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Selection of optimal supplier in supply chain management strategy
with analytic network process and choquet integral
Tseng Ming-Lang a,*, Jui Hsiang Chiang b, Lawrence W. Lan c
a Department of Business Administration, Ming-Dao University, #369 Wenhua Road, Peetou Township, Changhwa County, Taiwan, ROC b Department of International Business, Toko University, Taiwan, ROC c Department of global marketing and logistics, MingDao University, Taiwan, ROC
a r t i c l e i n f o
Article history:
Received 14 April 2007
Received in revised form 8 December 2008
Accepted 8 December 2008
Available online xxxx
Keywords:
Supply chain management strategy
Analytical network process
Choquet integral
a b s t r a c t
Selection of appropriate suppliers in supply chain management strategy (SCMS) is a challenging issue
because it requires battery of evaluation criteria/attributes, which are characterized with complexity,
elusiveness, and uncertainty in nature. This paper proposes a novel hierarchical evaluation framework
to assist the expert group to select the optimal supplier in SCMS. The rationales for the evaluation frame-
work are based upon (i) multi-criteria decision making (MCDM) analysis that can select the most appro-
priate alternative from a finite set of alternatives with reference to multiple conflicting criteria, (ii)
analytic network process (ANP) technique that can simultaneously take into account the relationships
of feedback and dependence of criteria, and (iii) choquet integral—a non-additive fuzzy integral that
can eliminate the interactivity of expert subjective judgment problems. A case PCB manufacturing firm
is studied and the results indicated that the proposed evaluation framework is simple and reasonable
to identify the primary criteria influencing the SCMS, and it is effective to determine the optimal supplier
even with the interactive and interdependent criteria/attributes. This hierarchical evaluation framework
provides a complete picture in SCMS contexts to both researchers and practitioners.
Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction
In facing an ever-increasingly competitive and changeable envi-
ronment, firms require constantly managing their organizational
resources as well as tightening their industrial relationships so as
to maintain competitive advantages. More specifically, firms re-
quire reorganizing their supply chain management strategy
(SCMS) to harmonize with the external environments by integrat-
ing the organizational resources, information, and activities. How-
ever, SCMS may encounter multi-dimensional difficulties as it
involves numerous organizational functions and resources integra-
tion among various departments. In practice, the contexts of SCMS
are uncertain, unpredictable, and difficult to assess accurately withqualitative information. As a consequence, selection of appropriate
suppliers in SCMS is a complicated, challenging process.
Davis (1993) indicated that there are three different sources of
uncertainties in SCMS: (i) Supplier uncertainty, arising from on-
time performance, average lateness, degree of inconsistency. (ii)
Manufacturing uncertainty, arising from process performance, ma-
chine breakdown, supply chain performance, etc. (iii) Demand
uncertainty, arising from forecasting errors, irregular orders, and
among others. Some researchers argued that uncertainty is strate-
gic vital for firms to test their management systems (Chen & Paul-
raj, 2004; Hahn, Watts, & Kim, 1990; Krause & Ellram, 1997; Van
Hoek, 1998; Webster, 1995). Organizational SCMS is made when
it is strategic, usually rather complex with external and internal
implications (Fuentes-Fuents, Albacete-Sae, & Llorens-Montes,
2004; Ward, Duray, Leong, & Sum, 1995). Relevant literature
showed that increased competition in the marketplace and greater
pace of technological innovation than before are two primary fac-
tors driving firms’ needs for world-class suppliers and for supplier
development; hence, the SCMS alignment must be synchronized
with uncertainty in a competitive and changeable environment
(Flint, 2004; Hahn et al., 1990).
Selection of appropriate suppliers in SCMS requires battery of evaluation criteria, which include such information as customer fo-
cus, competitive priority, strategic purchasing, information tech-
nology, and top management support of a firm (Carr and
Pearson, 1999; Johnston, McCutcheon, Stuart, & Kerwood, 2004;
Li, Ganesan, & Sivakumar, 2006a; Li, Ragu-Nathan, Ragu-Nathan,
& Rao, 2006b; Tan, Lyman, & Wisner, 2002). A firm must have out-
standing competitive priority in order to perform well manage-
ment in production, such as producing high quality products
with excellent logistics arrangement. The competitive priority is
closely related to top management support that requires strategic
purchasing. Although Chen and Paulraj (2004) asserted that
customer focus, competitive priority, information technology,
0360-8352/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved.doi:10.1016/j.cie.2008.12.001
* Corresponding author. Tel.: +886 910 309 400.
E-mail address: [email protected] (M.-L. Tseng).
Computers & Industrial Engineering xxx (2009) xxx–xxx
Contents lists available at ScienceDirect
Computers & Industrial Engineering
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / c a i e
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strategic purchasing, and top management support are indepen-
dently related, it is hard to justify the independency, and this
assertion has invited many disputable arguments among the social
science studies. As such, the present study will view SCMS a com-
plex, interactive process of many different resources with multi-
dimensional, interdependent criteria/attributes. Hence, conven-
tional multi-criteria approaches and weight average method
assuming independence of attributes are not suitable for evaluat-ing SCMS because only when information sources are non-interac-
tive can their weighted effects be viewed as additive (Wang, Leung,
& Wang, 1999). To overcome these shortcomings, non-additive set
functions must be used, and choquet integral (a nonlinear integral
or so-called ‘‘non-additive fuzzy integral”) with respect to non-
additive set functions can be employed to replace the weighted
average (Kahraman, Çevik, Ates, & Gülbay, 2007; Murofushi & Su-
geno, 1989; Wang & Klir, 1992; Wang et al., 1999). By using non-
additive fuzzy integral, one can eliminate experts’ subjective judg-
ment problems involving the complex SCMS.
To date, few studies have adopted a rigorous methodology
while selecting the suppliers in SCMS. Many suppliers have built
up their capabilities to support some other services, such as SCM,
just-in-time, service quality, etc. These suppliers normally have
long-term contracts providing their customers with multiple-func-
tion services (Choi & Hartley, 1996). There are different supplier
selection criteria in different status. Chan and Kumar (2007) iden-
tified some important and critical decision criteria including risk
factors for the development of an efficient system for global sup-
plier selection. Wang and Che (2007) presented a model, based
on the concepts of parts change requirements, fuzzy performance
indicators, and integration of different attributes, to allow the parts
supplier selection of a specific commercial product to be explored.
Xia and Wu (2007) proposed an integrated analytic hierarchy pro-
cess (AHP), which is improved by rough sets theory and multi-
objective mixed integer programming, to simultaneously deter-
mine the number of suppliers and the order quantity allocated to
each supplier, under the contexts of multiple sources, multiple
products, multiple criteria, and with suppliers capacity constraints.Saaty (1996) developed analytic network process (ANP), a new
analysis method that simultaneously takes into account both the
relationships of feedback and dependence. This paper attempts to
develop a novel hierarchical evaluation framework to assess the
SCMS, wherein the interdependency among various criteria/attri-
butes can be effectively captured by a combination of ANP with
choquet integral. The major advantages of combining ANP with
choquet integral are that the evaluation can account for the inter-
dependency of criteria/attributes, the nonlinear relationship
among criteria/attributes, and the environmental uncertainties.
Such a combination was rarely seen in literature before. This pro-
posed hierarchical analytical approach is not only novel but suffi-
ciently general to be applied under various settings, thus can
help firms to measure and select the optimal suppliers in SCMS.The remainder of this paper is organized as follows. Section 2
addresses the background of SCMS with relevant literature re-
viewed. Section 3 presents the hierarchical structure of SCMS with
criteria and attributes involved in the supplier evaluation. Section
4 proposes the hierarchical analytical approach for evaluating
SCMS. A PCB manufacturing case firm’s SCMS is evaluated in Sec-
tion 5. Managerial implications are discussed in Section 6, followed
by concluding remarks.
