A literature review of decision-making models and approaches for partner selection in agile supply chains

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

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Journal of Purchasing & Supply Management journal homepage: www.elsevier.com/locate/pursup

A literature review of decision-making models and approaches for partner selection in agile supply chains Chong Wu a,1, David Barnes b,n a b

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

a r t i c l e i n f o

a b s t r a c t

Article history: Received 18 May 2011 Received in revised form 8 August 2011 Accepted 26 September 2011 Available online 12 October 2011

The paper reviews the literature on supply partner decision-making published between 2001 and 2011, a period that has seen a significant increase in work published in this field. The progress made in developing new models and methods that can be applied to this task is assessed in the context of the previous literature. Particular attention is given to those methods that are especially relevant for use in agile supply chains. The paper uses a classification framework that enables models intended for similar purposes to be compared and tracked over time. It is also used to identify a number of gaps in the literature. The findings highlight an on-going need to develop methods that are able to meet the combination of qualitative and quantitative objectives that are typically found in partner selection problems in practice. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Literature review Partner selection Agile supply chain Decision-making

1. Introduction In today’s highly competitive environment, enterprises need to take advantage of any opportunity to improve their performance. There has been growing recognition of the need for a firm to work closely with its supply chain partners in order to optimize its business processes. A key step in the formation of any supply chain is that of supply partner selection (Mikhailov, 2002), which is reflected in the growing research interest in this issue in recent years. De Boer et al. (2001)’s review of the literature on supply partner decision-making represented pioneering work in that it classifies supplier selection methods according to different stages of the supplier selection process. Since then two other literature review papers are particularly noteworthy. Aissaoui et al. (2007) adopted De Boer et al. (2001)’s three-stage framework in their literature review. However, their focus was on the final stage of the selection process. More recently, Ho et al. (2010) reviewed multi-criteria decision-making approaches used in supplier evaluation and selection. However, they do so relatively uncritically and without employing any specific framework. As it is nearly a decade since De Boer et al. (2001)’s paper, it now seems an appropriate time to revisit this issue. During this time, the concept of the agile supply chain (ASC) has become increasingly important as means of achieving n

Corresponding author. Tel.: þ44 20 7911 5000x3426; fax: þ44 20 7911 5703. E-mail addresses: [email protected] (C. Wu), [email protected] (D. Barnes). 1 Tel.: þ86 5922180776. 1478-4092/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.pursup.2011.09.002

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 change frequently in response to fast-changing markets (Christopher and Towill, 2000; Wu and Barnes, in press). Miles and Snow (1984) were amongst the first to recognize the importance of supply partners as firms increasingly adopted vertically disaggregated forms. Their description of a ‘‘dynamic network’’ as a combination of independent businesses, each contributing what it does best to the network as a whole, foreshadowed the type of relationships that are characteristic of ASCs. More recently, in an era of increased outsourcing, Huang et al. (2004) have emphasized the concept of the virtual enterprise as an effective and viable solution to the problem of fulfilling requirements in a global market. In ASCs, companies must align with their supply partners to streamline their operations, as well as working together to achieve the necessary levels of agility throughout the entire supply chain and not just within an individual company. The increasing importance of ASCs has focused more attention on supply partner selection. In ASCs, decision-making about partner selection is particularly challenging, because of the complexity of putting together a network under dynamic conditions. Researchers have generally concluded that the problem of supplier selection under such conditions cannot be solved effectively and efficiently unless it is broken down into several sub-problems, which can then each be addressed and solved individually. For example, Lorange et al. (1992) developed a two-stage supply partner selection approach: first evaluating the degree of match with a candidate partner and

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

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

2. Methodology Relevant papers were identified by searching ISI Web of Knowledge using the keywords ‘‘partner selection’’, ‘‘supplier selection’’ and ‘‘vendor selection’’ in the fields of ‘‘Operations Research & Management Science’’ and ‘‘Management’’ date from 2001 to 2011 (up to 5 May 2011). The search returned one hundred and forty journal articles. These are listed in Appendix 1. Once identified, the papers were classified using the framework 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 and Fig. 1, and now described in more detail. The horizontal axis of the framework categorizes the complexity and degree of uncertainty associated with purchasing and supplier selection decisions. Based on the work of De Boer et al. (2001) and Robinson et al. (1967), it characterizes three typical situations: new task, modified re-buy and straight re-buy. The new task situation involves an entirely new product or service. As there is no previous experience, this situation carries a high level of uncertainty. In a modified re-buy, a new product is purchased from a known supplier or a modified product is purchased from a new supplier. Therefore, this has a moderate level of uncertainty. Finally, the straight re-buy has the lowest level of uncertainty as the buyer has near perfect information about the product specification and the supplier. Many Feedback

Criteria formulation Potential combinations

then analyzing the market potential, main competitors and simulating worst-case scenarios after the formation of the partnership. De Boer et al. (2001) characterized the supply chain partner selection process as three main stages, comprising the ‘‘criteria formulation’’ and ‘‘qualification’’ stages in which suitable partners are identified, followed by the ‘‘choice’’ stage in which a final selection is made from amongst suitably qualified partners. Huang et al. (2004) propose a two-stage selection framework based on the distinction between hard and soft factors in affect the partner selection process. Stage one identifies potential partner candidates who can meet the criteria of timeliness, quality and price for the required products or services. Stage two focuses on the assessment of their cooperation potential. Che (2010) also developed a two-phase model. In phase 1, suppliers are clustered according to their characteristics for meeting customer needs on multiple dimensions of cost, quality and time. In phase 2, a multi-criteria optimization mathematical model is constructed on the basis of these clusters. The aim of this paper is to review the literature on supply partner selection decision-making published between 2001 and 2011 and to place this in the context of previous work published in this field. Particular attention is given to those methods that may be especially relevant for supply partner selection in agile supply chains. In reviewing the literature published since 2001, the paper will apply the classification framework developed by Luo et al. (2009) and Wu and Barnes (in press), based on De Boer et al. (2001), identify any new trends in the literature and highlight any gaps in the literature that would benefit from future research efforts. Classification in science has properties that enable the representation of entities and relationships in structures that reflect knowledge of the domain under consideration (Kwasnik, 1999). Classification can also be helpful for the processes of knowledge discovery and creation. In this paper, the classification method is applied to the literature on partner selection in order to advance our understanding of this field of research and to facilitate the discovery of new knowledge in the subject. In addition, classifications can also be used to enhance our ability to discover meaningful information in large amounts of literature. Recent developments in our ability to retrieve large amounts of literature have stimulated an interest in new ways of exploiting the information available to advance the knowledge in this field. Subsequent to this Introduction, the ‘‘Methodology’’ section explains how the literature review was conducted and in particular how a phased model for supply partner selection in ASCs was used as the basis for the analysis. This model is then used as the basis of the structure of the next four sections, which are

257

Qualification

Final selection Application feedback

Few Low

Information available to purchasing enterprise

High

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).

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

New task

Re-buy

Agile supply chain

Modified re-buy

Straight re-buy

1. Formulation of criteria

No previously used criteria available Moderate initial set of partners

Previously used criteria available Large set of initial partners

Previously used criteria available Small set of partners

2. Qualification

Sorting rather than ranking No historical records available

Sorting as well as ranking Historical data available

Sorting rather than ranking Historical data available

3. Final selection

Ranking rather than sorting Many criteria Much interaction Model used once

Ranking rather than sorting Fewer criteria Less interaction Model used again

Evaluation rather selection Moderate criteria Moderate interaction Model used again

4. Application feedback

Any new customer demands? Modifying or rebuild the models used before?

Change current supply chain structure? The performance of the current supply chain structure fulfils the demands?

Stronger the relationships? Any more alternatives?

