International Journal of Production Research 2011, 1–13, iFirst
Supplier selection problem revisited from the perspective of product configuration Duck Young Kima and Stephan M. Wagnerb* a
School of Design and Human Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea; Department of Management, Technology, and Economics, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
b
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(Received 16 June 2010; final version received 31 January 2011) Collaboration with suppliers is essential for developing and producing a competitive product and it begins with selecting the right suppliers. This article addresses the supplier selection problem from the perspective of product configuration. We discuss first how the conventional decision models of supplier selection should be extended, considering the fact that supplier configuration requires supplier selection for each subsystem of a product. We then highlight the need to consider interrelationships between suppliers, namely, supplier– supplier relationships during the selection process of the combination of suppliers. Finally, we introduce the supplier configuration graph in order to provide a clear picture of the problem. Discussions are provided along with an example of a smartphone configuration. Keywords: supply chain management; supplier selection; product configuration; supplier configuration; multiple criteria decision analysis
1. Introduction Supplier selection, in general, can be seen as a decision-making problem in which it is necessary to select the best supplier(s) from a pre-defined set with respect to decision criteria as shown in Figure 1. The generic procedure of decision-making consists of (1) attention (demand); (2) setting decision criteria; (3) finding or designing suitable alternatives; and (4) evaluating and choosing alternatives (Simon et al. 1987). If a decision should be made with respect to multiple criteria, it is necessary to aggregate the set of mono-criterion preferences to the global preference for the final decision considering trade-offs among criteria, that is, ‘Multiple Criteria Decision Analysis’ (MCDA) has to be performed. Supplier selection decision requires the simultaneous consideration of multiple criteria such as quality, price, delivery, technology, and service (Weber et al. 1991); not surprisingly, several MCDA methods have been applied to supplier selection (De Boer et al. 2001). However, it is interesting to revisit supplier selection problems considering the importance of suppliers’ roles in new product development because new challenges in the global market such as shorter product life cycles, rapid technology change, and rising R&D cost compel the buyer (e.g. mobile phone manufacturer) to collaborate with suppliers (e.g. chip manufacturers) from the early phase of product development, and it begins with the correct choice of suppliers (Wagner 2010). In order to develop and produce a product, decisions must be made about who will design the subsystems and who will produce and test them (Krishnan and Ulrich 2001). In other words, there is no doubt that developing competitive products that maximise customer value is a key factor for the success of manufacturing firms. It is not only creating something new (creative, innovative design), but also modifying or rearranging something that pre-exist(s) for improvement (adaptive, selection design) (Kim and Xirouchakis 2008). In particular, the performance of electronic products such as mobile phones and computers is highly dependent on the quality of each subsystem (in this article, we use ‘subsystems’ as the most general term for all sub-assemblies, modules, components, and parts) which will be developed and provided (mainly by suppliers) while there is little change in the list of necessary subsystems comprising a product. Therefore, finding high-quality subsystems and integrating those subsystems/sub-assemblies into larger assemblies/the final product should be critical not only in product configuration but also in supplier selection. In light of the importance of suppliers in new product development, this article addresses supplier selection for each subsystem of a product. First, Section 2 reviews various MCDA-based supplier selection methods, then Section 3 discusses how the conventional decision model of supplier selection in Figure 1 will be extended, along *Corresponding author. Email:
[email protected] ISSN 0020–7543 print/ISSN 1366–588X online ß 2011 Taylor & Francis DOI: 10.1080/00207543.2011.561372 http://www.informaworld.com
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D.Y. Kim and S.M. Wagner S* Aggregate the set of mono-criterion preferences to the global preference in order to rank order the set of suppliers, and finally * select the best supplier, S , given: • a set of M suppliers • a set of decision criteria: quality, price, delivery, technology, service, etc. • preferences for all suppliers on the decision criteria
S1 S2 S3
SM
S: supplier
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Figure 1. Conventional supplier-selection decision.
Table 1. Typical evaluation matrix for supplier selection. Decision criteria 1: quality
2: price
Supplier
W1
W2
S1 S .. 2 . SN
P11 P.21 .. PN1
P12 P.22 .. PN2
...
... . .. . .. ...
C: service WC
Aggregated preference
P1C P2C .. . PNC
A1 A. 2 .. AN
A, aggregated preference for a supplier; C, number of criteria; N, number of suppliers; P, performance or preference; S, supplier; W, weight.