2. Background of SCMS
A well-integrated supply chain management (SCM) involves
coordinating the flows of materials and information between sup-pliers, manufacturers and customers, and implementing product
postponement and mass customization in the supply chain. Higher
level of integration with suppliers and customers in the chain is ex-
pected to result in more effective competitive advantages (Ander-
son & Katz, 1998; White, Pearson, & Wilson, 1999). Supply chain
management strategy (SCMS) is used to explain the planning and
control of materials/information flows and logistics activities, not
only internally within a firm but also externally between firms
(Cooper, Ellram, Gradner, & Hanks, 1997; Fisher, 1997; Seo,2006). The key concept is that the channel is viewed as an inte-
grated whole, with the goal of understanding the channel as an
application system. Each firm in the channel affects, directly or
indirectly, all the other channel members, as well as the ultimate,
overall channel performance (Beamon, 1999; Carr & Pearson, 2002;
Handfield & Nichols, 1999; Tan, Kannan, & Handfield, 1998).
Increased global competition pressures have been forcing firms
to continuously adopt, develop and enhance customer focus, com-
petitive priority, information technology, strategic purchasing and
top management support. For these reasons, a firm must enhance
its SCMS for developing the optimal suppliers more suitable than
other competitive firms, and must facilitate the criteria and attri-
butes of SCMS within its organization to strengthen its competitive
advantages. The goal of integrated SCMS is to create manufacturing
processes and logistics functions seamlessly across the supply
chain as an effective competitive weapon that cannot be easily
duplicated by competitors. Tan et al. (2002) explored the relation-
ships between supplier management practices, customer relations
practices, and organizational performance with the usage of pur-
chasing, quality and customer relations to represent SCMS. Yao,
Palmer, and Dresner (2007) studied the electronically-enabled sup-
ply chains, which offer the potential for reduced costs and higher
sales. They found top management support and external influences
are both important determinants. Besides, perceived benefits to
customers, perceived benefits to suppliers, and perceived inter-
nally focused benefits are all positively influencing electronically-
enabled supply chains use. The indicators of this construct are pre-
sented to denote the presence of electronic transactions, supplier
management and competitive priorities in various forms betweenthe supply chain partners. Based on these findings, the SCMS is
presented with interactive relationships among their proposed
dimensions (Li et al., 2006a; Li et al., 2006b; Chou & Chang,
2008). Even if a firm merely focuses on the customer criteria, it still
needs top management support and adjusts its competitive prior-
ity accordingly.
Numerous criteria and attributes must be considered when
evaluating the SCMS. In this study, we emphasize the following
five criteria: customer focus, competitive priority, information
technology, strategic purchasing, and top management support.
First of all, customer focus practices involve the establishment of
links between customer needs and satisfaction and internal pro-
cesses (Sousa, 2003; Stuart, 1997). Mendoza, María Pérez, and
Grimán (2007) forecasted that for various reasons and with moreor less clarity concerning the subject, the firms have a new trend
to implement customer relationship management as a factor
allowing them to survive in the new market conditions and favor-
ing the relationship with their customers. Tan et al. (1998) consid-
ers customer relationship management as an important
component of SCMS. Closer customer relationship allows an orga-
nization to differentiate its products from competitors, sustain cus-
tomer loyalty, and dramatically extend the value it provides to its
customers (Magretta, 1998). Closer customer relationship also re-
quires improving the customer satisfaction (Hwang, 1998; John-
ston et al., 2004). Day (2000) pointed out that committed
relationships are the most sustainable advantage because of their
inherent barriers to competition. The growth of mass customiza-
tion and personalized service has led to an era where relationshipmanagement with customers is becoming crucial for corporate
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survival. Good relationships with supply chain members, including
customers, are needed for successful implementation of SCMS.
Secondly, for competitive priority, Cox and Blackstone (1998)
stated that ‘‘To be most effective, the manufacturing should act
in support of the overall strategic directions of the business and
provide for its competitive priorities.” It guides the choice and
development of competitive priorities and specifies how the oper-
ational function provides a firm with competitive priority in themarketplace or tactical goal of the operational function (Chenhall
& Langfield-Smith, 1998). SCMS has to be involved in top-down
operation decisions; therefore, once competitive priority is evalu-
ated it becomes the basis for making operational decision as the
goal of operational function in marketplace (Margaret, 1996).
Establishing close relationships with a limited number of suppliers,
when properly and selectively used, has been directly linked to
customer focus (Stanley & Wisner, 2001) and competitive priority.
Thirdly, for information technology (IT) in SCMS, Dehning,
Richardsonand, and Zmud (2007) examined the financial benefits
of newly adopted IT-based SCMS and found a positive relation be-
tween firms’ investment in IT-based SCM systems and firms’ per-
formance. IT enhanced supply chain efficiency by providing real-
time information about product availability, inventory level, ship-
ment status, and production requirements. In particular, the com-
mon goal of these systems is to transform all the management
systems into perfect information.
Fourthly, the importance of purchasing function that a company
needs to optimize its entire SCM, rather than individual elements
within the supply chain, suggests that purchasing is indeed strate-
gic. For purchasing, operations and other elements of a supply
chain should work together and their functional strategies should
be aligned in support of the firm’s SCMS (Watts, Kim, & Hahn,
1992). Carr and Smeltzer (1999) explored how firms with strategic
purchasing are able to foster long-term, cooperative relationships
and communication, and achieve greater responsiveness to the
needs of their suppliers. Strategic purchasing can foster greater
commitment and trust to promote long-term relationships be-
tween the focal firm and its suppliers. Moreover, purchasinginvolvement earlier in the new product development process has
provided many companies with advantages in bringing new de-
signs to market faster, with fewer quality defects, and at lower
costs (Dyer, 1996). The present study presumes that strategic pur-
chasing contributes to effective SCM when it fosters a long-term
strategic orientation between the firm and its supplier selection.
Finally, for top management support in SCMS, Wilson and
McDonald (1996) regarded it as an important factor in the success-
ful implementation of decision support systems. SCMS may create
sustained competitive advantage directly should the top manage-
ment support be able to exploit unique SCM competencies. One of
the major functions for top management executives is to influence
the setting of organizational values and to develop suitable man-
agement styles to ensure the SCMS on track. Although top manage-ment support of SCMS has generally been regarded as a key success
parameter, yet the nature of its impact and the phenomenon it
brings into being are still not very clear to us, thus it requires an
in-depth analysis to enhance our understanding to draw useful
implications for research and practice (Chen & Paulraj, 2004).
Optimal supplier evaluation depends on SCMS requirements,
including as customer focus, competitive priority, information
technology, strategic purchasing, and top management support
(Hoque, 2004; Li, Ganesan, et al., 2006; Li, Ragu-Nathan, et al.,
2006; Tan, 2001). Thus, the SCMS models on supplier evaluation
are in essence multi-dimensional, complex, and interdependency
activities (Yao et al., 2007). The SCMS models on supplier evalua-
tion permitting intuitive judgment have garnered acceptance by
various experts, including scholars and SCM professionals. To assistthe expert group to select the optimal suppliers in SCMS contexts,
this study proposes an effective hierarchical evaluation framework
by explicitly describing the decision structure of SCMS upon which
pair comparison subjective judgments of experts can base.
3. Hierarchical structure of SCMS
Generally, literature on SCM addressed the purchasing and sup-ply perspective(Morgan & Monczka, 1996). This perspectiveof SCM
is synonymous with supplier base integration that evolves fromthe
traditional purchasing and SCM functions. It emphasizes that pur-
chasingand materials management represent a basic strategic busi-
ness process, rather than a narrow specialized supporting function,
to overall business strategy. This is a management philosophy that
extends traditional internal activities by embracing an inter-enter-
prise scope, bringing trading partners together with the common
goal of optimization and efficiency (Carr & Pearson, 2002).