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The vertical axis of the framework uses the conceptual model extended by Luo et al. (2009) and Wu and Barnes (in press) for ASCs based on De Boer et al. (2001)’s work, namely formulation of criteria, qualification, final selection and application feedback. Use of this step-by-step approach offers an effective means of solving what would otherwise be a highly complex problem. The structured and comprehensive approach is necessary to meet the challenge of partner selection in dynamic environment, and helps ensure that successful partnerships will not be threatened for 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 Boer et al. (2001), there are three types of rebuy and one new task decision-making situations. For simplicity, this paper combines the routine items straight rebuy decision-making situation and the strategic/bottleneck straight re-buy decision-making situation into one straight re-buy decision-making situation only. For the vertical axis, recent research has emphasized the importance of application feedback (Wu and Barnes, 2009). As Christopher and Towill (2000) pointed out, such a phase is important and necessary in today’s highly competitive environment. In incorporating this phase, this framework represents an advance on previous models of the partner selection process. Analyzing the relevant papers identified from the literature in accordance with these two dimensions, enables the various decision models associated with partner selection to be located within the framework. The framework is accordingly used as the basis for categorization as it enables each model to be associated with a specific selection phase and situation. This enables models intended for similar purposes to be compared and be tracked over time. It is also used to help identify the progress associated with particular selection phases and purchasing situations made in the research literature in the last decade to be assessed and compared to the best known literature published prior to 2001. Similarly, any gaps in the literature can also be identified. For reasons of space, the discussions that follow do not report in detail from all one hundred and forty journal articles. Rather they focus on those articles considered to be the most important and typical of the decision-making situations and methods/ models that they present. The choices of articles singled out for mention are inevitably somewhat subjective, but they are influenced by considerations, inter alia, of the frequency of their individual 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 selection process is that of determining what criteria to use in subsequent decision-making. Traditionally, the most important purchasing criterion has been that of cost. This has arguably become 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 than strategic approach to purchasing, which has led to its exclusion from the corporate agenda In his classic research, Dickson (1966) argued that the vendor selection 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 principal manufacturing performance and competitive priorities of cost, quality, delivery and flexibility (Aksoy and Ozturk, 2011), as these can be equally or even more important, when supply has a direct impact 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 partnerships are typically driven by the expectation of quality, cost efficiency, delivery dependability, volume flexibility, information and customer service (Rezaei and Davoodi, 2011). Thus, partner selection in ASCs can be viewed as a multi-criteria decision making problem that involves assessing trade-offs between conflicting 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 the customer, expertise with the use of alloys, acceptance of small orders, or product ranges (van der Rhee et al., 2009). Including a broad range of criteria, however, makes partner selection decisions complex (Weber et al., 1991). Tracey and Tan (2001) developed partner selection criteria, including quality, delivery, reliability, performance and price, and assessed customer satisfaction based on price, quality, variety and delivery. Kannan and Tan’s (2002) partner selection method is based on criteria of commitment, needs, capability, fit and honesty. They have also developed a partner evaluation system based on criteria of delivery, quality, responsiveness and information sharing. Lin et al. (2006) developed a fuzzy agility index, comprising attribute’ ratings and corresponding weights, and is aggregated by a fuzzy weighted average. Xia and Wu (2007) proposed an integrated approach to simultaneously determine the number of suppliers to employ and the order quantity allocated to these suppliers, with multiple criteria and with supplier’s capacity constraints. More so, as criteria may have quantitative as well as qualitative dimensions, and may also be conflicting. Preference for a given partner is generally assumed to depend on an assessment, case-by-case, of the quality, price, delivery and service it offers. The number and the set of the assessment criteria involved should depend on the product/service in question. However, considering a large number of criteria can make supplier selection excessively complex and problematic (Zeydan et al., 2011). Consequently, researchers have put much effort into methods that aim to develop a smaller, more customized set of attributes by determining the relative importance 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 selection. Lewis (2002) models the supply chain partner selection problem by proposing a qualitative approach. Several criteria were suggested, such as value added to products, operations and technologies strengthening, and improvement in market access, to measure the appropriateness of a particular strategic alliance for a firm. Lin and Chen (2004) proposed a systematic method to build a general set of criteria that can then be modified for a specific industry. Huang and Keskar (2007) present an integration mechanism that takes into account product type, supplier type, and supplier integration level criteria, to produce a set of comprehensive and configurable criteria for partner selection by original equipment manufacturers. Wu and Barnes (2010) draw on Dempster-Shafer theories and optimization in developing a method for formulating criteria to use in partner selection 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) can still be used to classify the majority of the criteria for supply partner selection presented in the more recent literature. Similarly, the result of Weber et al.’s (1991) review of 74 papers showed that price, delivery, quality and production capacity and location were the most commonly cited criteria. However, it should be acknowledged that an evolving competitive environment might modify the relative importance of the criteria. Furthermore, it is worth noting that most existing approaches

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

Respondents/empirical cases

Measurement approach

Main evaluation criteria

Dulmin and Mininno (2003)

A mid-sized Italian public road and rail The PROMETHEE approach 1. Make-up; 2. Processing time; 3. Prototyping time; 4. Quality system; firm 5. Co-design; 6. Technological levels

Lin and Chen A international personal computer (2004) company

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 manufacturer producing various functional components

AHP (pairwise comparisons)

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

Lin et al. (2006)

A Taiwan based international IT products company

Fuzzy logic and aggregate fuzzy 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 (pairwise comparisons)

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

Kannan and Haq (2007)

A company southern India

Interpretive structural modeling

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

Discrete choice analysis

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

Dempster-Shafer and optimization theory

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

van der Rhee 200 respondents from Germany, the et al. (2009) UK, Italy and France Wu and Barnes (2010)

Literatures and interviews with operations managers

do not take into account the dynamic interrelation between partner selection and supply chain performances. Table 2 presents a summary of representative studies on evaluation and formulation partner selection criteria in chronological order.

4. Decision models for qualification The qualification stage involves reducing the set of all possible partners to a smaller set of acceptable suppliers (De Boer et al., 2001). Sarkar and Mohapatra (2006) demonstrated that a reduced solution space for partner selection is a prerequisite for constructing closer relationship with partners. Therefore, qualification is a sorting process rather than a ranking process. The first step of this process always consists of defining and determining the set of acceptable partners while possible subsequent steps serve to reduce the number of partners to consider. The methods used for the qualification can be classified as follows: 4.1. Data envelopment analysis models Data envelopment analysis (DEA) is built on the concept of the efficiency of a decision alternative. Weber et al. (1991, 1998) discussed the application of DEA in partner selection some years ago. More recently, Wu and Blackhurst (2009) pointed out that selecting suppliers is an essential part of effectively managing today’s dynamic supply chains. They proposed a supplier evaluation and selection methodology based on an extension of DEA, which they call augmented DEA. Through the incorporation of a range of virtual standards, the methodology enhances discriminatory power over basic DEA models. One of the advantages of their model is that the weight constraints are used to reduce the possibility of having inappropriate input and output factor weights. Wu (2009) presented a hybrid model using DEA, decision trees and neural networks to assess supplier performance. The model applies DEA to classify suppliers into efficient and inefficient clusters based on the resulting efficiency scores and yield a favorable classification and prediction accuracy rate. Wu and Olson (2010) presented

the development and current status of enterprise risk management and developed a DEA VaR model to conduct risk management in vendor selection. Their models provided means to quantitatively improve decision making with respect to risk. Saen (2010) also proposed a DEA-based methodology for supplier selection. The strong point of her/his model is that it considers both undesirable outputs and imprecise data simultaneously. To increase the supplier selection and evaluation quality, Zeydan et al. (2011) considered both qualitative and quantitative variables in evaluating performance for selection of suppliers based on efficiency and effectiveness. In their model, qualitative variables are transformed into a quantitative variable for using in DEA. 4.2. Cluster analysis models Cluster analysis is a basic method from statistics. It uses a classification algorithm to group a number of items which are described by a set of numerical attribute scores into a number of clusters such that the differences between items within a cluster are minimal and the differences between items from different clusters are maximal (De Boer et al., 2001). Cluster analysis can be applied to a group of partners that are described by scores on some criteria too. The result is a classification of partners in clusters of comparable partners. Hinkle et al. (1969) were one of the first researchers on adopt this approach. Ha and Krishnan (2008) introduced a hybrid method which incorporates multiple techniques into the partner evaluation process, in order to select the most competitive one(s) in supply chains. The proposed model is flexible enough to allow the decision maker to do single sourcing and multiple sourcing by calculating a combined supplier score. Furthermore, this method can draw the supplier map to position suppliers within the qualitative and quantitative dimensions of performance efficiency by performing a cluster analysis. To effectively segment and select suppliers, Che (2010) developed a genetic simulated annealing k-means algorithm. By using the algorithm, the suppliers were clustered according to the characteristics for customer needs including multiple dimensions product cost, product quality and manufacturing time. It is found that select suppliers after cluster analysis, unwanted candidate