with the example of a smartphone configuration. Section 4 highlights the importance of considering interrelationships between suppliers, namely, supplier–supplier relationships during the selection process of the combination of suppliers, and then introduces a supplier configuration graph in order to provide a clear picture of the problem. Concluding remarks are provided in Section 5
2. Literature review 2.1 MCDA-based supplier selection The recognition of supplier selection as MCDA allows us to benefit from solid theories and methodologies made in Decision Analysis and Operations Research. Over the past decades a considerable number of quantitative approaches to supplier selection have been developed based on various MCDA methods. Table 1 shows the typical evaluation matrix for the case of a discrete and finite number of alternatives with multiple criteria. The main elements of various MCDA based supplier selection methods are: (1) a set of suppliers; (2) a set of decision criteria; (3) weights representing the relative importance of the decision criteria or trade-offs among the criteria; (4) performances (or preferences representing the performances) of suppliers on the decision criteria; and (5) an aggregation method of those preferences. The final choice will be made according to the aggregated preferences. In Multi-Attribute Utility/Value Theory (MAUT/MAVT), preferences are represented by either preference functions under uncertainty (utility functions) or ones under certainty (value functions) that map the levels of performances into decision-maker’s preferences (Keeney and Raiffa 1976, Dyer 2005). These preference functions were developed by some form of interactive communication (e.g. lottery-type questions) between the decision
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analyst (or computer software program) and the decision-maker. This preference function assessment procedure is a matter of human value judgement and is a bothersome process. The identification of a suitable form of utility function (e.g. exponential, linear, quadratic, etc.) with respect to the decision-maker’s attitude to risk (e.g. risk aversion, neutral, proneness) and the appropriate parameter setting for the function are the prerequisites for applying the preference aggregation function (e.g. general additive form) of MAUT to supplier selection. An application of MAUT to supplier selection can be found in Min (1993). Before we choose a certain method of MCDA for supplier selection, it is important to pose the question: Is it the goal to seek a truly scientific decision-making method which can replace the decision-maker, or is it to look for a decision aid method which gives the decision-maker important advice and helps them make consistent decisions (Roy 1990)? For the latter case, various outranking methods are applicable to supplier selection such as ELECTRE (ELimination Et Choix Traduisant la REalite´) (De Boer et al. 1998, Sevkli 2010) and PROMETHEE (Preference Ranking Organization METHod For Enrichment Evaluations) type (Dulmin and Mininno 2003). Note that, in outranking methods, binary outranking relations between alternatives (e.g. supplier A is at least as good as supplier B) are built with respect to the performance levels of criteria and the decision parameters (e.g. preference, indifference, and veto thresholds in ELECTRE III) that must be pre-defined by a decision-maker. It is sometimes reasonable to assume that preference has a monotonic relation with some real-valued attribute performance ‘x’ such as product price and manufacturing cost, therefore, we can consider ‘x’ as the value function v(x), that is, v(x) ¼ x (Dyer 2005). In this case, several practical methods have been widely used in the literature such as simple additive weighting (Timmerman 1986) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) (Li et al. 2008). The principle of the Total Cost of Ownership (TCO) based approaches (Bhutta and Huq 2002, Garfamy 2006) can be seen as an effort to express all preferences or performance scores on different criteria in a single commensurable unit, namely cost. Note that it is not always possible to properly and rationally formulate the overall objective function having a single synthesising criterion such as economic benefit or total cost while considering numerous conflicting and non-commensurable criteria such as quality, price, delivery, and technology. One of the most important issues in MCDA is the allocation of the weights specifying the relative power of each criterion. Several weights elicitation procedures have been presented in the literature, for example, trade-off analysis in MAUT (Keeney and Raiffa 1976), ratio estimation in SMART (Simple Multi Attribute Rating Technique) (Edwards 1977), pairwise comparisons of the importance of criteria in Analytic Hierarchy Process (AHP) (Saaty 1980), and revised Simos’s procedure in the ELECTRE type methods (Figueira and Roy 2002). In the supplier selection literature, AHP has been popularly applied to determining the weights (e.g. Ghodsypour and O’Brien 1998, Bhutta and Huq 2002). DEA (Data Envelopment Analysis) has been applied to evaluating the overall performances of suppliers without a priori information about the weights on criteria because it determines the optimal set of weights for each supplier so as to maximise the efficiency of the supplier (e.g. Weber 1996, Liu et al. 2000, Figueira and Roy 2002, Seydel 2005, Li et al. 2008, Wu and Blackhurst 2009). However, this flexibility in defining the weights raises the question as to the direct applicability of DEA to ranking suppliers on a common base. Ng (2008) proposed a weight restriction model of DEA (ranking of relative importance of criteria) for supplier selection. Wu and Blackhurst (2009) developed and augmented DEA to enhance discriminatory power in ranking of suppliers and to consider weight constraints that can reduce the possibility of having inappropriate input and output factor weights. Further discussion about DEA as a ranking method can be found in Adler et al. (2002). These methods have been often incorporated with mathematical programming methods such as linear programming and multi-objective mathematical programming (e.g. goal programming) in order to optimise the order quantity allocation for each supplier considering the constraints on the production capacities of suppliers, especially under the multiple sourcing strategy (Ghodsypour and O’Brien 1998, Wang et al. 2004).
2.2 Supplier selection in new product development It has been found that selecting the right suppliers and integrating them into new product development are very important to improve the quality of subsystems, reduce product development time, and improve access to and application of new technology (Krishnan and Ulrich 2001, Hou et al. 2006, Wagner 2010). Several research efforts therefore have been made to incorporate supplier selection with product configuration based on the classical optimisation formulation. Wang and Che (2007) proposed a genetic algorithm-based search method to determine the best combination of subsystem suppliers considering product configuration changes. A simple linear
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D.Y. Kim and S.M. Wagner
combination of subsystems’ prices, quality values in the cost-unit, and necessary assembly costs was formulated as the single synthesising objective function. Balakrishnan and Chakravarty (2008) considered the supplier selection issue concurrently with the new product model choice issue. By using the estimated demand and sales quantities of product models, the total cost–benefit formulation for the objective function was considered. Wang et al. (2004) discussed the issue of supplier selection for each subsystem. They applied AHP to supplier ratings and a goal programming method to order quantity allocation. However, supplier selection decision for each subsystem seems to be made independently without considering the interrelationship between suppliers. Various MCDA methods have been applied to supplier selection (Section 2.1), and many researchers have pointed out the importance of suppliers in new product development (Section 2.2). However, little attention has been given to the problem definition for early supplier selection in the MCDA context. This issue will be further discussed in this article.