Literature suggested that five rules must be obeyed when devel-
oping criteria: (i) completeness, criteria must cover all primary
SCMS of the decision making problem; (ii) operational, criteria
must be meaningful for the analysis; (iii) decomposable, criteria
can be decomposed from a hierarchy to a lower hierarchy to sim-
plify the evaluation process; (iv) non-redundant , criteria must not
be counted twice; and (v) minimum size, the number of criteria
should be kept as small as possible. As aforementioned, many cri-
teria must be considered in evaluating the suppliers for SCMS and
these criteria are normally interdependent and interactive with
each other. Information of SCMS contexts can be collected by liter-
ature review as well as interviews with expert SCMS staff and re-
lated senior managers. Particularly, interviews should obtain data
regarding what changes and attributes have led the firm to sustain
success of SCMS. This creates typical MCDM problem with varying
criteria and attributes on related activities. In addition, this study
considers uncertainty as determinants for further practices of
SCMS. As discussed earlier, the uncertain determinants are in the
forms of supply, demand and technology. Supply uncertainty in-
cludes indicators that represent quality, timeliness and the inspec-tion requirements of the suppliers. Demand uncertainty is
measured in terms of fluctuations and variations in demand. Tech-
nology uncertainty measures the extent of technological changes
evident within the industry. These constructs are operationalized
based on prior research (e.g., Chen & Paulraj, 2004; Davis, 1993;
Krause, 1999; Van Hoek, 1998).
Past research has offered valuable structures for SCMS, the ma-
jor references for this study are from Lado, Boyd, and Wright
(1992), Palmer and Griffith (1998), Carr and Pearson (1999), Tan
et al. (2002), Chen and Paulraj (2004), Johnston et al. (2004), Li,
Ganesan, et al. (2006) and Li, Ragu-Nathan, et al. (2006). An in-
depth discussion and literature review have led this study to de-
cide five criteria with eighteen attributes and to select four suppli-
ers. The five criteria used are customer focus (Hwang, 1998; Johnston et al., 2004; Mendoza et al., 2007; Sousa, 2003), compet-
itive priority (Chenhall & Langfield-Smith, 1998; Cox & Blackstone,
1998; Dreyer & Gronhaug, 2004; Kathuria, 2000; Margaret, 1996),
strategic purchasing (Carr & Smeltzer, 1999; Cousins, 1999; Dyer,
1996; Watts et al., 1992), top management support (Chen & Paul-
raj, 2004; Krause, 1999; Wilson & McDonald, 1996), and informa-
tion technology (King, 1996; Carr and Pearson, 1999; Dehning
et al., 2007; Yao et al., 2007). Our SCMS model will integrate the
most relevant activities, components, and characteristics found in
SCMS literature, which are put forward as criteria. The criteria
and their associated attributes are discussed as follows.
First, the customer focus (C1) is the goal of businesses, which is
to ‘‘create and maintain customers” (Levitt, 1985). The attributes
for customer focus construct contain the responses to customersevolving needs and wants (A11), evaluation of customer complains
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(A12), products satisfying customer expectation (A13) and satisfy-
ing customer needs—the central purpose of business plan (A14)
(Hwang, 1998; Johnston et al., 2004; Mendoza et al., 2007; Sousa,
2003).
Secondly, the competitive priority (C2) is a common success
theme of operations strategy, which picks up the manufacturers’
choices of emphasis among key capabilities. The attributes in this
construct contain offering products with lowest price (A21), great-er emphasis on innovation (A22), launching new product quickly
(A23), and quality performance (A24) (Chenhall & Langfield-Smith,
1998; Margaret, 1996; Stanley & Wisner, 2001).
Thirdly, the strategic purchasing (C3) is critical to facilitate close
interactions with a limited number of suppliers and making effec-
tive use of the firm’s supply base (Cousins, 1999). Three attributes
in this construct are considered: purchasing function with a for-
mally written long-range plan (A31), purchasing focus on longer
term issues that involve risk and uncertainty (A32), and purchasing
performance measured (A33) (Carr & Smeltzer, 1999; Dyer, 1996;
Watts et al., 1992).
Fourthly, all the SCMS activities are involved with the top man-
agement support (C4), which is a sustained competitive advantage
directly, should it be able to exploit unique SCM competencies
(Lado et al., 1992). This construct emphasizes such attributes as
purchasing function strategic role (A41), supporting the competi-
tive priority with company mission (A42), supporting the need
for inter-organizational information system (A43), and accounting
for customer needs a vital part of corporate strategy (A44) (Chen &
Paulraj, 2004; Krause & Ellram, 1997; Wilson & McDonald, 1996).
Lastly, the information integration needs the information tech-
nology (C5) to be applied in SCMS. The complete integration into
SCMS with electronic commerce component also aids in the evolu-
tion of SCMS. Sharing information with supply chain partners
through electronic data interchange (EDI), for instance, is also a crit-icalcomponent of SCMS (King, 1996). Theattributes in thisconstruct
include a direct link of computers to computers with key suppliers
(A51), inter-organizational coordination achieved by electronic
links (A52), and using IT-enabled transaction processing (A53) (Carr
and Pearson, 1999; Dehning et al., 2007; Yao et al., 2007).
Fig. 1 presents the hierarchical structure of evaluation frame-
work for SCMS, a MCDM analysis combining ANP with choquet
integral to select the optimal suppliers for case PCB manufacturing
firm. Basically, MCDM analysis is to assist the evaluators in select-
ing one or few most appropriate alternatives from a finite set of
alternatives with reference to multiple, usually conflicting, criteria
(Yeh, Deng, & Pan, 1999); ANP is to simultaneously account for the
relationships of feedback and dependence (Saaty, 1996); while
choquet integral (non-additive fuzzy integral) is to eliminate the
interactivity of expert subjective judgment problems (Kahraman
et al., 2007; Murofushi & Sugeno, 1989; Wang & Klir, 1992; Wang
et al., 1999).
Fig. 1. SCMS hierarchical structure.
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4. Methodologies
To determine the overall performance of SCMS, evaluation crite-
ria are multiple and frequently structured into multi-level hierar-
chies. Decision-making is the process of defining the decision
objectives, gathering relevant information, and selecting the opti-
mal alternatives. Hence, the first phase is to define the decision
objectives—here is to select the suppliers in SCMS under uncer-tainty. After defining the decision objectives, it is required to gen-
erate and establish evaluation objectives in current business
scenario, which is similar to a chain of the determinants—criteria
(purposes), attributes (evaluation factors) and alternatives (deci-
sions). As discussed in the previous section, five criteria of SCMS
are to be considered: customer focus, competitive priority, strate-
gic purchasing, top management support and information technol-
ogy. Moreover, the criteria cluster has to interact with supply,
demand, and technology determinants in order to precisely find
out the best supplier among the four suppliers of case firm. Overall
SCMS performance of the case firm (Suppliers A, B, C, and D) can be
obtained by (i) assigning weights to five criteria (C1, C2, C3, C4, and
C5) and their associated Aj attributes (Cij, i = 1,2,3,4,5; j = 1,2, . . .,
Aj) and (ii) assessing the performance rating of each aspect and
its associated criteria. We will first introduce the ANP technique
and then discuss the choquet integral approach, followed by the
proposed application procedures.
4.1. ANP
Analytic hierarchy process (AHP) was first proposed by Saaty
(1980a) and Saaty (1980b). It has been widely applied in areas such
as MCDM and has become a popular application for performance
evaluation. AHP must satisfy the characteristic of independence
among the criteria before it can proceed to decision making. How-
ever, given the problems encountered in reality, a dependent and
feedback relationship will usually be generated among the evalua-
tion criteria and such an interdependent relationship usually
becomes more complex with the change in scope and depth of thedecision-making problems. Therefore, Saaty (1996) developed a
newanalysismethod—analyticnetwork process(ANP)thatsimulta-
neously takes into account both the relationships of feedback and
dependence, and developed the ANP. Tseng, Lin, Chiu, and Liao
(2008) applied ANP to selecting of competitive priority in cleaner
production implementation. The merits of AHP in group decision-
making are as follows (Dyer & Forman, 1992): (i) both tangibles
andintangibles,individualvalues, andshared valuescanbe included
in the decisionprocess; (ii) the discussion in a group canbe focused
on objectives rather than on alternatives; (iii) the discussion can be
structured so that every factor relevant to thedecision is considered;
and (iv) in a structured analysis, the discussion continues until rele-
vant information from each individual member in the group is con-
sidered and a consensus is achieved.In addition to these merits of AHP, the ANP provides a more
generalized model in decision-making without making assump-
tions about the independency of the higher-level elements from
lower-level ones and also of the elements within their own level.
