 219–240.

van der Rhee, B., Verma, R., Plaschka, G., 2009. Understandingtrade-offs in the supplier selection process: the role of exibility,

delivery, and value-added services/support. International Journalof Production Economics 120 (1), 30–41.Vanteddu, G., Chinnam, R.B., Gushikin, O., 2011. Supply chain

focus dependent supplier selection problem. International Journalof Production Economics 129 (1), 204–216.

Vinodh, S., Ramiya, R.A., Gautham, S.G., 2011. Application of fuzzyanalytic network process for supplier selection in a manufacturingorganisation. Expert Systems with Applications 38 (1), 272–280.

Wadhwa, V., Ravindran, A.R., 2007. Vendor selection in out-sourcing. Computers & Operations Research 34 (12), 3725–3737.

Wang, H.S., 2008. Conguration change assessment: geneticoptimization approach with fuzzy multiple criteria for partsupplier selection decisions. Expert Systems with Applications34 (2), 1541–1555.

Wang, H.S., Che, Z.H., 2007. An integrated model for supplierselection decisions in conguration changes. Expert Systems withApplications 32 (4), 1132–1140.

Wang, T.Y., Yang, Y.H., 2009. A fuzzy model for supplierselection in quantity discount environments. Expert Systems withApplications 36 (10), 12179–12187.

Wang, Y.M., Chin, K.S., Leung, J.P.F., 2009. A note on theapplication of the data envelopment analytic hierarchy processfor supplier selection. International Journal of ProductionResearch 47 (11), 3121–3138.

Wang, Z.J., Xu, X.F., Zhan, D.C., 2009. Genetic algorithm forcollaboration cost optimization-oriented partner selection invirtual enterprises. International Journal of Production Research47 (4), 859–881.

Wu, C., Barnes, D., 2010. Formulating partner selection criteriafor agile supply chains: a Dempster-Shafer belief acceptabilityoptimisation approach. International Journal of ProductionEconomics 125 (2), 284–293.

Wu, C., Barnes, D., Rosenberg, D., Luo, X.X., 2009. An analyticnetwork process-mixed integer multi-objective programmingmodel for partner selection in agile supply chains. ProductionPlanning & Control 20 (3), 254–275.

Wu, C.W., Shu, M.H., Pearn, W.L., Liu, K.H., 2008. Bootstrapapproach for supplier selection based on production yield. Inter-national Journal of Production Research 46 (18), 5211–5230.

Wu, D.D., Olson, D., 2010. Enterprise risk management: a DEAVaR approach in vendor selection. International Journal of Production Research 48 (16), 4919–4932.

Wu, D.D., Zhang, Y.D., Wu, D.X., Olson, D.L., 2010. Fuzzy multi-objective programming for supplier selection and risk modeling: apossibility approach. European Journal of Operational Research200 (3), 774–787.

Wu, D.S., 2009. Supplier selection in a fuzzy group setting: amethod using grey related analysis and Dempster-Shafer theory.Expert Systems with Applications 36 (5), 8892–8899.

Wu, D.S., 2009. Supplier selection: a hybrid model using DEA,decision tree and neural network. Expert Systems with Applica-tions 36 (5), 9105–9112.

Wu, D.S., Olson, D.L., 2008. Supply chain risk, simulation, andvendor selection. International Journal of Production Economics114 (2), 646–655.

Wu, T., Blackhurst, J., 2009. Supplier evaluation and selection:an augmented DEA approach. International Journal of Production

Research 47 (16), 4593–4608.




Wu, W.Y., Shih, H.A., Chan, H.C., 2009. The analytic networkprocess for partner selection criteria in strategic alliances. ExpertSystems with Applications 36 (3), 4646–4653.

Wu, W.Y., Sukoco, B.M., Li, C.Y., Chen, S.H., 2009. An integratedmulti-objective decision-making process for supplier selectionwith bundling problem. Expert Systems with Applications 36(2), 2327–2337.

Xia, W.J., Wu, Z.M., 2007. Supplier selection with multiple

criteria in volume discount environments. Omega —International Journal of Management Science 35 (5), 494–504.Xu, N.X., Nozick, L., 2009. Modeling supplier selection and the

use of option contracts for global supply chain design. Computers& Operations Research 36 (10), 2786–2800.