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3. Supplier configuration 3.1 Smartphone configuration In general, a product consists of various subsystems as shown in Table 2. Once design teams in a firm have defined the architecture (e.g. functional structure and layout) of the subsystem configuration after the preliminary design stage, the firm must decide whether they will purchase subsystems from several suppliers or develop them in-house. This requires a selection process of the best suppliers for the required subsystems as well as coordination of the entire project by providing required functionalities for new subsystems to the suppliers. In the electronic industry, it is very common that the final system assembly is also outsourced to an Electronic Manufacturing Services (EMS) provider. Table 2 lists the main subsystems of an existing smartphone model in the market and potential suppliers for each subsystem. Other subsystems, whose functions are relatively independent from the main subsystems such as charger/USB controller, mechanical parts, stainless casing, connectors, and cables are not included in Table 2 for simplicity. It is obvious that the smartphone project team in the firm must select the best supplier(s) for each subsystem with respect to decision criteria such as quality, price, delivery, technology, and service. Let us call this problem supplier configuration. Figure 2 illustrates three possible decision models for supplier configuration (M subsystems and N suppliers for each subsystem) in the MCDA context: (1) supplier selection from the single cluster of suppliers; (2) supplier selection for each subsystem independently; (3) selection of the combination of suppliers for all subsystems. In the first model, using an MCDA method, the project team will evaluate and rank order the M N suppliers as a whole regardless of subsystems. It will then select the best supplier corresponding to each subsystem, eventually resulting in M suppliers. In the second model, the project team will evaluate the suppliers for each subsystem independently. That is, there are M independent MCDA problems in hand. In the third model, in order to take into account interrelationships between suppliers and product configuration issues, the project team will evaluate (N)M combinations of suppliers. For example, the project team must not only evaluate the compatibility (e.g. technical and economic compatibility and firms’ cultural conformity) of Infineon’s baseband processor and Samsung’s application processor but also find the best combination of suppliers that maximise synergy. The authors propose the third model as the most convincing for supplier configuration. In Table 2, there are 119 potential supplier firms for the 18 major subsystems. Given that some suppliers could deliver several subsystems, a total of 165 subsystem suppliers need to be considered, theoretically resulting in 19,664,496,230,400,000 combinations of suppliers (obtained by the product of the numbers of potential suppliers for all subsystems) are possible. The following questions immediately come to mind: Should we evaluate the entire set of combinations? Do we have decision criteria for evaluating a combination of suppliers?
3.2 Pre-qualification In order to narrow down the huge number of potential combinations of suppliers to a manageable size as discussed in De Boer et al. (2001), a pre-qualification process (a sort of screening) is essential to filter out a set of unsuitable
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International Journal of Production Research Table 2. Smartphone subsystems and potential suppliers. Subsystem Baseband processor (platform) Application processor Storage (NAND, DDR, NOR) Touch screen controller LCD Power management RF transceiver
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Camera module Battery Wi-Fi chipset Bluetooth chipset GPS chipset Motion sensor/accelerometer Power amplifier Audio PCB Operating system Assembly
Supplier
Count
Infineon, Qualcomm, Texas Instruments, EMP (Ericsson Mobile Platforms), Broadcom, Freescale, NXP, MediaTek, Sunplus mMobile, T3G, Spreadtrum, Commit Samsung, Texas Instruments, STMicroelectronics, Toshiba, Nvidia, AMD/ ATI, Renesas, Qualcomm, Marvell, Freescale, Mtekvision, Broadcom Samsung, Intel, Micron, Toshiba, Infineon, Spansion, Hynix, Sharp, SanDisk, STMicroelectronics, Renesas, Kingston, NEC, ProMos, Powerchip, Nanya, Elpida, Macronix, Etron, SST, ELPIDA, Etron, Qimonda, Winbond Balda, TPK, Optera, Synaptics, Cypress, EMC, ADI Sanyo-Epson, Sharp, TMDisplay, Samsung, CDT, LG-Philips LCD, Seiko Instruments, AU Optronics Infineon, NXP, Analog Devices, Enpirion, Fairchild Semiconductor, Freescale, LTRIM, Microchip, NSC, Philips, Qualcomm, Zilker Labs Infineon, Skyworks, STMicroelectronics, Silicon Lab, RFMD, Texas Instruments, Philips, Freescale, Qualcomm, TriQuint, Avago, Anadigics, Beijing LHWT Microelectronics, Epicom Primax, Altus, Premier, Chicony, Lite-On Semiconductor, Creative Sensor, Asia Optical, Macat, Cowell World Optech, OmniVision Siemens, NEC, Motorola, Alcatel, Panasonic, Samsung, O2, Sharp, Philips, Sony, Nokia, LG Marvell, Broadcom, CSR, Atheros Communications, Texas Instruments CSR, Broadcom, Texas Instruments, NXP, Infineon, STMicroelectronics Broadcom, CSR, CellGuide, eRide, NemeriX, Seiko Epson Corporation, SiRF, SkyTraq, u-blox, u-Nav Microelectronics STMicroelectronics, Analog Devices, Freescale Skyworks, Motorola, Wilson, TriQuint, Samsung, Digital Antenna, AxiomMicrodevices Wolfson, Texas Instruments, AKM, Cirrus Unimicron, Tripod, Nanya PCB, Compeq Manufacturing Apple OS X, Nokia Symbian OS, Microsoft Windows Mobile, Google Android, Linux Flextronics, Foxconn International Holdings, Elcoteq, Chi Mei, Quanta, Solectron, Compal Communications, Celestica, Sanmina-SCI, BenQ
12
Total
12 24 7 8 12 14 10 12 5 6 10 3 7 4 4 5 10 165
Source: iSuppli.com, Semiconductor insight, techonline.com.