A two-way arrow among different levels of attributes may graph-
ically represent the interdependencies in an ANP model. If interde-
pendencies are present within the same level of analysis, a looped
arc may be used to represent such interdependencies.
1. Saaty randomly generated reciprocal matrix using scale 1/9,1/
8,1/7, . . ., 1,. . .,8,9. The scales are described as 1: equal impor-
tance, 3 moderate importance, 5 strong importance, 7 very
strong or demonstrated importance, and 9 extreme importance.
Even numbered values will fall in between importance levels.
Reciprocal values (e.g. 1/3, 1/5, etc.) mean less importance,
strongly less importance, etc. Only the upper triangle of the
matrix needs to be completed. The lower triangle of the pair-
wise comparison matrix is composed of reciprocal values.
2. Assuming there are n number of criteria, denoted as (C 1, . . ., Cn),
its pairwise comparison matrix would be A = (aij), in which aij
represents the relative significance of C i to C j. Then, by using
the row vector average normalization proposed by Saaty(1996), the approximate weight Wi of Ci is calculated as
follows:
Wi ¼
Pn j¼1 aij=
Pni¼1aij
À Án
; 8i; j ¼ 1;2; . . . ; n ð1Þ
3. The consistency test of ANP is designed to ensure the consis-
tency of judgments by decision makers throughout the decision
making process. When inconsistencies exist in the pairwise
comparison matrix A, Saaty (1980a) and Saaty (1980b) proved
that for consistent reciprocal matrix, the kmax value is equal to
the number of comparisons, or kmax = n. Then Saaty gave a mea-
sure of consistency, called consistency index (CI), as deviation
or degree of consistency using the following formula:
CI ¼kmax À n
n À 1ð2Þ
5. The consistency ratio (CR) is then defined as the ratio of CI to
the mean random consistency index (RI) as follows:
CR ¼CI
RIð3Þ
The CR should be less than 0.1. It indicates that the consistency level
of the pairwise comparison matrix is acceptable. When CR is greater
than 0.1, it indicates that the results of the decision process are not
consistent, suggesting that the decision maker needs to perform the
pairwise comparison again.
5. Limiting the weight supermatrix for the weights.
ANP uses supermatrix to deal with the relationship of feedback
and interdependence among the criteria. If no interdependent rela-
tionship exists among the criteria, the pairwise comparison value
would be 0. In contrast, if an interdependent and feedback rela-
tionship exists among the criteria, then such value would no longer
be 0 and an unweighted supermatrix M will be obtained. If the
matrix does not conform to the principle of column stochastic,
the decision maker can provide the weights to adjust it into a
supermatrix that conforms to the principle of column stochastic,
and it will become a weighted supermatrix M . We thengetthe lim-
ited weighted supermatrix M Ã based on Eq. (4) and allow for grad-ual convergence of the interdependent relationship to obtain the
accurate relative weights among the criteria
M Ã ¼ limk!1
M k: ð4Þ
4.2. Choquet integral
The choquet integral (a non-additive fuzzy integral), is a
numeric-based approach which has been used for both pattern
recognition and image segmentation (Grabisch & Nicolas, 1994;
Keller, Gader, Tahani, Chiang, & Mohamed, 1994). Adoption of
a fuzzy integral in membership aggregation, rather than a tradi-
tional aggregation operator, leads to an important distinction as
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to how processes of fuzzy integration are utilized. The success of
a choquet integral depends on an appropriate representation of
fuzzy measures, which captures the importance of individual
criterion or their combination (Chiang, 2000; Klir, Wang, &
Harmanec, 1997). This holistic utilization of fuzzy integral is an
important component in many related works to provide informa-
tion integration capability. For instance, Chiang (2000) demon-
strated a good classification result using the proposedaggregator to integrate the memberships of several cluster in a
complex, unconstrained, handwritten digit recognition domain.
Chen and Tzeng (2001) presented a good traffic assignment
model with fuzzy integral to describe the characteristics of traf-
fic behavior in a road network, and their results have explained
more interactivity and uncertainty of traffic among links, com-
pared to traditional models. Since the fuzzy integral is not differ-
entiable with respect to fuzzy measures, an identification
scheme based on the conventional optimization technique can-
not be applied to solving k-fuzzy measures. The basic properties
and definitions of the choquet integral with respect to fuzzy
measures applied in this study are introduced as follows.
Sugeno (1974) introduced monotonic and non-additive fuzzy
integrals to express the grades of importance for attributes, which
is useful to model the preference structure. Fuzzy measure can be
explicated as the subjective importance of a criterion during the
evaluation process. Sugeno and Terano (1977) incorporated the k-
additive axiom to reduce the difficulty of collecting information. In
fuzzy measure space ( X ,b, g ), let k 2 ðÀ1;1Þ. If A 2 b; B 2 b;
A \ B ¼ /, thenthefuzzy measureg isk-additive. This particular fuz-
zy measure is termed as k-fuzzy measure because it has to satisfy k-
additively, named Sugeno measure.
Assume that X = { x1, x2, x3, . . .,xn} and P ( X ) is the power set of X ,
the set function g: P ( X )?[0,1] is called a fuzzy measure, which is
non-additive and preserves the following properties:
1. g (u) = 0 ;
2. g ( X ) = 1 ;
3. if A,BeP ( X ) and A&B then g ð AÞ 6 g ðBÞ (monotonicity);4. In P ( X ), if A1& A2& A3& A4, . . ., and U 1i¼1 Ai 2 P ð X Þ, then limi!1 g
ð AiÞ ¼ g ðU 1i¼1 AiÞ (continuity from below);
5. In P ( X ), if A1 ' A2 ' A3 ' A4 . . ., and \1i¼1 Ai 2 P ð X Þ, then limi!1 g
ð AiÞ ¼ g ð\1i¼1 AiÞ (continuity from above).
In addition, k-fuzzy measure has the following additional
properties:
g ð A [ BÞ ¼ g ð AÞ þ g ðBÞ þ k g ð AÞ g ðBÞ ð5Þ
where k > 0 for all A; B 2 P ð X Þ and A \ B ¼ /. If X is a finite set,
then U ni¼1 Ai ¼ X . The k-fuzzy measure g satisfies the following:
g ð X Þ ¼ g U
n
i¼1 Ai
¼
1k Q
n
i¼1
½1 þ k g ð AiÞ� À 1
& 'if k–0;
Pn
i¼1
g ð AiÞ if k ¼ 0;
8>>><>>>: ð6Þ
where Ai \ A j ¼ / for all i, j = 1,2,3,. . .,n and i– j. In Eq. (1), k– 0
indicates that the k-fuzzy measure g is non-additive; otherwise,
the k-fuzzy measure g is additive and there is no interaction
between Ai and A j for i – j. The interaction means there is informa-
tion fusion between criteria (Klir et al., 1997). k > 0 implies that
g ð A [ BÞ ¼ g ð AÞ þ g ðBÞ and the set { A,B} has multiplicative effect;
whereas k < 0 indicates the substitutive effect of the set { A,B} (Chen
& Tzeng, 2001). In fuzzy measure space ( X,b, g ), let h be a measurable
function from X to [0,1], the definition of the fuzzy integral of h over
A with respect to g is
Z A hð xÞd g ¼ supa2½0;1�½a ^ g ð A \ F aÞ� ð7Þ
where F a = { x|h( x)P a} (Wang & Klir, 1992). A is a domain of fuzzy
integral. When A = X , the fuzzy integral can be denoted byR
h d g .
Consider a fuzzy measure g of ( X,P ( X )) and X is a finite set here.