Ye, F., 2010. An extended TOPSIS method with interval-valuedintuitionistic fuzzy numbers for virtual enterprise partner selec-tion. Expert Systems with Applications 37 (10), 7050–7055.

Ye, F., Li, Y.N., 2009. Group multi-attribute decision model topartner selection in the formation of virtual enterprise underincomplete information. Expert Systems with Applications 36 (5),9350–9357.

Yeh, W.C., Chuang, M.C., 2011. Using multi-objective geneticalgorithm for partner selection in green supply chain problems.Expert Systems with Applications 38 (4), 4244–4253.

Yigin, I.H., Taskin, H., Cedimoglu, I.H., Topal, B., 2007. Supplierselection: an expert system approach. Production Planning &Control 18 (1), 16–24.

Yucel, A., Guneri, A.F., 2011. A weighted additive fuzzy pro-gramming approach for multi-criteria supplier selection. ExpertSystems with Applications 38 (5), 6281–6286.

Zeydan, M., Colpan, C., Cobanoglu, C., 2011. A combinedmethodology for supplier selection and performance evaluation.Expert Systems with Applications 38 (3), 2741–2751.

Zhang, D.F., Zhang, J.L., Lai, K.K., Lu, Y.B., 2009. An novelapproach to supplier selection based on vague sets group deci-sion. Expert Systems with Applications 36 (5), 9557–9563.

Zhang, J.L., Zhang, M.Y., 2011. Supplier selection and purchaseproblem with xed cost and constrained order quantities understochastic demand. International Journal of Production Economics129 (1), 1–7.

Zhao, K., Yu, X., 2011. A case based reasoning approach onsupplier selection in petroleum enterprises. Expert Systems withApplications 38 (6), 6839–6847.

Zolghadri, M., Amrani, A., Zouggar, S., Girard, P., 2011. Powerassessment as a high-level partner selection criterion for newproduct development projects. International Journal of ComputerIntegrated Manufacturing 24 (4), 312–327.

Appendix 2. Articles on supply partner selection published inthe Journal of Purchasing and Supply Management 2001–2011(identied from ScienceDirect)

Bevilacqua, M., Ciarapica, F.E., et al., 2006. A fuzzy-QFDapproach to supplier selection. Journal of Purchasing and SupplyManagement 12(1), 14–27.

Dabhilkar, M., Bengtsson, L., et al. 2009. Supplier selection orcollaboration? Determining factors of performance improvementwhen outsourcing manufacturing. Journal of Purchasing andSupply Management 15(3), 143–153.

de Boer, L., Labro, E., et al., 2001. A review of methodssupporting supplier selection. European Journal of Purchasing &Supply Management 7(2), 75–89.

de Boer, L., van der Wegen, L.L.M., 2003. Practice and promiseof formal supplier selection: a study of four empirical cases.

Journal of Purchasing and Supply Management 9(3), 109–118.

Dulmin, R., Mininno, V., 2003. Supplier selection using a multi-criteria decision aid method. Journal of Purchasing and SupplyManagement 9(4), 177–187.

Humphreys, P., Huang, G., et al., 2007. Integrating designmetrics within the early supplier selection process. Journal of Purchasing and Supply Management 13(1), 42–52.

Kamann, D.-J.F., Bakker, E.F., 2004. Changing supplier selectionand relationship practices: a contagion process. Journal of Pur-

chasing and Supply Management 10(2), 55–64.Luo, X., Wu, C. et al., 2009. Supplier selection in agile supplychains: an information-processing model and an illustration. Journal of Purchasing and Supply Management 15(4), 249–262.

Micheli, G.J.L., Cagno, E., et al., 2009. Reducing the total cost of supply through risk-efciency-based supplier selection in the EPCindustry. Journal of Purchasing and Supply Management 15(3),166–177.

References

Aissaoui, N., Haouari, M., Hassini, E., 2007. Supplier selection and order lot sizingmodeling: a review. Computers & Operations Research 34 (12), 3516–3540.

Aksoy, A., Ozturk, N., 2011. Supplier selection and performance evaluation in just-

in-time production environments. Expert Systems with Applications 38 (5),6351–6359.Amid, A., Ghodsypour, S.H., O’brien, C., 2006. Fuzzy multiobjective linear model for

supplier selection in a supply chain. International Journal of ProductionEconomics 104 (2), 394–407.

Basnet, C., Leung, J.M.Y., 2005. Inventory lot-sizing with supplier selection.Computers & Operations Research 32 (1), 1–14.