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suppliers could be effectively eliminated, and the resulting supplier combination could still meet customer needs. Two major drawbacks exist in these cluster methods. Firstly, only globalscaled clusters have been verified. Secondly, the relationship between global and local perspectives on cluster detection has not 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 data and/or the enterprise’s own experience. The evaluation process consists of categorizing the potential partner’s performance on a criterion as either ‘‘positive’’, ‘‘neutral’’ or ‘‘negative’’. After a potential partner has been rated on all selected criteria, the decision makers give an overall rating by ticking one of the three options again. In this way, potential partners are sorted into these three categories. During this decade, there is few research using categorical approaches as quantitative methods dominating the area. 4.4. Artificial intelligence models Artificial intelligence models are based on computer-aided systems which in one way or another can be ‘‘trained’’ by experts or historical data. Subsequently, when non-experts who face similar but new decision situations, they can consult the system models. Humphreys et al. (2003) presented a framework of environmental criteria that a company can consider during their supplier selection process as environmental pressure increases. A knowledge-based decision support system which integrated both quantitative and qualitative environmental criteria into the supplier selection process was built within the framework. Yet, it is very difficult to set an appropriate and reasonable acceptance threshold value. Lee and Ou-Yang (2009) propose an artificial neural networkbased model to provide support and recommendations to buyers involved in partner selection negotiations. The authors have shown that the ANN approach offers an adaptive negotiation support tool for use in sophisticated and challenging negotiations that can help achieve the buyer’s objective. However, the limitations of model include an inadequate number of input factors and its predication objective (i.e. the bid price only). Luo et al. (2009) developed a model that helps overcome the information processing difficulties inherent in screening a large number of potential suppliers in the early stages of the selection process. Based on radial basis function artificial neural network, their model enables potential suppliers to be assessed against multiple criteria using both quantitative and

qualitative measures. Aksoy and Ozturk (2011) presented an ANNbased supplier selection and supplier performance evaluation systems to aid JIT manufacturers in selecting the most appropriate suppliers and in evaluating supplier performance. One of advantages of their ANN model is that decision-makers can see the points that need to be developed in the output value of the ANN model. Another AI-technology used in supplier evaluation is expert systems. Choy et al. (2002) present an intelligent partner relationship management system using hybrid case based reasoning and ANN techniques to select and benchmark potential partners. Yigin et al. (2007) also developed an expert system for partner selection based on six rules and fourteen criteria which are grouped step by step. As the general characteristics of expert system, their method could never fully capture the expertise used in difficult situations that is common in ASCs partner selection. Case-based-reasoning systems also belong to artificial intelligence approach. Primarily, a case-based-reasoning system is a software-driven database that provides a decision-maker with useful information and experiences from similar, previous decision situations. Choy et al. (2004) used supplier relationship management system to integrate supplier rating system and product coding system by case-based-reasoning technique, to select preferred suppliers during the new product development process. It is found that the outsource cycle time could be reduced and manufacturers can identified preferred suppliers to form a supply network effectively. Faez et al. (2009) combined integer programming, fuzzy set theories and CBR method for the vender selection program. Their model improved the covential CBR systems by covering the fuzzy parameters. Moreover, a mixed integer programming model was applied to simultaneously consider suitable vendor selection and order allocation. Based on data mining techniques, Zhao and Yu (2011) mined the data in multi data resources in case-based-reasoning system to improve the autoimmunization level of knowledge acquisition, performance of the system, and expedite the exploring period of the intelligent system. However, the main problem with these models is complexity. In particular, as the numbers of cases increases, the efficiency of decision-makings decreases very quickly. Table 3 provides a summary of the models in the literature on the qualification.

5. Decision models for final selection Final selection models involve selecting which of the qualified partners to use for specific purchases. Initial research in this area

Table 3 A summary of representative studies on qualification. Methods/ models

Key concept

DEA

Te weight constraints are used to reduce the Efficiency¼ the ratio of the weighted sum Weber et al. (1991, 1998), Wu and Olson (2008), Saen of its outputs to the weighted sum of its (2009), Wu (2009), Wu and Blackhurst (2009), Azadeh and possibility of having inappropriate input and output factor weights Alem (2010), Wu and Olson (2010), Saen (2010), Zeydan inputs et al. (2011)

Cluster analysis

Hinkle et al. (1969), Ha and Krishnan (2008), Che (2010) Differences between items within a cluster are minimal; Differences between items from different clusters are maximal

Categorical models

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

Artificial ‘‘Trained’’ computer-aided systems which intelligence do not require formalization of the decision-making process

Representative works

Strong/weakness points

Only global-scaled clusters have been verified. Relationship between global-local perspectives on cluster detection has not been explored

None found

Cannot be applied to a complex problem, such as that represented by a hierarchical structure of decision attributes

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

Can cope better with complexity and uncertainty than traditional models as it designed to operate in a similar way to human judgement

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261

Table 4 The classification 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), Amid et al. (2006), Ng (2008), Jarimo and Salo (2009)

Weber et al. (1998), Hajidimitriou and Georgiou (2002), Talluri and Baker (2002), Cakravastia and Takahashi (2004), Sha and Che (2005), Choi and Chang (2006), Cao and Wang (2007), Chen et al. (2007), Stadtler (2007), Glickman and White (2008), Guneri et al. (2009), Kheljani et al. (2009), Nepal et al. (2009), Xu and Nozick (2009), Hsu et al. (2010), Sawik (2010), Ravindran et al. (2010), Zhang and Zhang (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), Chou et al. (2007), Bayrak et al. (2007), Jain et al. (2007), Buyukozkan et al. (2008), Chan et al. (2008), Chou and Chang (2008), Amid et al. (2009), Amin and Razmi (2009), Boran et al. (2009), Guneri and Kuzu (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), Liu and Hai (2005), Sha and Che (2006), Sarkis et al. (2007), Demirtas and Ustun (2008), Sari et al. (2008), Wu et al. (2009), Wu et al. (2009a), Chamodrakas et al. (2010), Lin et al. (2010, 2011), Buyukozkan and Cici (2011)