Let h: X ?[0,1] and assume without loss of generality that the func-
tion h(xi) is monotonically decreasing with respect to i, for instance
h(x1)P h( x2)P Á Á ÁP h( xn). To assure that the elements in X be
renumbered, we get the following equation:
Z hð xÞ g ¼ _n
i¼1½hð xiÞ ^ g ðH iÞ ð8Þ
where H = ( x1, x2 ,. . ., xi), i = 1,2, . . ., n. In practice, h can be regarded
as the performance on a particular attribute for the criteria; g pre-
sents thegrade of subjective importance of each attribute. The fuzzy
integral of h(x) with respect to g gives the overall assessment of the
attribute. To simply the calculation, the same fuzzy measure of cho-
quet integral is expressed as follow:
ðc Þ
Z hdg ¼ hð xnÞ g ðH nÞ þ ½hð xnÀ1Þ À hð xnÞ� g ðH nÀ1Þ þ Á Á Á
þ ½hð x1Þ À hð x2Þ� g ðH iÞ ð9Þ
where 0 6 hð x1Þ 6 hð x2Þ 6 . . .:hð xnÞ 6 1; hð x0Þ ¼ 0 and H i ¼ xðiÞ; . . . ;
xðnÞ. In literature, the fuzzy integral defined by
R hd
g is called ‘‘cho-quet integral.” The basic concept can be illustrated in Fig. 2. The fuz-
zy integral measurement model needs not assume independency
among alternatives; it can, therefore, be used in nonlinear
situations.
4.3. Proposed procedures
The expert group should follow the following five-step proce-
dures to the evaluation of favorable supplier in SCMS with
uncertainties.
Step 1: building up a network framework. As the subjective pref-
erence of every decision maker is different and the judgments
made will not be completely identical, Saaty (1996) suggested an
integration of decision makers’ preferences with the geometric
mean by establishing a pairwise comparison matrix for each com-
ponent, which needs to conform to the characteristic of positive
reciprocal matrix.
Step 2: calculating the relative weight for criteria and attributes.
This study calculates the relative weights of the criteria with
dependent relationships. The pairwise comparison matrices for
evaluation criteria are established. Then the maximum eigenvalues
and the corresponding eigenvectors of the pairwise comparison
matrices are calculated to generate the supermatrix.
Step 3: limiting the weighted supermatrix for the weigh. The un-
weighted supermatrix contains the priorities derived from the
Fig. 2. The basic concept of choquet integral.
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pairwise comparisons of the elements. In an unweighted superm-
atrix, its columns may not be column stochastic. To obtain a sto-
chastic matrix (i.e., each column sums to one), multiply the
blocks of the unweighted supermatrix by the corresponding cluster
priority. The supermatrix must satisfy the principle of column sto-
chastic, which means every column should add up to 1. Based on
the rule of ANP (Saaty, 1996), the decision makers believe that if
the column stochastic is not conformed, then the matrix weightsof the shadow area are 0.5 and the remaining matrix weights
would add up to 0.5.
Step 4: calculating the weights of all attributes. After obtaining
the relative weights of all criteria, integrating the evaluation by
multiplying the score of each criterion with the relative attributes
weight. Eventually, the attributes weight of hierarchical structure
can be generated based on the scores.
Step 5: calculating the final weights of each supplier. Using Eqs.
()(5)–(9) to solve the proposed SCMS model with non-additive fuz-
zy integral needs not assume mutual independence of criteria. It
can be applied to nonlinear cases. Even if any two criteria are
objectively and mutually independent, they are not considered
independent of subjective evaluators.
5. Empirical evaluation of case firm
This section aims to operationalize the proposed hierarchical
evaluation framework to evaluate an optimal alternative supplier
for the case firm. There are some reasons. First, the case firm has
to constantly improve its manufacturing processes while facing
challenges as to how they manage the SCMS in the competitive
and changeable environments. Second, the case firm has to keep
reforming the SCMS in the industrial sector to cope with market
competition and customer requirements. The expert opinions are
obtained from the expert group, composed of five professors and
six senior management staff, with extensive experience consulting
in this study.
5.1. Case firm
Under the prosperous and booming electronic consumption
products and network markets, Taiwan plants of COM Co. Ltd. were
built for IC substrates and entering IC packing field to meet the cus-
tomer demand in related products in 1998. Today, COM is the larg-
est professional PCB manufacturer in Taiwan; it is also ranked as
number six worldwide. To offer the best services for electronic
manufacturers, COM has been constantly developing new genera-
tion technology, enhancing competitiveness, fully satisfying the
market and customer demands, and building up closer relation-
ships with its suppliers and customers. COM, insisting on the prin-
ciple of ‘‘Highest Quality, Customer First,” has continually spent a
lot of effort on improving processes, developing the SCMS and set-ting up full quality system to meet customer requirements. Due to
the quick replacement of electronic products and rapid exploration
of new technologies, the R&D for COM is leading its competitors so
as to meet product demands from customers and explore new
products in markets. COM’s operational principles are focused on
prompting technology development, developing closer relation-
ships with upper- and down-stream of industry, and committing
the customer-demand satisfaction. The SCMS is relatively impor-
tant for COM to sustain in facing an ever-increasingly competitive
and changeable environment.
The expert group with eleven members strived to recommend
the SCMS criteria and attributes, which are expected to remain
long-term competition in intensively competitive markets. The
group members reviewed the SCMS criteria and attributes becauseSCMS is one of the most prioritized issues of the management
team. They have the same need to find a suitable supplier in SCMS
for COM; namely, to evaluate and select the most proper supplier
in SCMS, and to make this selection more logically and persua-
sively. For better handling of this MCDM problem, the eleven
experts’ management group should adopt possible solutions and
criteria and attributes of SCMS which cover the five criteria men-
tioned above.
5.2. The results
The objective of this empirical study is to demonstrate how ANP
and choquet integral can be used to determine the best supplier
selection prior to SCMS criteria and attributes. The expert group
followed the five-step procedures.
Step 1: building up a network framework. An expert committee
for group knowledge is formed. Relevant information from the case
firm is collected to evaluate the advantages and disadvantages and
to monitor the results to ensure the objectives canbe achieved. The
relevant information consists of five criteria and eighteen attri-
butes, most of which are determined from extensive literature
review.
Step 2: calculating the relative weight for criteria and attributes. In
the formation of a pairwise comparison matrix, group decision-
making is used to avoid the biased attitude of the decision maker
towards a particular supplier. A series of pairwise comparisons
are made to the importance of determinants in achieving objec-
tives. Table 1 shows the pairwise comparison of criteria under
determinant (U1) along with the eigenvector (local priority vector),
also known as e-vector. Decomposing k by Eqs. (1)–(3), one obtains
k1 =À7.778, k2 = À3.395, k3 =À0.981, k4 = 0.386, and k5 = 8.245.
The evaluation matrices are also reported. The kmax is equal to
8.245. The consistency of the pairwise judgment of each compari-
son matrix is also checked by consistency index and consistency
ratio, both should be less than 0.1. Table 1 presents CI = 0.035
and CR = 0.024, therefore the results are acceptable and consistent.
This computational results for e-vector are U1(0.53, 0.22, 0.70,
0.41, 0.11) and the normalized results are U1(0.27, 0.11, 0.35,0.21, 0.006). Repeat all the process of pair comparisons, the nor-
malized results under determinants U2 and U3 are, respectively,
U2 (0.26, 0.01, 0.16, 0.24, 0.32) and U3 (0.08, 0.22, 0.11, 0.14,
0.45). These computational results are for the composition of
unweighted supermatrix as shown in Table 3.
Table 2 reports the pairwise comparison of determinants under
criteria (C1). By decomposing k one would obtain k1 =À3.78,
k2 =À1.25and k3 = 8.049. The kmax is equal to 8.049. As shown in Ta-
ble2, CI = 0.007 and CR = 0.005 suggestingthe resultsare acceptable
Table 1
Pairwise comparison matrix for criteria in determinant (U1).