Bayrak, M.Y., Celebi, N., Taskin, H., 2007. A fuzzy approach method for supplierselection. Production Planning & Control 18 (1), 54–63.

Bevilacqua, M., Ciarapica, F.E., Giacchetta, G., 2006. A fuzzy-QFD approach tosupplier selection. Journal of Purchasing and Supply Management 12 (1),14–27.

Buyukozkan, G., Cifci, G., 2011. A novel fuzzy multi-criteria decision framework forsustainable supplier selection with incomplete information. Computers inIndustry 62 (2), 164–174.

Buyukozkan, G., Feyzioglu, O., Nebol, E., 2008. Selection of the strategic alliancepartner in logistics value chain. International Journal of Production Economics113 (1), 148–158.

Cakravastia, A., Takahashi, K., 2004. Integrated model for supplier selection andnegotiation in a make-to-order environment. International Journal of Produc-tion Research 42 (21), 4457–4474.

Cao, Q., Wang, Q., 2007. Optimizing vendor selection in a two-stage outsourcingprocess. Computers & Operations Research 34 (12), 3757–3768.

Chan, F.T.S., 2003. Interactive selection model for supplier selection process: ananalytical hierarchy process approach. International Journal of ProductionResearch 41 (15), 3549–3579.

Che, Z.H., 2010. A two-phase hybrid approach to supplier selection through clusteranalysis with multiple dimensions. International Journal of InnovativeComputing Information and Control 6 (9), 4093–4111.

Chen, Y.M., Huang, P.N., 2007. Bi-negotiation integrated AHP in suppliers selection.International Journal of Operations & Production Management 27 (11),1254–1274.

Cheng, F.Q., Ye, F.F., Yang, J.G., 2009. An Ant Colony Optimization algorithm forpartner selection in Virtual Enterprises. International Journal of Materials &Product Technology 34 (3), 227–240.

Chou, S.Y., Shen, C.Y., Chang, Y.H., 2007. Vendor selection in a modied re-buysituation using a strategy-aligned fuzzy approach. International Journal of

Production Research 45 (14), 3113–3133.Choy, K.L., Lee, W.B., Lau, H., Lu, D., Lo, V., 2004. Design of an intelligent supplierrelationship management system for new product development. International Journal of Computer Integrated Manufacturing 17 (8), 692–715.

Choy, K.L., Lee, W.B., Lo, V., 2002. An intelligent supplier management tool forbenchmarking suppliers in outsource manufacturing. Expert Systems withApplications 22 (3), 213–224.

Christopher, M., Towill, D.R., 2000. Supply chain migration from lean andfunctional to agile and customised. Supply Chain Management —An Interna-tional Journal 5 (4), 206–213.

Crispim, J.A., de Sousa, J.P., 2009. Partner selection in virtual enterprises: a multi-criteria decision support approach. International Journal of ProductionResearch 47 (17), 4791–4812.

Crispim, J.A., de Sousa, J.P., 2010. Partner selection in virtual enterprises. Interna-tional Journal of Production Research 48 (3), 683–707.

Darwish, M.A., 2009. Economic selection of process mean for single-vendor single-buyer supply chain. European Journal of Operational Research 199 (1),162–169.

De Boer, L., Labro, E., Morlacchi, P., 2001. A review of methods supporting supplier

selection. European Journal of Purchasing and Supply Management 7, 75–89.




Demirtas, E.A., Ustun, O., 2008. An integrated multiobjective decision makingprocess for supplier selection and order allocation. Omega 36 (1), 76–90.

Dickson, G.W., 1966. An analysis of vendor selection systems and decisions. Journal of Purchasing 2 (1), 5–17.

Dulmin, R., Mininno, V., 2003. Supplier selection using a multi-criteria decision aidmethod. Journal of Purchasing and Supply Management 9 (4), 177–187.

Faez, F., Ghodsypour, S.H., O’Brien, C., 2009. Vendor selection and order allocationusing an integrated fuzzy case-based reasoning and mathematical program-ming model. International Journal of Production Economics 121 (2), 395–408.

Fulga, C., 2007. The partner selection problem in a virtual enterprise. EconomicComputation and Economic Cybernetics Studies and Research 41 (3–4),193–201.

Geoffrion, A., Graves, G.W., 1974. Multicommodity distribution system design bybenders. Management Science 20, 828–844.