Basnet and Leung (2005), Hong et al. (2005), Wadhwa and Ravindran (2007), Liao and Rittscher (2007a), Ustun and Demirtas (2008), Wu et al. (2009), Huang et al. (2010), Mendoza and Ventura (2010), Keskin et al. (2010), Kara (2011), Rezaei and Davoodi (2011), Vanteddu et al. (2011)

mainly dealt with single business process, single-objective and single-product problems. However, subsequent studies have increasingly focused on multiple business processes, multi-criteria, multi-products cases. The overwhelming majority of supply partner decision models apply to the final selection phase. Partner selection models can be distinguished in according to whether they are for single or multiple deals/products, and whether or not they involve inventory management (see Table 4). As Table 4 illustrates, almost two thirds of models identified in the literature can be characterized as ‘‘single deal/product’’. These models consider the selection of a partner for a single one product or group of items. ‘‘Multiple deals/products’’ models, on the other hand, take into account situations involving different products in product groups. Furthermore, most of the existing literature does not consider inventory management of the items purchased. The relevant literature on each of these techniques is discussed as below. 5.1. Linear weighting models In linear weighting models, different weights are given to different criteria, with the biggest weight indicating the highest importance. Plenty of adaptations have been suggested for the sake of making linear weighting models better capable of dealing with the uncertainty and imprecision which inevitably surrounds partner selection in real business practice. Ko et al. (2001) suggested an idea for selecting partners in a distributed dynamic manufacturing environment, which enables companies to share their machine capacities. They proposed a model to minimize the sum of the operation and transportation costs based on alternative process plans considering several kinds of operation characteristics. Amid et al. (2006) established a fuzzy multiobjective linear model to solve the partner selection problem in a supply chain by applying an asymmetric fuzzy-decision making technique. Jarimo and Salo (2009) applied a mixed-integer linear model to assist the selection of partners in a virtual organization. Their model extends the fixed and variable costs to include accommodate transportation costs, capacity risk measures, and inter-organizational dependencies such as the success of past collaboration. Ng (2008) constructed a weighted linear program for the multi-criteria supplier selection problem by using a transformation technique that could solve the problem without

applying an optimizer. The benefit of the model is that it does not require the user to learn any optimization technique. 5.2. Mathematical programming models Geoffrion and Graves (1974) undertook early mathematical work in the area of supply chain design. They proposed a multicommodity logistics network design model for optimizing product flows through the whole supply chain, which involved all the nodes from raw material vendors to producers to distribution centers, and finally to customers. Subsequently, a number of different mathematical programming models have been proposed. They can be classified into the following three subcategories. 5.2.1. Goal programming Hajidimitriou and Georgiou (2002) employed a goal programming (GP) technique for the supply partner selection problem that was able to achieve multiple goals for different levels of performance of the corresponding attributes. However, this method did not consider the combination of potential partners that may results in better solutions for the whole supply chain comparing with only one candidate being identified. Basnet and Leung (2005) solved the supplier selection problem in a multiperiod inventory lot-sizing scenario. Their work gave an answer to the question about what products to order in what quantities with which suppliers in which periods. Comparing with the enumerative search algorithm they proposed, the heuristic that based on the traditional lot sizing based heuristic algorithm is fast enough for practical problems. Ravindran et al. (2010) developed two types of risk models, value-at-risk and miss-the-target, for the partner selection problem that has been modeled as a multicriteria optimization problem. The researchers solved the problem in two separate steps, named qualification and order quantities allocation step, by using the goal programming approach. Vanteddu et al. (2011) considered inventory costs and the supply chain ‘‘cycle time’’ reduction costs and proposed a programming model for focus dependent supplier selection problem. Yet, the model does not involve any qualitative factors such as quality, supplier’s reputation, cultural match, etc.

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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 initial attempt, they applied the interactive weighted Tchebycheff method and Benders decomposition method to generate the set of effective alternatives before the order volume allocating to each selected supplier. Zhao et al. (2006) argue that the virtual enterprise is a basic organization form to achieve agile manufacturing in enterprise. As such, selecting the most appropriate supply partners is a key success factor. Based on the concept of the inefficient candidate, they constructed a multi-objective optimization model while applying both fuzzy factors-based rules and the genetic algorithm during the selection phases. Wadhwa and Ravindran (2007) considered price, lead-time and quality as three conflicting criteria that have to be minimized simultaneously in a multiobjective optimization model. They also present and compared several multi-objective optimization methods, including weighted objective, goal programming and compromise programming, for solving the multi-objective optimization problem. Cao and Wang (2007) proposed a two-stage vendor selection framework in outsourcing. The first stage helps the client to find the best match between the vendor and the outsourced project. In the second stage, employs the chosen vendor for the full implementation. Their work pointed out that the selection of vendors for the first stage testing is more about creating a good vendor portfolio than simply picking the frontrunners. Recently, Wu et al. (2010) presented a fuzzy multi-objective programming model to decide on supplier selection taking risk factors into consideration. The authors modeled a supply chain consisting of three levels and used simulated historical quantitative and qualitative data to measure the fuzzy events into the fuzzy multi-objective programming models. Furthermore, Rezaei and Davoodi (2011) developed two multi-objective mixed integer non-linear models for multi-period lot sizing problems involving multiple products and multiple suppliers. By comparing the outputs of these two models, the authors pointed out that buyers are better able to optimize their objectives 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 efficiency game model with integer and linear programming. Although this model overcomes the limitations of unrestricted weight flexibility, it risks producing a sub-optimal solution as the filter phase might filter the optimal one. Sha and Che (2005) pointed out that virtual integration offers a way to make manufacturing systems more agile and competitive, and then the problem of partner selection is the essential and the most important issue. Based on AHP, multi-attribute utility theory and integer programming, they developed a partner selection and production-distribution planning model. They also provided a Branch & Bound algorithm to solve the model. In addition to the typical costs associated with vendor selection and delivery, Keskin et al. (2010) considered the inventory-related costs and decisions of the stores. The authors emphasized the relationship between facility locations and proposed an integrated vendor selection and inventory optimization model. Zhang and Zhang (2011) developed a mixed integer programming model to minimize the total cost, including selection, purchase, holding and shortage costs. Yet, their model neglects the supply risk and price discount based on the order quantity. Sawik (2011) considered the risk-neutral and riskaverse objective functions separately and simultaneously in a bi-objective optimization problem. Based on mixed integer programming models, this approach provides the decision-maker

with a simple tool for evaluating the relationship between expected and worst-case costs. 5.3. Analytic hierarchy/network process models Tam and Tummala (2001) proposed and applied an AHP-based model to a real case study in selecting a vendor for a telecommunications system. The use of the proposed model proves that it can improve the group decision making and reduce the time taken to select a vendor. Mikhailov (2002) applied AHP to cope with the fuzziness that occurs when a decision-maker compares the relative importance of different attributes. Contrary to the other interval prioritization methods, this method can derive crisp priorities from inconsistent interval pairwise comparison matrices. However, this method ignores the effects resulted from interdependent attributes. Chan (2003) proposed a Chain of Interaction method to solve the problems associated with the dynamic nature of supply chain management by using subjective human judgment in determining the relative importance of the tangible selection factors. In this method, an Interactive Selection Model is suggested to systemize the earlier steps firstly, followed by the implementation of the AHP and the multi-criterion decision making software Expert Choice. As the final outcome of the Interactive Selection Model greatly depends on the quality of the data collected, a systematic data collection method is required whilst applying their interaction method and model. Liu and Hai (2005) developed a voting AHP method, which combines DEA and AHP methodology. After determining the weights in a selected rank, their method selects partners by comparing 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 a well-known Turkish company operating in the appliance industry. In this method, the DEA approach is embedded into AHP methodology. The criteria the model used reflect closer to the real optimum of the decision made. Sari et al. (2008) proposed an AHP model to contribute in the selection of the partner companies in the dynamic environment. Their AHP model was linked with a generic multi-criteria analysis model, and provided a means of structuring the decision problem and estimating importance weights for the objectives of the various stakeholder groups. In general, the methods proposed by using AHP only consider oneway hierarchical relationships between the factors. This is a simplistic assumption that does not consider the many possible relationships. Moreover, AHP does not explicitly consider the interactions between the various factors/clusters. To overcome the disadvantages of the previous AHP models proposed, Sarkis et al. (2007) built a strategic model for partner selection by using analytical network process (ANP) methodology. The ANP, also introduced by Saaty, is a new generalization of the AHP (Saaty, 1996). Whereas AHP represents a framework with a uni-directional hierarchical relationship, ANP allows for more complex interrelationships among decision levels and attributes. Therefore, a hierarchical structure with a linear top-to-bottom form is not applicable for a complex system. Sarkis et al. (2007)’s ANP model effectively overcomes the problem of rank reversal which is also a limitation of AHP. Yet, as the authors acknowledged, without incorporating secondary criteria, the final solutions may not be clearly defined. Considering both tangible and intangible factors, Demirtas and Ustun (2008) integrated ANP and multi-objective mixed integer linear programming approach to answer two questions: (1) which suppliers are the best, and (2) how much should be purchased from each selected supplier if anyone supplier could not fulfill the whole demand? The special characteristic of the model is that it could include the decision makers’ preferences. Wu et al. (2009)