U1 C1 C2 C3 C4 C5 e-Vector WeightU1
C1 1 2 5/6 5 1/2 1 3/8 3 0.53 0.27
C2 2 5/6 1 2 3/5 3 4/5 2 0.22 0.11
C3 5 1/2 2 3/5 1 4 2/7 4 4/5 0.70 0.35
C4 1 3/8 3 4/5 4 2/7 1 3 1/3 0.41 0.21
C5 3 2 4 4/5 3 1/3 1 0.11 0.06
kmax = 8.245; CI = 0.035; CR= 0.024
Table 2
Pairwise comparison matrix for determinants in criteria (C1).
C1 U1 U2 U3 e-Vector WeightC1
U1 1 3 4/7 4 1/2 0.76 0.47
U2 3 4/7 1 2 1/3 0.29 0.18
U3 4 ½ 2 1/3 1 0.58 0.36
kmax = 0.007; CI = 0.007; CR = 0.005
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and consistent. The computational results for e-vector are C1(0.76,
0.29, 0.58) and the normalized results are C1(0.47, 0.18, 0.36). Re-
peated all the process of pair comparisons, the normalized results
for the remaining criteria are C2 (0.45, 0.26, 0.29), C3 (0.47, 0.15,
0.38), C4 (0.47, 0.11, 0.41) and C5 (0.47, 0.16, 0.36), respectively.Again, these computational results are for the composition of un-
weighted supermatrix as shown in Table 3.
Step 3: limiting the weighted supermatrix for the weigh. The out-
comes of the process in Table 3 form the unweighted supermatrix,
which is for interdependency among determinants and SCMS. Its
columns contain the priorities derived from pairwise comparisons
resulted from Tables 1 and 2. In an unweighted supermatrix, its
columns may not be column stochastic. To obtain a stochastic ma-
trix, i.e., each column sums to one, one can multiply the blocks of
the unweighted supermatrix by the corresponding cluster priority.
Raise the supermatrix to a large power to capture first-, second-,
and third-degree influences. If the differences between corre-
sponding elements of a column are less than a very small number,
for successive powers of the supermatrix using Eq. (4), the process
has converged. To derive the overall priorities of elements, we need
to multiply submatrices numerous times, in turn, until the col-umns stabilize and become identical in each block of submatrices.
Because our model involves inner dependences between determi-
nants and criteria, it requires adjusting the unweighted superma-
trix to make it column stochastic. Then, the weighted
supermatrix (Table 4) can be raised to limiting powers to calculate
the priority weights.
The supermatrix is made to converge to obtain a long-term sta-
ble set of weights, shown in Table 4. For convergence to occur, the
supermatrix needs to be column stochastic. In other words, the
sum of each column of the supermatrix needs to be one. The con-
verged supermatrix presents the results of the relative importance
measures for determinants and SCMS.
The elements of the supermatrix have been imported from the
pairwise comparison matrices of interdependencies, shown in
Table 5. As there are still five such pairwise comparison matrices,
one for each interdependent attribute in the criteria, therefore,
there will be non-zero columns in this supermatrix. Each of the
non-zero values in a column is the relative importance weight
associated with the interdependent pairwise comparison matrices.
The supermatrix is made to converge to obtain a stable set of
weights. The converged supermatrix is shown in Table 6.
Step 4: calculating the weights of all attributes. The local priority
weights of criteria can be obtained from the integration of determi-
nants and criteria, and the priority weights of attribute are from
the converged supermatrix. The integration weight results are
through normalization process. The resulted global priority
weights are shown in Table 7.
Step 5: calculating the final weights of each supplier. The choquet
integral provides with functionality and reliability for determiningbest alternatives by solving k-fuzzy measure, where the k values
are limited to [0,1]. The choquet integral (c )R
h d g in Eqs. (1), (3)–
(9) is employed to obtain the aggregated value for each criteria
based on attributes. The aggregated values of R
h d g for the four
alternatives are then used for case firm to select its suppliers in
SCMS and determinants. The ranking order of aggregated value
when k % 1 for these four alternative suppliers is that supplier B
(8.648) is suitable for its present requirements. Note that there is
Table 5
Supermatrix of attributes in interdependency relationships before convergence.
A11 A12 A13 A14 A21 A22 A23 A24 A31 A32 A33 A41 A42 A43 A44 A51 A52 A53
A11 0.00 0.49 0.31 0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A12 0.41 0.00 0.21 0.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A13 0.40 0.21 0.00 0.39 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A14 0.47 0.13 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A21 0.00 0.00 0.00 0.00 0.00 0.57 0.23 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A22 0.00 0.00 0.00 0.00 0.53 0.00 0.26 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A23 0.00 0.00 0.00 0.00 0.52 0.29 0.00 0.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A24 0.00 0.00 0.00 0.00 0.56 0.26 0.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A31 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.78 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A32 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.81 0.00 0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.83 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.54 0.30 0.16 0.00 0.00 0.00
A42 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.41 0.00 0.43 0.16 0.00 0.00 0.00
A43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.55 0.25 0.00 0.20 0.00 0.00 0.00
A44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.54 0.28 0.18 0.00 0.00 0.00 0.00
A51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.80 0.20
A52 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.75 0.00 0.25
A53 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.86 0.14 0.00
Table 4
Converged supermatrix for interdependency among uncertainty and SCMS.
Determinants SCM strategy
U1 U2 U3 C1 C2 C3 C4 C5
Determinants
U1 0.47 0.47 0.47 0.00 0.00 0.00 0.00 0.00U2 0.17 0.17 0.17 0.00 0.00 0.00 0.00 0.00
U3 0.36 0.36 0.36 0.00 0.00 0.00 0.00 0.00
SCMS
C1 0.00 0.00 0.00 0.20 0.20 0.20 0.20 0.20
C2 0.00 0.00 0.00 0.14 0.14 0.14 0.14 0.14
C3 0.00 0.00 0.00 0.23 0.23 0.23 0.23 0.23
C4 0.00 0.00 0.00 0.19 0.19 0.19 0.19 0.19
C5 0.00 0.00 0.00 0.24 0.24 0.24 0.24 0.24
Table 3
The unweighted supermatrix for interdependency among determinants and SCM
strategy.
Determinants SCM strategy
U1 U2 U3 C1 C2 C3 C4 C5
Determinants
U1 0.00 0.00 0.00 0.47 (Table 2) 0.45 0.47 0.47 0.47
U2 0.00 0.00 0.00 0.18 0.26 0.15 0.11 0.16
U3 0.00 0.00 0.00 0.36 0.29 0.38 0.41 0.36
SCMS
C1 0.27 (Table 1) 0.26 0.08 0.00 0.00 0.00 0.00 0.00
C2 0.11 0.01 0.22 0.00 0.00 0.00 0.00 0.00
C3 0.35 0.16 0.11 0.00 0.00 0.00 0.00 0.00
C4 0.21 0.24 0.14 0.00 0.00 0.00 0.00 0.00
C5 0.06 0.32 0.45 0.00 0.00 0.00 0.00 0.00
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unchanged ranking order of aggregated value when k % 0. The final
results are showed in Table 8.
6. Managerial implications
The proposed hierarchical evaluation framework has been
effectively applied to select the optimal supplier in SCMS for the
case PCB manufacturing firm. Especially, it has provided managers
and researchers with better understanding of the differences in
SCMS activity needs and specific management interventions by
examining the eighteen attributes. These attributes serve as a
bridging mechanism, which is very helpful in SCMS. It can also pro-
vide other PCB manufacturing firms with a mechanism to monitor
and establish measurement platform of SCMS.
In order to examine if the evaluation of suppliers is effective in
SCMS, this study further conducted a post-survey discussion with
the SCM expert group. The discussion results are summarized as
follows. First, it is a common understanding that the purposes of
SCMS often emphasize the expectation of improving performance.The expert group chose an optimal supplier with determinants of
supply uncertainty containing quality, timeliness, and inspection
requirements of the suppliers, which are most concerned by the
case firm.