Ha, S.H., Krishnan, R., 2008. A hybrid approach to supplier selection for themaintenance of a competitive supply chain. Expert Systems with Applications34 (2), 1303–1311.

Hajidimitriou, Y.A., Georgiou, A.C., 2002. A goal programming model for partnerselection decisions in international joint ventures. European Journal of Operational Research 138 (3), 649–662.

Haq, A.N., Kannan, G., 2006. Design of an integrated supplier selection and multi-echelon distribution inventory model in a built-to-order supply chain envir-onment. International Journal of Production Research 44 (10), 1963–1985.

Hinkle, C.L., Robinson, P.J., Green, P.E., 1969. Vendor evaluation using clusteranalysis. Journal of Purchasing 5 (3), 49–58.

Ho, W., Xu, X.W., Dey, P.K., 2010. Multi-criteria decision making approaches forsupplier evaluation and selection: a literature review. European Journal of Operational Research 202 (1), 16–24.

Huang, S.H., Keskar, H., 2007. Comprehensive and congurable metrics for

supplier selection. International Journal of Production Economics 105 (2),510–523.Huang, X.G., Wong, Y.S., Wang, J.G., 2004. A two-stage manufacturing partner

selection framework for virtual enterprises. International Journal of ComputerIntegrated Manufacturing 17 (4), 294–304.

Humphreys, P.K., Wong, Y.K., Chan, F.T.S., 2003. Integrating environmental criteriainto the supplier selection process. Journal of Materials Processing Technology138 (1–3), 349–356.

Ip, W.H., Huang, M., Yung, K.L., Wang, D., 2003. Genetic algorithm solution for arisk-based partner selection problem in a virtual enterprise. Computers andOperations Research 30 (2), 213–231.

Jarimo, T., Salo, A., 2009. Multicriteria partner selection in virtual organizationswith transportation costs and other network interdependencies. IEEE Transac-tions on Systems Man and Cybernetics, Part C —Applications and Reviews39 (1), 124–129.

Kannan, V.R., Tan, K.C., 2002. Supplier selection and assessment: their impact onbusiness performance. Journal of Supply Chain Management: A Global Reviewof Purchasing & Supply 38 (4), 11–21.

Keskin, B.B., Uster, H., Cetinkaya, S., 2010. Integration of strategic and tacticaldecisions for vendor selection under capacity constraints. Computers &Operations Research 37 (12), 2182–2191.

Ko, C.S., Kim, T., Hwang, H., 2001. External partner selection using tabu searchheuristics in distributed manufacturing. International Journal of ProductionResearch 39 (17), 3959–3974.

Kumar, M., Vrat, P., Shankar, R., 2006. A fuzzy programming approach for vendorselection problem in a supply chain. International Journal of ProductionEconomics 101 (2), 273–285.

Kwasnik, B.H., 1999. The role of classication in knowledge representation anddiscovery. Library Trends 48 (1), 22–49.

Lau, H.C.W., Wong, E.T.T., 2001. Partner selection and information infrastructure of a virtual enterprise network. International Journal of Computer IntegratedManufacturing 14 (2), 186–193.

Lee, C.C., Ou-Yang, C., 2009. A neural networks approach for forecasting thesupplier’s bid prices in supplier selection negotiation process. Expert Systemswith Applications 36 (2), 2961–2970.

Lewis, J.D., 2002. Partnerships for Prot: Structuring and Managing StrategicAlliances. Free Press, New York.

Liao, Z.Y., Rittscher, J., 2007. A multi-objective supplier selection model understochastic demand conditions. International Journal of Production Economics105 (1), 150–159.

Lin, C.R., Chen, H.S., 2004. A fuzzy strategic alliance selection framework for supplychain partnering under limited evaluation resources. Computers in Industry55 (2), 159–179.

Lin, C.T., Chiu, H., Chu, P.Y., 2006. Agility index in the supply chain. International Journal of Production Economics 100 (2), 285–299.

Liu, F.H.F., Hai, H.L., 2005. The voting analytic hierarchy process method forselecting supplier. International Journal of Production Economics 97 (3),308–317.

Lorange, P., Roos, J., Bronn, P.S., 1992. Building successful strategic alliances. LongRange Planning 25 (6), 10–18.