suppliers from the original set of potential suppliers. In general, this pre-qualification process must be performed carefully in order not to eliminate any valuable supplier that is worth investigating. For this reason, the prequalification process should be based on the assured information only such as dominant/non-dominant (Pareto optimality), efficient/non-efficient (DEA), and yes/no with respect to the pre-specified aspiration levels of criteria (constraints). Any evidence that a certain pair of suppliers is incompatible (e.g. firm A’s application processor is not compatible with firm B’s audio codec; firm C does not want to collaborate with firm D) will drastically reduce the number of suitable combinations of suppliers (Singhal and Singhal 2002).
3.3 Selection criteria There is little doubt that it is necessary to revise decision criteria to be employed for supplier selection and their relative importance considering firms’ differing outsourcing strategies, levels of supplier involvement, and business environments (Sen et al. 2008). For example, for early supplier selection, more attention should be paid to responsiveness to changing requirements, trust and reliability, technological capability, and compatibility with other suppliers and their systems, as well as conventional decision criteria such as quality and price (Monczka et al. 2000,
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D.Y. Kim and S.M. Wagner S* (a) S 11
S 21
SM1
S 12
S 22
SM2
S 13
S 23
SM3
S 1N
S 2N
S MN
Supplier selection from the single cluster of suppliers
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(b)
∗ S1
S 2∗
∗ SM
S11
S21
SM1
S12
S22
SM2
S13
S23
SM3
S1N
S 2N
SMN
Supplier selection for each subsystem independently S∗ ← S1∗ × S∗2 × L × S∗M (c)
S11
S 21
S M1
S12
S 22
S M2
S13
S 23
S M3
S1N
S 2N
S MN
Selection of the combination of suppliers for all subsystems S: supplier, M: number of subsystems N: number of suppliers for each subsystem (the same number N for all subsystems for simplifying notation)
Figure 2. Decision models of supplier configuration.
Wagner and Hoegl 2006, Wagner 2010). Furthermore, decision criteria and relevant constraints can be inherited not solely from the buyer–supplier relationship (Jain et al. 2009) but also from the supplier–supplier relationships (Choi et al. 2002), especially in evaluating the performances of combinations of suppliers. It is worth noting here that considering the early phase of product development, order quantity allocation might be difficult at this point, but the other criteria such as production capability and flexibility to changing order quantities can be included in decision criteria indirectly as a precondition.
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In addition, the implication of certain criteria can be re-interpreted, which can be typified by the versatility of the supplier (e.g. there are some multiple nominations of suppliers such as Samsung and Infineon in Table 2). In the conventional supplier selection, more preference is given to a supplier who can develop and produce a larger number of subsystems in order to minimise the number of suppliers in the final result. However, it is difficult for a smartphone manufacturer to solicit giant electronics and semi-conductor companies such as Samsung and Infineon to develop all subsystems, even supposing that those companies could, because then they would heavily depend on these large companies. Therefore, it is reasonable to say that priority should go to a supplier who has the ability to improve, develop and produce high-quality subsystem variants (e.g. 40/60/70 nm – 32/16/8 Gb NAND flash memories).