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proposed a two-stage approach, based on the application of an analytic network process-mixed integer multi-objective programming (ANP-MIMOP) model, to solve the problem of partner selection in ASCs. In their first stage, an ANP methodology is applied to calculate the priorities of different criteria for partner selection. Secondly, using these priorities, a MIMOP method is used to determine the supply chain structure and optimize the allocation of order quantities. Buyukozkan and Cifci (2011) developed a fuzzy ANP approach within multi-person decisionmaking schema under incomplete preference relations for sustainable suppliers’ selection. These ANP models can overcome the shortcomings of AHP approaches but cannot solve the detailed lot-sizing problem. 5.4. Fuzzy sets models A number of authors suggest using fuzzy sets theory (FST) to model uncertainty and imprecision in partner selection situations. Sarkar and Mohapatra (2006) used a fuzzy set approach to measure the imprecision of these two dimensions to rank and reduce the number of potential partners, by focusing on their performance and capability. However, there is a compensation problem with this method, in that a potential partner scoring highly in one dimension may compensate for a low score in some other. Using fuzzy analytical hierarchy process and a genetic algorithm, Haq and Kannan (2006) developed an integrated supplier selection and multi-echelon distribution inventory model in a built-to-order supply chain environment. Kumar et al. (2006) combined the multi-objective integer programming and fuzzy set theories for vendor selection. In their model, various input parameters have been treated as vague with a linear membership function. The proposed model provides a tool that facilitates the vendor selection and their quota allocation under different degrees of information vagueness. Bevilacqua et al. (2006) proposed a fuzzy quality function deployment (QFD) approach to support supply partner selection. This approach uses both internal and external variables to rank the potential partners. The advantage of this method lays in its ability to transforming decision makers’ verbal assessments to linguistic variables, which are more accurate than other non-fuzzy methods. However, it is used to rank potential partners, which is not the main objective in the early phase of partner selection. Chou et al. (2007) utilized the supplier positioning matrix to link the capability of potential suppliers with the requirements of the customers. Then, their research identified the strategy-aligned criteria for vendor selection in a modified re-buy situation. Finally, based on the type of components required by the customers, a fuzzy factor rating system was used to evaluate the potential vendors. Bayrak et al. (2007) also proposed a fuzzy approach method for partner selection by assessing delivery, quality, flexibility, and service criteria. However, it is a pure subjective method that will inevitably depend heavily on experts’ experiences. Buyukozkan et al. (2008) proposed a fuzzy AHP and fuzzy Technique for Order Preference by Similarity to Ideal Solution approach to rank partners under conditions of uncertainty and complexity. To avoid the single decision maker’s bias, it would be beneficial to extend the model in a group decisionmaking environment. As different enterprises have different motivations for establishing supply partners, the identification of universal criteria weights for use in any situation will not be appropriate. Based on fuzzy sets theory and VIKOR method, Sanayei et al. (2010) applied linguistic values to assess the ratings and weights for the established criteria, and built a hierarchy multiple criteria decision making model to deal with the supplier selection problems in the supply chain system. The VIKOR method in their model is developed to solve multiple criteria

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decision making problems with conflicting and non-commensurable criteria. Yucel and Guneri (2011) developed a weighted additive fuzzy programming approach for multi-criteria supplier selection. Their model has not very computational procedure, so it can deal with the rating of factors effectively. 5.5. Genetic algorithms models There are a number of studies that try to use genetic algorithms 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 inefficient candidate, they built a risk-based partner selection model by using genetic algorithm (GA) to minimize the risk in partner selection. However, they failed to simultaneously consider both qualitative and quantitative evaluation attributes. Sha and Che (2006) proposed an approach which is based on the GA, AHP and the multi-attribute utility theory to satisfy simultaneously the preferences of the suppliers and the customers at each level in the network. This approach seems likely to outperform that of the single-phase genetic algorithm in supplier selection. Liao and Rittscher (2007) constructed a multi-objective supplier selection model under stochastic demand conditions. They extended the measurement of supplier flexibility to consider demand quantity and timing uncertainties comprehensively. Moreover, they proposed a problem specific genetic algorithm to handle the combinatorial optimization problem. Their solution alternatives and objective trade-offs are valuable for the final supplier selection. Wang et al. (2009) emphasized that partner selection is a key step in organizing a well-designed dynamic supply network. They carefully analyzed various collaboration patterns between distributed partners with the corresponding evaluation criteria for collaboration time and cost, and then proposed a genetic algorithm solution for collaboration cost optimization-oriented partner selection. Yeh and Chuang (2011) also developed an optimum mathematical planning model for green partner selection by adopting two multi-objective genetic algorithms to find 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 that possessed by most managers and organizational decision makers. Also a severe drawback is that some feasible solutions cannot be generated 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 belong to any above categories. These models and methods consider the dynamic decision-making situation, like ASCs. They are reviewed as below. Recognizing that virtual enterprises and agile supply chains are becoming a growing trend, Lau and Wong (2001) make use of the technologies such as MRPII, CAD, CAPP, DNC Link, to address the problem of selection and management in dynamic networks. Their paper provides insights into the issues raised by managing dispersed production networks using electronic media. Valluri and Croson (2005) applied agent-based modeling approach to improve the small numbers outsourcing model, which displays a complicated reward and punishment profile under incomplete information and dynamic decision-making condition. Moreover, their research shows that it is better for a buyer to transact with relatively few suppliers. Yet, in their model, the authors had allowed only relative quality evaluation while assuming the relative ranking to be 100% accurate.

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Sucky (2007) proposed a dynamic decision making approach for strategic vendor selection based on the principles of hierarchical planning. This approach considered the interdependencies in time arising from investment costs of selecting a new vendor and costs of switching from an existing vendor to a new one. Chen and Huang (2007) built a logic model to describe the relationships among the manufacturing capabilities of virtual enterprises and the manufacturing requirements of clients in the formation of dynamic virtual enterprise. Based upon the logic model, three search algorithms were developed for three different optimal goals, respectively. Fulga (2007) dealt with the partner selection problem which considers the bid cost and the bid completion time of subprojects, the due date and the budget constraint, in the fast changing business environment which potential partners dispersed geographically and had different core competencies. They gave two algorithms to solve their model. Zarvic and Seifert (2008) described an approach for the partner selection process, which is based on task-resource dependencies, with related constraints and priorities. Their dependency concepts between resources and tasks stemming from coordination theory have proved to be a helpful instrument for the purpose of partner selection in ASCs. The partner selection problem was modeled as a nonlinear integer programming problem by Cheng et al. (2009). Also, they gave an Ant Colony Optimization (ACO) algorithm with embedded project scheduling to solve the problem with the lead time, subproject cost and risk factor constraints to solve their model in the dynamic environment. Comparing with the GA and enumeration algorithm, the effectiveness of the ACO algorithm was shown. Darwish (2009) built a model that integrates the single-vendor single-buyer problem with the process mean selection problem. The integrated model allows the vendor to deliver the produced lot to buyer in a number of unequal-sized shipments and reduces the processing cost. Ye and Li (2009) constructed two MADM (multi-attribute decision model) methods for group decision making with interval values to solve partner selection problem under incomplete information in dynamic business situation. Based on deviation degree, the first method is a technique for order preference by similarity to ideal solution (TOPSIS) for group decision making. The second method is a TOPSIS for group decision-making based on risk factor. These two

methods for group decision making can not only be applied to solve the partner selection problem, but also be utilized in other similar fields, 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 partner selection is much depended on the technical and economic evaluation criteria. Crispim and de Sousa (2009, 2010) found that partner selection in ASCs, in general, is a very complex problem due to the dynamic topology of the network, the large number of alternatives and the different types of criteria. They proposed an exploratory process to help the decision makers to obtain knowledge about the network in order to identify the criteria and the potential partners that best suit the needs of each particular project. The processes they proposed involves a multi-objective tabu search meta-heuristic and a fuzzy TOPSIS algorithm. Table 5 summarizes some representative approaches in recent literature 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 selection process, namely that of application feedback, which to date has not been adopted by other researchers. They argue that such a stage is important and necessary in today’s highly competitive environment (Christopher and Towill, 2000). By applying principles of continuous improvement and organizational learning, this stage is designed to provide feedback so that the process of supplier selection process in ASCs can be continuously improved. Their model seeks to capitalize on the increased number of applications of the supplier section process that are inherent in the more dynamic conditions that prevail in environments in which ASCs are likely to be best suited. Its aim is to support organizational decision-makers in their efforts to optimize the performance of the supply chain by ensuring that the most appropriate suppliers are selected at all times. Their test within two simulation groups showed that participants found the model was likely to have significant benefits when used in practice.