Secondly, the expert group chose the satisfying customer needs
that contain purchasing function formally written with long-range
plan (A31) and direct linking computers to computers with key
suppliers (A51) to be the most important attributes. This choice
Table 6
Supermatrix of attributes in interdependency relationships after convergence.
A11 A12 A13 A14 A21 A22 A23 A24 A31 A32 A33 A41 A42 A43 A44 A51 A52 A53
A11 0.30 0.23 0.24 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A12 0.30 0.23 0.24 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A13 0.30 0.23 0.24 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A14 0.30 0.23 0.24 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A21 0.00 0.00 0.00 0.00 0.35 0.30 0.19 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A22 0.00 0.00 0.00 0.00 0.35 0.30 0.19 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00A23 0.00 0.00 0.00 0.00 0.35 0.30 0.19 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A24 0.00 0.00 0.00 0.00 0.35 0.30 0.19 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A31 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.45 0.38 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A32 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.45 0.38 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.45 0.38 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.28 0.25 0.14 0.00 0.00 0.00
A42 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.28 0.25 0.14 0.00 0.00 0.00
A43 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.28 0.25 0.14 0.00 0.00 0.00
A44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.28 0.25 0.14 0.00 0.00 0.00
A51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.44 0.38 0.18
A52 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.44 0.38 0.18
A53 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.44 0.38 0.18
Table 7
Integrated the priority weights of criteria and attributes for Choquet integral.
Criteria Attributes Incumbent
weights
Local
priority
Global
priority
C1 0.200 A11 0.30 0.06 0.30 0.06
A12 0.23 0.05 0.23 0.05
A13 0.24 0.05 0.24 0.05
A14 0.24 0.05 0.24 0.05
C2 0.136 A21 0.35 0.05 0.35 0.07
A22 0.30 0.04 0.30 0.06
A23 0.19 0.03 0.19 0.04
A24 0.17 0.02 0.17 0.03
C3 0.233 A31 0.45 0.11 0.45 0.09
A32 0.38 0.09 0.38 0.08
A33 0.17 0.04 0.17 0.03
C4 0.187 A41 0.33 0.06 0.33 0.07
A42 0.28 0.05 0.28 0.06
A43 0.25 0.05 0.25 0.05A44 0.14 0.03 0.14 0.03
C5 0.244 A51 0.44 0.11 0.44 0.09
A52 0.38 0.09 0.38 0.08
A53 0.18 0.04 0.18 0.04
Table 8
Choquet integral computation of overall weight index for alternatives.
Attributes Global priority S1 S2** S3 S4
Value (c)R
h d g (k-value) Value (c)R
hd g (k-value) Value (c)R
h d g (k-value) Value (c)R
h d g (k-value)
A11 0.06 – 8.552 (1.00)
8.549 (0.00)
– 8.684** (1.00)
8.682** (0.00)
– 8.216 (1.00)
8.213 (0.00)
– 8.315 (1.00)
8.312 (0.00)A12 0.05 0.108 0.095 0.108 0.108
A13 0.05 0.175 0.140 0.153 0.153
A14 0.05 0.235 0.210 0.200 0.200
A21 0.07 0.272 0.298 0.237 0.266
A22 0.06 0.348 0.374 0.271 0.322
A23 0.04 0.436 0.410 0.361 0.371
A24 0.03 0.472 0.469 0.437 0.407
A31 0.09 0.521 0.507 0.471 0.495
A32 0.08 0.577 0.567 0.537 0.571
A33 0.03 0.606 0.600 0.612 0.600
A41 0.07 0.672 0.690 0.648 0.670
A42 0.06 0.720 0.766 0.737 0.729
A43 0.05 0.767 0.800 0.793 0.767
A44 0.03 0.800 0.866 0.842 0.800
A51 0.09 0.876 0.922 0.871 0.890
A52 0.08 0.966 0.971 0.940 0.966
A53 0.04 1.000 1.000 1.000 1.000
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is sensible because satisfied IT is important in central point of
SCMS activities, which should aim primarily to take good care of
their IT ability.
Thirdly, many works on SCM suggested that a sound SCMS
should be a broader consideration in many criteria, which should
integrate all attributes as the ideal form of SCMS, rather than an
attribute along. However, the expert group remarked on the merits
of the proposed solutions. Unlike a traditional hierarchical modelbased on the linear and piecemeal approach, the Modified Feedback
System model of the proposed evaluation framework is novel since
it is based on a complex interrelationship and intertwining among
criteria and attributes. It is favorable to use ANP to handle the prob-
lem of inner dependences since it can provide more valuable infor-
mation for decision-making (Wu, 2008; Tseng et al., 2008).
In broader sense, the proposed hierarchical evaluation frame-
work in SCMS can also be used as an analytical monitoring tool
to further developing or constructing an overall supplier evalua-
tion. For the practice of management, SCMS is sufficient for organi-
zational managers to better understand the relevant criteria and
attributes. Moreover, the managers are able to capture a fairly
complete picture of SCMS while assessing the relative performance
of the components developed, validated, and operationalized by
this approach.
7. Conclusions
This study has contributed to, in particular, the SCM literature
by: (i) proposing a research framework that relates determinants
to SCMS under uncertainty, (ii) developing valid and reliable mea-
sures for the dimensions based on expert’s qualitative opinion, and
(iii) developing a SCMS hierarchical analytical structure to evalu-
ate the optimal supplier using ANP and fuzzy integral. In practical
SCMS problems, vast amounts of criteria and attributes are typi-
cally interactive and interdependent and with elusive qualitative
information. This study developed an effective evaluation frame-
work to select the most appropriate supplier in SCMS. The pro-posed framework incorporated a hierarchical MCDM structure,
ANP and choquet integral, wherein MCDM is to select the most
appropriate alternative from a finite set of alternatives with refer-
ence to multiple conflicting criteria, ANP is to simultaneously con-
sider the relationships of feedback and dependence of criteria, and
choquet integral is to eliminate the interactivity of expert subjec-
tive judgment problems. The proposed evaluation framework has
been validated with its effectiveness and simplicity in selecting
the optimal supplier in SCMS for the case firm. The ANP is a rela-
tively new MCDM method which can deal with many interactions
systematically, particularly the SCMS evaluation problems. More-
over, the ANP can be used not only as a way to handle the inner
dependences within a set of criteria/ attributes, but also as a
way of producing more valuable information for decision-making.The whole supply chain members can apply the proposed frame-
work to evaluate and determine firms’ optimal suppliers in
uncertainty.
Some directions for future study are proposed here. Develop-
ment of a richer, multi-hierarchical structure that incorporates
other criteria/attributes with quantitative measurement, not only
the qualitative measurement in this study, deserves further explo-
ration. To prevent the information bias, future study might involve
the fuzzy set theory in when, especially, qualitative measures are
involving imprecision, constraints, and possible actions that are
not precisely in description (Bellman & Zadeh, 1970). Furthermore,
the uncertain environment is highly affected by subjective judg-
ments, which is vague and imprecise in nature (Al-Najjar & Als-
youf, 2003). Modeling the imprecise and uncertain problemswith proper mathematical tools, such as fuzzy logic (Zadeh,
1975), also deserves attempting to account for the vagueness in
the SCMS evaluation process.
References
Al-Najjar, B., & Alsyouf, I. (2003). Selecting the most efficient maintenance approachusing fuzzy multiple criteria decision making. International Journal of ProductionEconomics, 84(1), 85–100.
Anderson, M. G., & Katz, P. B. (1998). Strategic sourcing.International Journal of
Logistics Management, 9(1), 1–13.Beamon, B. (1999). Measuring supply chain performance. International Journal of
Operations and Production Management, 19(3), 275–292.Bellman, R., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment.
Management Science, 17 , 141–164.Carr, A. S., & Pearson, J. N. (1999). Strategically managed buyer–supplier
relationships and performance outcomes. Journal of operations management,17 (5), 497–519.
Carr, A. S., & Pearson, J. N. (2002). The impact of purchasing and supplierinvolvement on strategic purchasing and its impact on firm’s performance.International Journal of Operations and Production Management, 22(9),1032–1053.