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4. Supplier–supplier relationships and supplier configuration graph 4.1 Supplier–supplier relationships Most MCDA methods (selection of an alternative from a set of alternatives) do not fit naturally into supplier configuration (selection of a set of alternatives from a set of sets of alternatives, see decision model (c) in Figure 2) addressed in the previous section. In particular, when those alternatives are interdependent, selection is not straightforward because some alternatives may be incompatible with each other in a set of alternatives (De Boer et al. 1998). This section therefore highlights the importance of considering interdependencies between suppliers, namely supplier–supplier relationships (Choi et al. 2002, Dulmin and Mininno 2003) by showing the different results when applying DEA, which is the most popular approach for supplier selection (Min 1993, Seydel 2005, Li et al. 2008, Ho et al. 2010), to the decision model (b) respectively (c) in Figure 2. Note that the main purpose of this demonstration is to explain how supplier configuration is different from the conventional supplier selection by means of typical DEA assessments, rather than to propose a certain MCDA method as the most appropriate tool for supplier configuration. DEA (Charnes et al. 1978) measures the relative efficiency of a Decision Making Unit (DMU) – suppliers in our case – by the ratio of the sum of weighted outputs to the sum of weighted inputs without knowing the DMU’s endogenous system structure (production function). Naturally, in order to optimise the efficiency in converting inputs into outputs, the sum of weighted inputs should be minimised, or else the sum of weighted outputs should be maximised. In the applications of DEA for MCDA, therefore, a set of criteria to be minimised (such as price and delivery) is considered as input, while one to be maximised such as quality and technology is considered as output in general (Stewart 1996). This efficiency concept (output maximisation) is formulated as the following linear model (Charnes et al. 1978): max zo ¼
J X
vj yjo
j¼1
s:t:
I X
ui xio ¼ 1
ð1Þ
i¼1 J X j¼1
vj yjn
I X
ui xin 0,
8n ¼ 1, . . . , N
i¼1
ui , vj " 4 0,
8i, j,
where the oth DMU is being evaluated among N DMUs; xin, yjn are the given inputs and outputs of the nth DMU; ui, vj are the variables to be determined for the weights on xi, yj respectively. Andersen and Petersen (1993) have proposed the super-efficiency model in order to allow DMU’s efficiency score to be greater than 1. This can be achieved simply by removing the oth DMU from the second constraint in Equation (1), i.e. 8n ¼ 1, . . . , N, n 6¼ o. The feasibility issue of various DEA extensions as a ranking method is beyond the scope of this article, but the interested readers can find more discussion in Bouyssou (1999) and Adler et al. (2002). Let us return to the smartphone project. For simplicity, only two subsystems, baseband and application processors, are under consideration, and it is assumed that only the top five suppliers for each subsystem resulting from the pre-qualification process as discussed in Section 3.2 are examined in the DEA assessments.
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D.Y. Kim and S.M. Wagner Table 3. Efficiencies of the suppliers for baseband processor. Inputs Baseband processor A B C D E
Infineon Qualcomm Texas Instruments EMP Broadcom
Outputs
BI1
BI2
BO1
BO2
Efficiency
Super-efficiency
5 6 7 3 6
15 25 20 15 20
9 8 8 6 6
7 3 7 7 5
1.000 0.696 0.750 1.000 0.545
1.500 0.696 0.750 1.667* 0.545
Note: *Highest super-efficiency score.
Table 4. Efficiencies of the suppliers for application processor. Inputs
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Application processor a b c d e
Samsung Texas Instruments STMicroelectronics Toshiba Nvidia
Outputs
AI1
AI2
AO1
AO2
Efficiency
Super-efficiency
5 6 6 5 6
13 22 20 18 25
8 6 8 8 6
7 5 5 7 8
1.000 0.833 0.833 1.000 0.952
1.795* 0.833 0.833 1.000 0.952
Note: *Highest super-efficiency score.
In the conventional supplier selection under the typical MCDA settings, it is obvious that the decision is made with respect to a finite set of decision criteria. However, considering the decision models in Figure 2, it is not necessary to assume that the decision criteria for all subsystems are common. For example, the decision criteria and their closely related sub-attributes in evaluating the performances of suppliers for baseband processors can be different from those for the other subsystems. In this regard, the inputs and outputs of baseband processors, BI1, BI2, BO1, and BO2 can be different from those for application processors, AI1, AI2, AO1, and AO2 in Tables 3 and 4. First, Tables 3 and 4 show the efficiency scores of the suppliers for baseband processors and application processors, respectively. Following the second decision model in Figure 2: (b) supplier selection for each subsystem independently, the DEA results (based on the super-efficiency scores) reveal that EMP and Samsung are the best suppliers for the two subsystems. However, as shown in Table 5, if we evaluate the performances of the combinations of two subsystems’ suppliers following the third decision model in Figure 2: (c) selection of the combination of suppliers for all subsystems, then different results are obtained, such that Infineon and Samsung are the best combination of suppliers, while EMP and Samsung are the second-ranked combination. It is reasonable to assume that after a strong pre-qualification process, only efficient suppliers (Infineon and EMP in Table 3 and Samsung and Toshiba in Table 4) are taken into account to evaluate the efficiency of combinations of suppliers as discussed in Section 3.2. Table 6 shows the efficiencies of the combinations of suppliers after a strong pre-qualification, and the results still confirm that it is necessary to evaluate the performance of combinations of suppliers for selecting the right supplier regardless of the number of pre-qualified suppliers. There is still room for further investigation about whether it is reasonable to evaluate the performance of a combination of suppliers by simply including the respective inputs and outputs of the suppliers in a single DEA model (Table 5). First, if we apply Kao’s DEA model for parallel production systems (Kao 2009) to supplier configuration, then the performance of a combination of two suppliers is simply the sum of two suppliers’ performances, (i.e. the sum of input and output values). In other words, if suppliers for baseband and application processors are homogeneous, similar to parallel machines, and the inputs and outputs of baseband processor, BI1, BI2, BO1, and BO2 are the same with application processor, AI1, AI2, AO1, and AO2 in Tables 3 and 4 (i.e. AI1 ¼ BI1, AI2 ¼ BI2, AO1 ¼ BO1, and AO2 ¼ BO2), then the efficiency of combination of suppliers can be
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International Journal of Production Research Table 5. Efficiencies of the combinations of suppliers for baseband and application processors.