Table 5 Representative approaches in supply partner selection literature. Methods/models categories

Author(s) and research publication years

Method/model types

Structure of Types of criteria criteria

Criteria aggregation

Assignment of weights

Mathematic programming

Hajidimitriou and Georgiou (2002) Rezaei and Davoodi (2011) Sha and Che (2005)

Goal programming

Three levels Quantitative

Linear aggregation

By users

Multi objective programming Integer programming

Flat

Non-linear programming

Generate by genetic algorithm Buyer’s subjective preference

Analytical hierarchical/ Mikhailov (2002) network process Sarkis et al. (2007)

Fuzzy AHP

Fuzzy set approach

Lin and Chen (2004)

Hierarchical fuzzy rules

Bayrak et al. (2007)

Fuzzy set approach

Wu and Barnes (2010)

Dempster-Shafer and optimization theories RBF-ANN

Combined methods

Luo et al. (2009) Wu et al. (2009) Wu and Barnes (2009)

Analytical network process (ANP)

Quantitative

Hierarchical Quantitative

Linear aggregation

Hierarchical Quantitative and Linear aggregation Qualitative Network Quantitative and Supermatrix Qualitative

Fuzzy algorithm

Hierarchical Quantitative and Fuzzy relationship Qualitative hierarchy Flat Quantitative Fuzzy weighted mean operators

Fuzzy favourability

Hierarchical Quantitative Qualitative Hierarchical Quantitative Qualitative ANP-MIMOP Network Quantitative Qualitative PDCA model and statistics Hierarchical Quantitative analysis Qualitative

and Dempster-Shafer evidence combination theory and RBF function and Mixed-integer multiobjective programming and Statistics analysis

Pairwise comparisons

By users and fuzzy algorithm By decision-makers By network training Pairwise comparisons by decision-makers By users and statistics methods

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supply partner selection over the last decade, especially in the last five years. With eighteen papers already published in the first four months 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 with strong quantitative traditions, as might be expected from the Operations Research and Management Science (OR/MS) field. Experts Systems with Applications (46), International Journal of Production Research (25) and International Journal of Production Economics (21) are the most frequent outlets. (NB, the nine papers shown in Fig. 2 from the Journal of Purchasing and Supply Management (JPSM) were identified from a search of ScienceDirect. These are listed in Appendix 2. These are included for comparison purposed only, as they lie outside of the method used to find the one hundred and forty papers that form the basis of this paper’s literature review.) The distribution of papers by their authors’ affiliations, as shown in Fig. 4, clearly shows the global interest in the issue of supply partner selection over the last decade, especially the greater China area and the USA. As to the research institutions of the authors (Fig. 5), we can see that there are three ‘‘research centers’’ around the greater China area and the world.

7. Discussion The distribution of the one hundred and forty papers identified in this research by their publishing year is shown in Fig. 2. This clearly illustrates the growing academic interest in the issue of 40

34

35

26 30 25

18

19 20

13

15

8 10

5

6

5

4 2

5

265

0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

7

Fig. 2. Numbers of papers on supply partner selection in international journals since 2001. (NB: the numbers of paper in 2011 is not a full year, only up to May. 5th).

6 5 4

50

46

3

40 30

2 25

1 21

0

20 9

9

9

8

8

10

5

5

2

2

0

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

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

2 2 2

Portugal Indonesia Greece Netherlands Iceland France Germany South Korea India England Canada Iran Turkey USA Mainland China &Hong Kong Taiwan District

3 4 4 5 6 7 10 11 13 17 22 26 28 0

5

10

15

20

25

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

30

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The biggest one is located in Taiwan (17) including NATL TAIPEI UNIV TECHNOL, NATL TAIWAN UNIV SCI & TECHNOL, CHUNG HUA UNIV, NATL CHENG KUNG UNIV, and NATL CHIAO TUNG UNIV. The second research center is located in Hong Kong (10), which includes HONG KONG POLYTECH UNIV and CITY UNIV HONG 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 final phase partner selection: linear weighting, mathematic programming, analytical hierarchical/network process and fuzzy set approach. Although each has its own specific merits, each also has its own shortcomings. First of all, linear weighting is a very simple method, but it depends heavily on human judgment. As such, different weights could be given to the various attributes according to the decision-makers’ subjective judgment, However, as Bevilacqua et al. (2006, p. 16) note, all linear weighting techniques are fully compensatory. Secondly, given an appropriate decision setting, mathematic programming allows the decision-makers to formulate the decision problem in terms of a mathematical objective function. It may be argued that mathematic programming models are more objective than rating models because they force the decision-maker to explicitly state the objective function. At the other hand, mathematic programming models often only consider the more quantitative criteria and this may cause a significant problem in considering qualitative factors. Furthermore, they also require arbitrary aspiration levels and cannot accommodate subjective attributes. Thirdly, AHP does not consider the interactions among the various factors and also cannot effectively take into account risk and uncertainty

in estimating the partner’s performance, because it presumes that the relative importance of attributes to evaluate partner performance is known with a high degree of certainty (Saaty, 1996). ANP can overcome the shortcomings of AHP but cannot solve the detailed sourcing problem. Finally, fuzzy set theoretic analysis does allow simultaneous treatment of precise and imprecise variables. However, fuzzy set theory is complex and it would be difficult for the users to comprehend and understand the rationale for the output results. As Huang and Keskar (2007) have noted, there appears to be something of a dichotomy between the quantitative and qualitative approaches to partner selection, which typically betrays the academic backgrounds of the researchers. On the one hand, engineering scholars, who typically operate within the OR/MS paradigm, have mostly treated partner selection as an optimization problem. On the other hand, business school scholars often emphasize philosophical issues and focus on developing qualitative principles to guide management decision making. However, strategic thinking cannot provide practical solutions. Neither will a mathematically optimization solution have any meaning if it does not match the business strategy. Consequently, effective and efficient decision-making for partner selection seems to require that approaches based on qualitative strategic thinking be combined with those of quantitative optimization. The foregoing extensive literature analysis is summarized in Table 6, which categorizes the supply partner selection process literature using the framework outlined in the Methodology of this article and depicted in Table 1 and Fig. 1. This enables the following observations to be drawn.