Luo, X., Wu, C., Rosenberg, D., Barnes, D., 2009. Supplier selection in agile supplychains: an information processing model and an illustration. Journal of Purchasing and Supply Management 15 (4), 249–262.

Mikhailov, L., 2002. Fuzzy analytical approach to partnership selection in forma-tion of virtual enterprises. Omega 30 (5), 393–401.

Miles, R., Snow, C., 1984. Fit, failure, and Hall of Fame. California Management

Review 26 (3), 10–28.

Ng, W.L., 2008. An efcient and simple model for multiple criteria supplier selectionproblem. European Journal of Operational Research 186 (3), 1059–1067.

Ravindran, A.R., Bilsel, R.U., Wadhwa, V., Yang, T., 2010. Risk adjusted multicriteriasupplier selection models with applications. International Journal of Produc-tion Research 48 (2), 405–424.

Rezaei, J., Davoodi, M., 2011. Multi-objective models for lot-sizing with supplierselection. International Journal of Production Economics 130 (1), 77–86.

Robinson, P.J., Faris, C.W., Wind, Y., 1967. Industrial Buying and Creative Market-ing. Allyn & Bacon, Boston.

Saaty, T.L., 1996. Decision Making with Dependence and Feedback: The AnalyticNetwork Process. RWS Publications, Pittsburgh.

Saen, R.F., 2010. Developing a new data envelopment analysis methodology forsupplier selection in the presence of both undesirable outputs and imprecisedata. International Journal of Advanced Manufacturing Technology 51 (9–12),1243–1250.

Sanayei, A., Mousavi, S.F., Yazdankhah, A., 2010. Group decision making process forsupplier selection with VIKOR under fuzzy environment. Expert Systems withApplications 37 (1), 24–30.

Sari, B., Sen, T., Kilic, S.E., 2008. AHP model for the selection of partner companiesin virtual enterprises. International Journal of Advanced ManufacturingTechnology 38 (3–4), 367–376.

Sarkar, A., Mohapatra, P.K.J., 2006. Evaluation of supplier capability and perfor-mance: a method for supply base reduction. Journal of Purchasing and SupplyManagement 12 (3), 148–163.

Sarkis, J., Talluri, S., Gunasekaran, A., 2007. A strategic model for agile virtualenterprise partner selection. International Journal of Operations & ProductionManagement 27 (11), 1213–1234.

Sawik, T., 2011. Supplier selection in make-to-order environment with risks.Mathematical and Computer Modelling 53 (9–10), 1670–1679.

Sevkli, M., Koh, S.C.L., Zaim, S., Demirbag, M., Tatoglu, E., 2007. An application of data envelopment analytic hierarchy process for supplier selection: a casestudy of BEKO in Turkey. International Journal of Production Research 45 (9),1973–2003.

Sha, D.Y., Che, Z.H., 2005. Virtual integration with a multi-criteria partner selectionmodel for the multi-echelon manufacturing system. International Journal of Advanced Manufacturing Technology 25 (7–8), 793–802.

Sha, D.Y., Che, Z.H., 2006. Supply chain network design: partner selection andproduction/distribution planning using a systematic model. Journal of theOperational Research Society 57 (1), 52–62.

Stoica, M., Ghilic-Micu, B., 2009. Virtual organization —cybernetic economicsystem. Modeling partner selection process. Economic Computation andEconomic Cybernetics Studies and Research 43 (2), 73–83.

Sucky, E., 2007. A model for dynamic strategic vendor selection. Computers &Operations Research 34 (12), 3638–3651.

Talluri, S., Baker, R.C., 2002. A multi-phase mathematical programming approachfor effective supply chain design. European Journal of Operational Research141 (2), 546–560.

Tam, M.C.Y., Tummala, V.M.R., 2001. An application of the AHP in vendor selectionof a telecommunications system. Omega —International Journal of Manage-ment Science 29 (2), 171–182.

Tracey, M., Tan, C.L., 2001. Empirical analysis of supplier selection and involvement,customer satisfaction, and rm performance. Supply Chain Management —An International Journal 6 (4), 174–188.

Valluri, A., Croson, D.C., 2005. Agent learning in supplier selection models.Decision Support Systems 39 (2), 219–240.

van der Rhee, B., Verma, R., Plaschka, G., 2009. Understanding trade-offs in thesupplier selection process: the role of exibility, delivery, and value-addedservices/support. International Journal of Production Economics 120 (1),30–41.