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Baseband and application processors A A A A A B B B B B C C C C C D D D D D E E E E E
a b c d e a b c d e a b c d e a b c d e a b c d e
Inputs
Outputs
AI1
AI2
BI1
BI2
AO1
AO2
BO1
BO2
Efficiency
Super-efficiency
5 5 5 5 5 6 6 6 6 6 7 7 7 7 7 3 3 3 3 3 6 6 6 6 6
15 15 15 15 15 25 25 25 25 25 20 20 20 20 20 15 15 15 15 15 20 20 20 20 20
5 6 6 5 6 5 6 6 5 6 5 6 6 5 6 5 6 6 5 6 5 6 6 5 6
13 22 20 18 25 13 22 20 18 25 13 22 20 18 25 13 22 20 18 25 13 22 20 18 25
9 9 9 9 9 8 8 8 8 8 8 8 8 8 8 6 6 6 6 6 6 6 6 6 6
7 7 7 7 7 3 3 3 3 3 7 7 7 7 7 7 7 7 7 7 5 5 5 5 5
8 6 8 8 6 8 6 8 8 6 8 6 8 8 6 8 6 8 8 6 8 6 8 8 6
7 5 5 7 8 7 5 5 7 8 7 5 5 7 8 7 5 5 7 8 7 5 5 7 8
1.000 1.000 1.000 1.000 1.000 1.000 0.741 0.833 1.000 0.952 1.000 0.833 0.833 1.000 0.952 1.000 1.000 1.000 1.000 1.000 1.000 0.625 0.833 1.000 0.952
1.261* 1.000 1.000 1.000 1.100 1.000 0.741 0.833 1.000 0.952 1.000 0.833 0.833 1.000 0.952 1.244** 1.000 1.000 1.000 1.143 1.000 0.625 0.833 1.000 0.952
Notes: *Highest super-efficiency score, **second highest super-efficiency score.
calculated simply considering the sum of two input values and the sum of two output values in Table 5. However, the fundamental assumption in this case is that the sets of inputs and outputs of suppliers are common across homogeneous subsystems, which is not the case in supplier configuration and thus Kao’s model is not suitable for supplier configuration in the real world. Second, if we have more explicit information of the internal structure of the combinations of suppliers (e.g. a certain output of a subsystem may be used as an input of another subsystem), then the efficiency evaluation model of interdependent sub-units (Castelli et al. 2001) can be considered. However, citing the notion of DEA, i.e. DMU’s production function (i.e. efficiency of a set of suppliers including their interrelationships), as a ‘black box’, this study uses the classical DEA efficiency and super-efficiency models, but this issue is still open to discussion. Despite the necessity of further investigation, the simple DEA results lead us to argue that the consideration of product configuration issues and accordingly the interrelationships between suppliers could play a significant role in supplier configuration.
4.2 Supplier configuration graph A clear picture of supplier configuration can be reached by borrowing a formal definition from generalised network design problems. The generalised minimum clique problem, according to Feremans et al. (2003, p. 7), ‘consists of finding a subset of vertices containing exactly one vertex from each cluster such that the cost of the induced subgraph (the cost of the selected vertices plus the cost of the edges in the induced subgraph) is minimised’. Generalised network design problems transferred to supplier configuration is depicted in Figure 3. Here, each cluster implies a subsystem or a business function such as logistics (provider) in which a finite set of suppliers or strategy choices exists. Accordingly, vertices denote suppliers or strategy choices, costs on vertices connote aggregated preferences for suppliers (transformed reciprocally into costs), and costs on edges imply quantified values of interrelationships between suppliers. As a result, the task of supplier configuration is to find a combination
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D.Y. Kim and S.M. Wagner Table 6. Efficiencies of the combinations of suppliers for baseband and application processors after a strong pre-qualification. Inputs
Baseband and application processors A A D D
a d a d
Outputs
AI1
AI2
BI1
BI2
AO1
AO2
BO1
BO2
Efficiency
Super-efficiency
5 5 3 3
15 15 15 15
5 5 5 5
13 18 13 18
9 9 6 6
7 7 7 7
8 8 8 8
7 7 7 7
1.000 1.000 1.000 1.000
1.385* 1.000 1.244 1.000
Note: *Highest super-efficiency score.
(a)
(b)
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15
20
7
3
2
17
Single sourcing
Multiple sourcing
The cluster implies a subsystem or a business function such as logistics
Supplier or business strategy alternative
Costs on vertices: aggregated preferences for suppliers Costs on edges: interrelationships between suppliers
Figure 3. Supplier-configuration graph.
of suppliers containing exactly one supplier from each cluster such that the cost of the combination is minimised. Under the multiple sourcing strategies, ‘exactly one’ will be replaced by ‘at least one’. The supplier configuration graph above provides important research directions for quantitative approaches to supplier selection.
4.2.1 Construction of the input information for MCDA methods Cost (or score) on a vertex in the supplier configuration graph implies the aggregated preference for a supplier by means of a certain MDCA method. In the supplier selection literature, valuable studies have been made on determining the important multiple supplier selection criteria, and accordingly, most MCDA-based supplier selection studies have given details on how to apply the existing MCDA methods (e.g. MAUT, AHP, and DEA) to supplier selection, focusing on how to aggregate the set of mono-criterion preferences to the global preference with appropriate weight allocations. The decision of choosing an appropriate MCDA method seems to rely on the familiarity of the method rather than its suitability to supplier selection (Guitouni and Martel 1998). Furthermore, it is usually assumed that the input information for those MCDA methods as shown in Table 1 is given a priori, and apparently supplier selection problems are regarded as good case studies of the existing MCDA methods.