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

New task

Modified re-buy

1. Formulation of criteria

Lin and Chen (2004), Kannan and Haq (2007), Xia Tracey and Tan (2001), Lewis (2002), Lee et al. (2003), Lin et al. (2006), Huang and Keskar and Wu (2007), Wu and Barnes (2010), Kara (2007), Sen et al. (2008), Lee et al. (2009b), Chang (2011) et al. (2011)

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

2. Qualification Choy et al. (2004), Luo et al. (2009)

Humphreys et al. (2003), Lin and Chen (2004), Ni et 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), Guo et al. (2009), Saen (2009), Wu and Blackhurst (2009), Wu and Olson (2010), Aksoy and Ozturk (2011), Zhao and Yu (2011)

3. Final selection

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

Ghodsypour and O’Brien (2001), Talluri and Baker (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), Choi and 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 and Demirtas (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 and Yang (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), Kuo et al. (2010), Mendoza and Ventura (2010), Osman and Demirli (2010), Sevkli (2010), Sanayei et al. (2010), Amin et al. (2011), Vinodh et al. (2011), Yeh and Chuang (2011), Rezaei and Davoodi (2011), Vanteddu et al. (2011)

Ko et al. (2001), Tam and Tummala (2001), Hajidimitriou and Georgiou (2002), Mikhailov (2002), Ip et al. (2003), Cakravastia and Takahashi (2004), Onesime et al. (2004), Ding et 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), Wang and 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), Azadeh and Alem (2010), Keskin et al. (2010), Lin et al. (2010, 2011), Ravindran et al. (2010), Sawik (2010), Buyukozkan and Cici (2011), Dalalah et al. (2011), Hadi-Vencheh (2011), Liu and Zhang (2011), Yucel and Guneri (2011), Sawik (2011)

4. Application feedback

Wu and Barnes (2009, in press)

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Firstly, to date, most attention has been given to the final selection phase, the supply partner selection process. The phases that precede and follow this (i.e. criteria formulation, qualification and application feedback) have received far less attention. Although final selection is often the most visible phase in the process, its quality largely depends on the quality of the other phases. It seems clear that these phases need more attention. Secondly, in Table 6, the research works filled in new task decisionmaking situation is still much less than the research works which filled in the re-buy decision-making situation. This finding indicates that researchers have so far paid less attention to partner selection in the agile supply chain environment. However, this is the most complex and challenging of the three situations. A new task can arise due to a new market requirement, which will create a need to construct a new ASC in order to meet a new customer demand effectively and efficiently. ASCs offer new opportunities to companies operating with a growing number of participants (consumers, vendors, partners and others) in a global business environment (Crispim and de Sousa, 2010). The success of ASCs is strongly dependent on its composition, and the selection of partners therefore becomes a crucial issue. Further research is required to address this problem. Thirdly, the framework shows that not all the methods and models used in partner selection are equally useful in every possible situation. Rather they seem to be contextually specific. Yet, the existing literature seems not to adequately address this issue. The framework indicates that more consideration needs to be given to the situational characteristics in order to determine the most suitable method or model. Finally yet importantly, the combination and integrated models and approaches are summarized within Table 7 and Fig. 6. From Table 7, we can see that the most famous combined approaches are the models that include mathematical programming, AHP/ANP or

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fuzzy set approach. On the contrary, the artificial intelligence approach, including both ANN and CBR, has fewer examples to integrate with other approaches, such as AHP or ANP. As artificial intelligence approaches have the potential ability to overcome the information processing difficulties, there are plenty opportunities for further research in this area.

8. Conclusions Construction of an effective and efficient partner selection model is one of the most important issues before a partnership can be built. This research is based on a literature review of the decision-making models and approaches for partner selection from 2001 to 2011. This paper reviews these papers based on the framework used by De Boer et al. (2001), Luo et al. (2009) and Wu and Barnes (in press). Based on the extensive review, this research then presents several observations and recent trends on the development of the decisionmaking models and approaches for partner selection in ASCs. This paper also advances De Boer et al. (2001)’s groundbreaking work by considering the various combinations of methods applied and the geographic origin of supplier selection research. The foregoing analysis provides some useful information and enabled us to find some gaps in the past decade’s literature. Firstly, most of the existing research proposes decision-making models for the final selection phase but very few works consider the stages that precede or follow it. There has been no significant change to this situation over the last decade in comparison to the literature published before 2001. Further work is still needed to bridge this gap, for as many researchers have continued to argue, the decision quality of the previous stage determines the decision quality of the following stages (De Boer et al., 2001; Aissaoui et al., 2007; Luo et al., 2009;

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

DEA

Artificial Intelligence (ANN)

Mathematical Programming

AHP/ANP

Wu (2009)

Wu and Olson (2008)

Azadeh and Alem (2010), Zeydan et al. Liu and Hai (2011) (2005), Sevkli (2007), Kuo et al. (2010), Zeydan et al. (2011)

Mathematical programming

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)

Fuzzy Set

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 and Rittscher (2007a), Wang et al. (2009), Wu et al. (2010), Yeh and Chuang (2011)

Mikhailov (2002), Haq and Kannan (2006), Chan et al. (2008), Buyukozkan et al. (2008), Onut et al. (2009), Wang and 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)

Cluster analysis Artificial intelligence (CBR)

Linear weighting

Genetic Algorithms

Ha and Krishnan (2008)

Keskin et al. (2010) Faez et al. (2009)

Choy et al. (2002, 2004), Zhao and Yu (2011) Ko et al. (2001), Jarimo and Salo (2009), Ng (2008)

Amid et al. (2006)

Che (2010)

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Partner selection models and approaches Integrated approaches

Single approaches

Approaches for formulation of criteria

Approaches for final choice

Approaches for qualification

DEA+ANN or AHP/ANP

Fuzzy agility index

Data Envelopment Analysis

Linear Weighting

ANN+ CBR

0-1 Programming

Cluster Analysis

ANP/ AHP

LW + MP or Fuzzy Set

DempsterShafer theory

Categorical approaches

Fuzzy Set

MP + GA or Fuzzy Set

Artificial Intelligence

Genetic Algorithms

AHP/ANP + MP

Mathematical Programming

Cluster Analysis + AHP/ANP

Neural Network

Expert Sustem

Case-BasedReasoning

Goal Programming

Multi-objective Programming

Interger Programming

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

Wu and Barnes, in press). One of the possible ways to overcome this gap would be for more multi disciplinary research, including descriptive empirical studies. Secondly, the success of any ASCs is strongly dependent on its construction, and the selection of partners therefore becomes a crucial issue. However, very few researchers have paid attention to this special and important decision-making environment. Further research is required to address this problem as the business environment 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 review seem to have been written in the context of selecting partners for the purchase of raw materials and finished products in the manufacturing environment. More attention needs to be given to partner selection in the service operations context. Fourthly, much like De Boer et al. (2001)’s finding, there is still very little research on partner selection in public procurement. Fifthly, there is an important trend in the field of purchasing and supply management which was less prominent at the time of De Boer et al. (2001)’s work, namely electronic reverse auctions (ERA). There seems to have been a dearth of research into the impact of such practices on partner selection over the last ten years. Finally, the above summary of existing approaches to partner selection highlights the need to adopt and meet a combination of qualitative and quantitative objectives. Therefore, no single methodology is likely to be able to solve the partner selection problem, especially when different organizations have different qualitative requirements. Further research is needed to work towards developing a new more mature combination of methods and models.

Acknowledgment This work was financially 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 published 2001–2011 (identified from ISI Web of Knowledge) Aissaoui, N., Haouari, M., Hassini, E., 2007. Supplier selection and order lot sizing modeling: 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 Production Economics 104 (2), 394–407. Amid, A., Ghodsypour, S.H., O’Brien, C., 2009. A weighted additive fuzzy multiobjective model for the supplier selection problem 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 for supplier management: a case study of ISP selection and evaluation. Expert Systems with Applications 36 (4), 8639–8648. Amin, S.H., Razmi, J., Zhang, G.Q., 2011. Supplier selection and order allocation based on fuzzy SWOT analysis and fuzzy linear programming. 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 under stochastic demand. International Journal of Production Economics 117 (1), 229–233. Azadeh, A., Alem, S.M., 2010. A flexible deterministic, stochastic and fuzzy Data Envelopment Analysis approach for supply chain risk and vendor selection problem: simulation analysis. Expert Systems with Applications 37 (12), 7438–7448.