Vanteddu, G., Chinnam, R.B., Gushikin, O., 2011. Supply chain focus dependentsupplier selection problem. International Journal of Production Economics129 (1), 204–216.

Wadhwa, V., Ravindran, A.R., 2007. Vendor selection in outsourcing. Computers &Operations Research 34 (12), 3725–3737.

Wang, Z.J., Xu, X.F., Zhan, D.C., 2009. Genetic algorithm for collaboration costoptimization-oriented partner selection in virtual enterprises. International Journal of Production Research 47 (4), 859–881.

Weber, C.A., Current, J.R., Benton, W.C., 1991. Vendor selection criteria andmethods. European Journal of Operational Research 50 (1), 2–18.

Weber, C.A., Current, J.R., Desai, A., 1998. Non-cooperative negotiation strategiesfor vendor selection. European Journal of Operational Research 108, 208–223.

Wu, C., Barnes, D., 2009. A model for continuous improvement in supplierselection in agile supply chains. Knowledge and Process Management 16 (3),85–110.

Wu, C., Barnes, D., 2010. Formulating partner selection criteria for agile supplychains: a Dempster-Shafer belief acceptability optimization approach. Inter-national Journal of Production Economics 125 (2), 284–293.

Wu, C., Barnes, D. A dynamic feedback model for partner selection in agile supplychains. International Journal of Operations & Production Management, inpress.

Wu, C., Barnes, D., Rosenberg, D., Luo, X.X., 2009. An analytic network process-mixed integer multi-objective programming model for partner selection inagile supply chains. Production Planning & Control 20 (3), 254–275.

Wu, D.D., Olson, D., 2010. Enterprise risk management: a DEA VaR approach invendor selection. International Journal of Production Research 48 (16),

4919–4932.




Wu, D.D., Zhang, Y.D., Wu, D.X., Olson, D.L., 2010. Fuzzy multi-objective program-ming for supplier selection and risk modeling: a possibility approach.European Journal of Operational Research 200 (3), 774–787.

Wu, D.S., 2009. Supplier selection: a hybrid model using DEA, decision tree andneural network. Expert Systems with Applications 36 (5), 9105–9112.

Wu, T., Blackhurst, J., 2009. Supplier evaluation and selection: an augmented DEAapproach. International Journal of Production Research 47 (16), 4593–4608.

Xia, W., Wu, Z., 2007. Supplier selection with multiple criteria in volume discountenvironments. Omega 35 (5), 494–504.

Ye, F., Li, Y.N., 2009. Group multi-attribute decision model to partner selection inthe formation of virtual enterprise under incomplete information. ExpertSystems with Applications 36 (5), 9350–9357.

Yeh, W.C., Chuang, M.C., 2011. Using multi-objective genetic algorithm for partnerselection in green supply chain problems. Expert Systems with Applications 38(4), 4244–4253.

Yigin, I.H., Taskin, H., Cedimoglu, I.H., Topal, B., 2007. Supplier selection: an expertsystem approach. Production Planning & Control 18 (1), 16–24.

Yucel, A., Guneri, A.F., 2011. A weighted additive fuzzy programming approach formulti-criteria supplier selection. Expert Systems with Applications 38 (5),6281–6286.

Zarvic, N., Seifert, M., 2008. Coordination-based dependencies for partner selectionin virtual organisations. PPS Management 13 (4), 18–20.

Zeydan, M., Colpan, C., Cobanoglu, C., 2011. A combined methodology for supplierselection and performance evaluation. Expert Systems with Applications38 (3), 2741–2751.

Zhang, J.L., Zhang, M.Y., 2011. Supplier selection and purchase problem with xedcost and constrained order quantities under stochastic demand. International Journal of Production Economics 129 (1), 1–7.

Zhao, F., Hong, Y., Yu, D., 2006. A multi-objective optimization model of thepartner selection problem in a virtual enterprise and its solution with geneticalgorithms. International Journal of Advanced Manufacturing Technology28 (11–12), 1246–1253.

Zhao, K., Yu, X., 2011. A case based reasoning approach on supplier selection inpetroleum enterprises. Expert Systems with Applications 38 (6), 6839–6847.


A Literature Review of Decision-making Models and Approaches for Partner Selection in Agile Supply...

Documents

Transcript of A Literature Review of Decision-making Models and Approaches for Partner Selection in Agile Supply...