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As a prerequisite for the valuable studies in MCDA-based supplier selection, a future research direction will be how to construct the required input information, that is, the evaluation matrix in Table 1. To elicit preferences in practice, it must be specified first how to measure the performance level of each criterion and corresponding sub-attributes. In particular, it is very often the case that performances are represented only by qualitative data rather than clear quantitative value. In this case, the integrated decision model proposed by Kuo et al. (2010) will be very useful to take both quantitative and qualitative evaluation data into account for supplier selection, as this model can provide a practical weight determination process in the development of a supplier configuration graph. Next, suitable preference functions should be defined which map performance levels with decision-makers’ preferences (the so-called explicit preference function). Preferences can be directly expressed by a decision-maker without defining an explicit preference function (the so-called implicit preference function). It is important to note that utility or value (i.e. preference) is a way of representing a decision-maker’s satisfaction, happiness, or desires in MCDA, and thus preference elicitation (or preference function definition) is the main task that requires human value judgement together with weight allocations. Therefore, the objective measurement of performance levels of criteria and the subjective preference elicitation should be discriminated. For example, the performance of cost, a penny, might be less critical for a giant electronics company than for a small venture company. Once the evaluation matrix is constructed, the selection of an appropriate MCDA method (Guitouni and Martel 1998) will follow, considering the quality of necessary decisions (e.g. complete ranking) and the characteristics of input information such as quantitative/qualitative, ordinal/cardinal, commensurability and independency of criteria, deterministic/non-deterministic (uncertainty), and single decision-maker/group decisionmakers.
4.2.2 Interrelationship in supplier configuration Costs on edges in the supplier configuration graph connote interrelationships between suppliers and business functions. From a strategic point of view, the relationship between suppliers within a cluster tends to be very competitive, but suppliers across clusters should be cooperative (Choi et al. 2002). It is difficult to manipulate the qualitative nature of relationships (e.g. in some form of linguistic expressions relying upon perception-based judgement). Nevertheless, for example, technical and economic compatibility and firms’ cultural conformity can be quantified (e.g. fuzzy relation) which is not necessary to be a good or bad dichotomy. Supplier configuration is further compounded by the conditional nature of costs on vertices and edges. For example, the combination of suppliers A and B can exert a positive or negative influence on the performance of supplier C, so that the costs on vertices and edges are no longer static but they can be changed dynamically. Therefore, how to stabilize these data, or how to deal with the dynamics in supplier configuration will be an important research direction.
5. Concluding remarks We have discussed the supplier selection problem from the perspective of product configuration, namely supplier configuration, taking the smartphone configuration example, and elucidated the demarcating features of supplier configuration from the conventional MCDA based supplier selection models. The discussions in this article were not confined to a particular MCDA method such as DEA, but rather covered a novel viewpoint for the problem definition of supplier selection. The supplier configuration graph in Figure 3 provides a visual snapshot of the supplier configuration landscape although it does not directly explain how to measure the performance level of criteria and how to develop an appropriate preference function, which are also bothersome processes in any application of MCDA methods. It rather emphasizes that the input information preparation and the interrelationship analysis are very important research issues in supplier selection for the future. Obtaining results on these issues is a prerequisite for (1) developing particular MCDA methods to supplier evaluation and (2) adapting graph theoretic techniques for efficient solution search.
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References Adler, N., Friedman, L., and Sinuany-Stern, Z., 2002. Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research, 140 (2), 249–265. Andersen, P. and Petersen, N.C., 1993. A procedure for ranking efficient units in data envelopment analysis. Management Science, 39 (10), 1261–1264. Balakrishnan, N. and Chakravarty, A.K., 2008. Product design with multiple suppliers for component variants. International Journal of Production Economics, 112 (2), 723–741. Bhutta, K.S. and Huq, F., 2002. Supplier selection problem: a comparison of the total cost of ownership and analytic hierarchy process approaches. Supply Chain Management: An International Journal, 7 (3), 126–135. Bouyssou, D., 1999. Using DEA as a tool for MCDM: some remarks. Journal of the Operational Research Society, 50 (9), 974–978. Castelli, L., Pesenti, R., and Ukovich, W., 2001. DEA-like models for efficiency evaluations of specialized and interdependent units. European Journal of Operational Research, 132 (2), 274–286. Charnes, A., Cooper, W.W., and Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research, 2 (6), 429–444. Choi, T.Y., et al., 2002. Supplier–supplier relationships and their implications for buyer–supplier relationships. IEEE Transactions on Engineering Management, 49 (2), 119–130. De Boer, L., Labro, E., and Morlacchi, P., 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., and Telgen, J., 1998. Outranking methods in support of supplier