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Bai, C., Sarkis, J., 2010. Integrating sustainability into supplier selection with grey system and rough set methodologies. International Journal of Production Economics 124 (1), 252–264. Basnet, C., Leung, J.M.Y., 2005. Inventory lot-sizing with supplier selection. Computers & Operations Research 32 (1), 1–14. Baum, J.A.C., Cowan, R., Jonard, N., 2010. Network-independent partner selection and the evolution of innovation networks. Management Science 56 (11), 2094–2110. Bayrak, M.Y., Celebi, N., Taskin, H., 2007. A fuzzy approach method for supplier selection. Production Planning & Control 18 (1), 54–63. Boran, F.E., Genc, S., Kurt, M., Akay, D., 2009. A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications 36 (8), 11363–11368. Buyukozkan, G., Feyzioglu, O., Nebol, E., 2008. Selection of the strategic alliance partner in logistics value chain. International Journal of Production Economics 113 (1), 148–158. Cakravastia, A., Takahashi, K., 2004. Integrated model for supplier selection and negotiation in a make-to-order environment. International Journal of Production Research 42 (21), 4457–4474. Cao, Q., Wang, Q., 2007. Optimizing vendor selection in a twostage outsourcing process. Computers & Operations Research 34 (12), 3757–3768. Chamodrakas, I., Batis, D., Martakos, D., 2010. Supplier selection in electronic marketplaces using satisficing and fuzzy AHP. Expert Systems with Applications 37 (1), 490–498. Chan, F.T.S., 2003. Interactive selection model for supplier selection 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. International 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 Systems with Applications 38 (3), 1850–1858. Chang, B., Hung, H.F., 2010. A study of using RST to create the supplier selection model and decision-making rules. Expert Systems with Applications 37 (12), 8284–8295. Che, Z.H., 2010. A genetic algorithm-based model for solving multi-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 for supplier 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 the process incapability index. International Journal of Production Research 44 (3), 589–600. Chen, Q.X., Chen, X., Lee, W.B., 2007. Qualitative search algorithms for partner selection and task allocation in the formulation of virtual enterprise. International Journal of Computer Integrated Manufacturing 20 (2–3), 115–126. Choi, J.H., Chang, Y.S., 2006. A two-phased semantic optimization 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 for supplier selection based on a strategy-aligned fuzzy SMART approach. Expert Systems with Applications 34 (4), 2241–2253. Chou, S.Y., Shen, C.Y., Chang, Y.H., 2007. Vendor selection in a modified re-buy situation using a strategy-aligned fuzzy approach. 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 enterprise collaborative management system: a case study of supplier selection in new product development. International Journal of Technology Management 28 (2), 206–226.

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Crispim, J.A., de Sousa, J.P., 2009. Partner selection in virtual enterprises: a multi-criteria decision support approach. International Journal of Production Research 47 (17), 4791–4812. Crispim, J.A., de Sousa, J.P., 2010. Partner selection in virtual enterprises. International Journal of Production Research 48 (3), 683–707. Dalalah, D., Hayajneh, M., Batieha, F., 2011. A fuzzy multicriteria decision making model for supplier selection. Expert Systems with Applications 38 (7), 8384–8391. 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. Demirtas, E.A., Ustun, O., 2008. An integrated multiobjective decision making process for supplier selection and order allocation. Omega—International Journal of Management Science 36 (1), 76–90. Deng, S.J., Elmaghraby, W., 2005. Supplier selection via tournaments. Production and Operations Management 14 (2), 252–267. Ding, H.W., Benyoucef, L., Xie, X.L., 2005. A simulation optimization 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 in vendor selection decisions. European Journal of Operational Research 176 (1), 534–541. Faez, F., Ghodsypour, S.H., O’Brien, C., 2009. Vendor selection and order allocation using an integrated fuzzy case-based reasoning and mathematical programming model. International Journal of Production Economics 121 (2), 395–408. Feng, B., Fan, Z.P., Ma, J., 2010. A method for partner selection of codevelopment alliances using individual and collaborative utilities. International Journal of Production Economics 124 (1), 159–170. Ghodsypour, S.H., O’Brien, C., 2001. The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint. International Journal of Production Economics 73 (1), 15–27. Glickman, T.S., White, S.C., 2008. Optimal vendor selection in a multiproduct supply chain with truckload discounts. Transportation Research, Part E—Logistics and Transportation Review 44 (5), 684–695. Guneri, A.F., Kuzu, A., 2009. Supplier selection by using a fuzzy approach 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-lp approach for a supplier selection problem in supply chain management. Expert Systems with Applications 36 (5), 9223–9228. Guo, X.S., Yuan, Z.P., Tian, B.J., 2009. Supplier selection based on hierarchical potential support vector machine. Expert Systems with Applications 36 (3), 6978–6985. Ha, S.H., Krishnan, R., 2008. A hybrid approach to supplier selection 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 multiple criteria supplier-selection problem. International Journal of Computer Integrated Manufacturing 24 (1), 32–39. Hajidimitriou, Y.A., Georgiou, A.C., 2002. A goal programming model for partner selection 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 environment. International Journal of Production Research 44 (10), 1963–1985. Ho, W., Xu, X.W., Dey, P.K., 2010. Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. European Journal of Operational Research 202 (1), 16–24.

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Wu, W.Y., Shih, H.A., Chan, H.C., 2009. The analytic network process for partner selection criteria in strategic alliances. Expert Systems with Applications 36 (3), 4646–4653. Wu, W.Y., Sukoco, B.M., Li, C.Y., Chen, S.H., 2009. An integrated multi-objective decision-making process for supplier selection with 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-valued intuitionistic fuzzy numbers for virtual enterprise partner selection. Expert Systems with Applications 37 (10), 7050–7055. Ye, F., Li, Y.N., 2009. Group multi-attribute decision model to partner selection in the formation of virtual enterprise under incomplete information. Expert Systems with Applications 36 (5), 9350–9357. Yeh, W.C., Chuang, M.C., 2011. Using multi-objective genetic algorithm 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. Supplier selection: an expert system approach. Production Planning & Control 18 (1), 16–24. Yucel, A., Guneri, A.F., 2011. A weighted additive fuzzy programming approach for multi-criteria supplier selection. Expert Systems with Applications 38 (5), 6281–6286. Zeydan, M., Colpan, C., Cobanoglu, C., 2011. A combined methodology 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 novel approach to supplier selection based on vague sets group decision. Expert Systems with Applications 36 (5), 9557–9563. Zhang, J.L., Zhang, M.Y., 2011. Supplier selection and purchase problem with fixed cost and constrained order quantities under stochastic demand. International Journal of Production Economics 129 (1), 1–7. Zhao, K., Yu, X., 2011. A case based reasoning approach on supplier selection in petroleum enterprises. Expert Systems with Applications 38 (6), 6839–6847. Zolghadri, M., Amrani, A., Zouggar, S., Girard, P., 2011. Power assessment as a high-level partner selection criterion for new product development projects. International Journal of Computer Integrated Manufacturing 24 (4), 312–327.

Appendix 2. Articles on supply partner selection published in the Journal of Purchasing and Supply Management 2001–2011 (identified from ScienceDirect) Bevilacqua, M., Ciarapica, F.E., et al., 2006. A fuzzy-QFD approach to supplier selection. Journal of Purchasing and Supply Management 12(1), 14–27. Dabhilkar, M., Bengtsson, L., et al. 2009. Supplier selection or collaboration? Determining factors of performance improvement when outsourcing manufacturing. Journal of Purchasing and Supply Management 15(3), 143–153. de Boer, L., Labro, E., et al., 2001. A review of methods supporting supplier selection. European Journal of Purchasing & Supply Management 7(2), 75–89. de Boer, L., van der Wegen, L.L.M., 2003. Practice and promise of 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 multicriteria decision aid method. Journal of Purchasing and Supply Management 9(4), 177–187. Humphreys, P., Huang, G., et al., 2007. Integrating design metrics 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 selection and relationship practices: a contagion process. Journal of Purchasing and Supply Management 10(2), 55–64. Luo, X., Wu, C. et al., 2009. Supplier selection in agile supply chains: 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-efficiency-based supplier selection in the EPC industry. Journal of Purchasing and Supply Management 15(3), 166–177.

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