selection. European Journal of Purchasing & Supply Management, 4 (2–3), 109–118. Dulmin, R. and Mininno, V., 2003. Supplier selection using a multi-criteria decision aid method. Journal of Purchasing & Supply Management, 9 (4), 177–187. Dyer, J.S., 2005. MAUT – multiattribute utility theory. In: J. Figueira, S. Greco and M. Ehrgott, eds. Multiple criteria decision analysis: state of the art surveys. New York: Springer, 266–295. Edwards, W., 1977. How to use multiattribute utility measurement for social decision making. IEEE Transactions on Systems, Man and Cybernetics, 7 (5), 326–340. Feremans, C., Labbe´, M., and Laporte, G., 2003. Generalized network design problems. European Journal of Operational Research, 148 (1), 1–13. Figueira, J. and Roy, B., 2002. Determining the weights of criteria in the ELECTRE type methods with a revised Simos’ procedure. European Journal of Operational Research, 139 (12), 317–326. Garfamy, R.M., 2006. A data envelopment analysis approach based on total cost of ownership for supplier selection. Journal of Enterprise Information Management, 19 (6), 662–678. Ghodsypour, S.H. and O’Brien, C., 1998. A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming. International Journal of Production Economics, 56–57 (1), 199–212. Guitouni, A. and Martel, J.-M., 1998. Tentative guidelines to help choosing an appropriate MCDA method. European Journal of Operational Research, 109 (2), 501–521. Ho, W., Xu, X., and 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. Hou, L., Han, D., and Lin, Z., 2006. Research on supplier selection for inter-firm product collaborative development. In: IEEE Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, 6988–6992. Jain, V., Wadhwa, S., and Deshmukh, S.G., 2009. Select supplier-related issues in modelling a dynamic supply chain: potential, challenges and direction for future research. International Journal of Production Research, 47 (11), 3013–3039. Kao, C., 2009. Efficiency measurement for parallel production systems. European Journal of Operational Research, 196 (3), 1107–1112. Keeney, R.L. and Raiffa, H., 1976. Decisions with multiple objectives: preferences and value tradeoffs. New York: Wiley. Kim, D.Y. and Xirouchakis, P., 2008. CO2DE: a decision support system for collaborative design. Journal of Engineering Design, 21 (1), 31–48. Krishnan, V. and Ulrich, K.T., 2001. Product development decisions: a review of the literature. Management Science, 47 (1), 1–21. Kuo, R.J., Hong, S.Y., and Huang, Y.C., 2010. Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection. Applied Mathematical Modelling, 34 (12), 3976–3990. Li, W., Chen, Y., and Fu, Y., 2008. Combination of TOPSIS and 0–1 programming for supplier selection in supply chain management. In: IEEE International Conference on Networking, Sensing and Control, Sanya, China, 1531–1535. Liu, J., Ding, F.-Y., and Lall, V., 2000. Using data envelopment analysis to compare suppliers for supplier selection and performance improvement. Supply Chain Management: An International Journal, 5 (3), 143–150.
Downloaded by [ETH Zurich] at 05:47 16 August 2011
International Journal of Production Research
13
Min, H., 1993. International supplier selection: a multi-attribute utility approach. International Journal of Physical Distribution & Logistics Management, 24 (5), 24–33. Monczka, R.M., et al., 2000. New product development: strategies for supplier integration. Milwaukee, WI: American Society for Quality. Ng, W.L., 2008. An efficient and simple model for multiple criteria supplier selection problem. European Journal of Operational Research, 186 (3), 1059–1067. Roy, B., 1990. Decision-aid and decision making. European Journal of Operational Research, 45 (2–3), 324–331. Saaty, T.L., 1980. The analytic hierarchy process: planning, priority setting, resource allocation. New York: McGraw-Hill. Sen, S., et al., 2008. A framework for defining both qualitative and quantitative supplier selection criteria considering the buyer– supplier integration strategies. International Journal of Production Research, 46 (7), 1825–1845. Sevkli, M., 2010. An application of the fuzzy ELECTRE method for supplier selection. International Journal of Production Research, 48 (12), 3393–3405. Seydel, J., 2005. Supporting the paradigm shift in vendor selection: multicriteria methods for sole-sourcing. Managerial Finance, 31 (3), 49–66. Simon, H.A., et al., 1987. Decision making and problem solving. Interfaces, 17 (5), 11–31. Singhal, J. and Singhal, K., 2002. Supply chains and compatibility among components in product design. Journal of Operations Management, 20 (3), 289–302. Stewart, T.J., 1996. Relationships between data envelopment analysis and multicriteria decision analysis. Journal of the Operational Research Society, 47 (5), 654–665. Timmerman, E., 1986. An approach to vendor performance evaluation. IEEE Engineering Management Review, 15 (3), 14–20. Wagner, S.M., 2010. Supplier traits for better customer firm innovation performance. Industrial Marketing Management, 39 (7), 1139–1149. Wagner, S.M. and Hoegl, M., 2006. Involving suppliers in product development: insights from R&D directors and project managers. Industrial Marketing Management, 35 (8), 936–943. Wang, G., Huang, S.H., and Dismukes, J.P., 2004. Product-driven supply chain selection using integrated multi-criteria decisionmaking methodology. International Journal of Production Economics, 91 (1), 1–15. Wang, H.S. and Che, Z.H., 2007. An integrated model for supplier selection decisions in configuration changes. Expert Systems with Applications, 32 (4), 1132–1140. Weber, C.A., 1996. A data envelopment analysis approach to measuring vendor performance. Supply Chain Management: An International Journal, 1 (1), 28–39. Weber, C.A., Current, J.R., and Benton, W.C., 1991. Vendor selection criteria and methods. European Journal of Operational Research, 50 (1), 2–18. Wu, T. and Blackhurst, J., 2009. Supplier evaluation and selection: an augmented DEA approach. International Journal of Production Research, 47 (16), 4593–4608.