Carr, A. S., & Smeltzer, L. R. (1999). The relationship of strategic purchasing tosupply chain management. European Journal of Purchasing and SupplyManagement, 5, 43–51.
Chan, F. T. S., & Kumar, N. (2007). Global supplier development considering riskfactors using fuzzy extended AHP-based approach. Omega, 35(4), 417–431.
Chen, I. J., & Paulraj, A. (2004). Towards a theory of supply chain management: The
constructs and measurements. Journal of Operations Management, 22(2),119–150.
Chen, Y. W., & Tzeng, G. H. (2001). Using fuzzy integral for evaluating subjectivelyperceived travel costs in a traffic assignment model. European Journal of Operational Research, 130, 653–664.
Chenhall, R. H., & Langfield-Smith, K. (1998). The relationship between strategicpriorities, management techniques and management accounting: An empiricalinvestigation using a system approach. Accounting, Organization and Society,
23(3), 243–264.Chiang, J. H. (2000). Aggregating membership values by a Choquet-fuzzy-integral
based operator. Fuzzy Sets and Systems, 11(4), 367–375.Choi, T. Y., & Hartley, J. L. (1996). An exploration of supplier selection practices
across the supply chain. Journal of Operations Management, 14(4), 333–343.Chou, S. Y., & Chang, Y. H. (2008). A decision support system for supplier selection
based on a strategy-aligned fuzzy SMART approach. Expert systems withapplications, 34(4), 2241–2253.
Cooper, M. C., Ellram, L. M., Gradner, J. T., & Hanks, A. M. (1997). Meshing multiplealliances. Journal of Business Logistics, 18(1), 67–89.
Cousins, P. D. (1999). Supply base rationalization: Myth or reality? European Journal
of Purchasing and Supply Management, 5, 143–155.Cox, J. F., III, & Blackstone, J. H. (1998). Apics dictionary (9th ed.). VA: Falls Church.Davis, T. (1993). Effective supply chain management. Sloan Management Review,
34(4), 35–46.Day, G. S. (2000). Managing market relationships. Journal of the Academy of
Marketing Science, 28(1), 24–30.Dehning, B., Richardsonand, V. J., & Zmud, R. W. (2007). The financial performance
effects of IT-based supply chain management systems in manufacturing firms. Journal of Operations Management, 25(4), 806–824.
Dreyer, B., & Gronhaug, K. (2004). Uncertainty, flexibility, and sustained competitiveadvantage. Journal of Business Research, 57 , 484–494.
Dyer, J. H. (1996). How Chrysler created an American keiretsu. Harvard BusinessReview, 74(4), 42–56.
Dyer, R. F., & Forman, E. H. (1992). Group decision support with the analytichierarchy process. Decision Support Systems, 8(2), 99–124.
Fisher, M. L. (1997). What is the right supply chain for your product? HarvardBusiness Review, 75(2), 105–116.
Flint, D. J. (2004). Strategic marketing in global supply chain: Four challenges.
Industrial Marketing Management, 33, 45–50.Fuentes-Fuents, M. M., Albacete-Sae, C. A., & Llorens-Montes, F. J. (2004). The impactof environmental characteristics on TQM principles and organizationalperformance. The International Journal of Management Science, 32, 425–442.
Grabisch, M., & Nicolas, J. (1994). Classification by fuzzy integral: Performance andtests. Fuzzy Sets and Systems, 65, 255–273.
Hahn, C. K., Watts, C. A., & Kim, K. Y. (1990). The supplier development program: Aconceptual model. International Journal of Purchasing and Materials Management,
26 (2), 2–7.Handfield, R. B., & Nichols, E. L. (1999). Introduction to supply chain management .
USA: Prentice-Hall.Hoque, Zahirul (2004). A contingency model of the association between strategy,
environmental uncertainty and performance measurement: Impact onorganizational performance. International Business Review, 13(4), 485–502.
Hwang, A. S. (1998). Designing a customer-focusedworkshop for strategic planning. Journal of Management Development, 17 (5), 338–350.
Johnston, D. A., McCutcheon, D. M., Stuart, F. I., & Kerwood, H. (2004). Effects of supplier trust on performance of cooperative supplier relationships. Journal of Operations Management, 22(1), 23–38.
Kahraman, C., Çevik, S., Ates, N. Y., & Gülbay, M. (2007). Fuzzy multi-criteriaevaluation of industrial robotic systems, 52(4), 414–433.
10 M.-L. Tseng et al. / Computers & Industrial Engineering xxx (2009) xxx–xxx
ARTICLE IN PRESS
Please cite this article in press as: Tseng, M.-L., et al. Selection of optimal supplier in supply chain management strategy ... Computers &
Industrial Engineering (2009), doi:10.1016/j.cie.2008.12.001
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http://slidepdf.com/reader/full/three-novel-methods-to-predict-traffic-time-series-in-reconstructed-state-spaces 11/11
Kathuria, R. (2000). Competitive priorities and managerial performance: Taxonomyof small manufacturers. Journal of Operation Management, 18, 627–641.
Keller, J., Gader, P., Tahani, H., Chiang, J. H., & Mohamed, M. (1994). Advances infuzzy integration for pattern recognition. Fuzzy Sets and Systems, 65, 273–283.
King, J. (1996). Supply and demand. Computer World, 30(6), 45.Klir, G. J., Wang, Z., & Harmanec, D. (1997). Constructing fuzzy measures in expert
systems. Fuzzy Sets and Systems, 92, 251–264.Krause, D. R., & Ellram, L. M. (1997). Critical elements of supplier development.
European Journal of Purchasing and Supply Management, 3(1), 21–31.Krause, D. R. (1999). The antecedents of buying firms’ efforts to improve suppliers.
Journal of Operations Management, 17 (2), 205–224.Lado, A. A., Boyd, N. G., & Wright, P. (1992). A competency-based model of
sustainable competitive advantage: Toward a conceptual integration. Journal of Management, 18, 77–91.
Levitt, T. (1985). Globalization of markets. New York: John Wiley & Sons.Li, K., Ganesan, V. K., & Sivakumar, A. I. (2006a). Scheduling of single stage assembly
with air transportation in a consumer electronic supply chain. Computers andIndustrial Engineering, 51(2), 264–278.
Li, S., Ragu-Nathan, B., Ragu-Nathan, T. S., & Rao, S. S. (2006b). The impact of supplychain management practices on competitive advantage and organizationalperformance. Omega, 34(2), 107–124.
Magretta, J. (1998). The power of virtual integration an interview with Dellcomputers’ Michael Dell. Harvard Business Review, 76 (2), 72–84.
Margaret, N. A. (1996). Manufacturing competitive priority and productivity: Anempirical study. International Journal of Operations and Production Management,16 , 8–12.
Mendoza, Luis E., María Pérez, A. M., & Grimán, A. C. (2007). Critical success factorsfor a customer relationship management strategy. Information and SoftwareTechnology, 49
(8), 913–945.Morgan, J., & Monczka, R. M. (1996). Supplier integration: A new level of supplychain management. Purchasing, 120(1), 110–113.
Murofushi, T., & Sugeno, M. (1989). An interpretation of fuzzy measures and theChoquet integral as an integral with respect to a fuzzy measure. Fuzzy Sets andSystems, 29(2), 201–227.
Palmer, J. W., & Griffith, D. A. (1998). Information intensity: A paradigm forunderstanding web site design. Journal of Marketing Theory and Practice, 6 (3),38–42.
Saaty, T. L. (1980a). The analytic hierarchy process—Planning, priority setting, resourceallocation. NY: McGraw-Hill.
Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process. Pittsburgh, PA: RWS Publications.
Saaty, T. L. (1980b). The analytic hierarchy process. NY: McGraw-Hill Book Co.Seo, Y. (2006). Controlling general multi-echelon distribution supply chains with
improved reorder decision policy utilizing real-time shared stock information,51(2), 229–246.
Sousa, R. (2003). Linking quality management to manufacturing strategy: Anempirical investigation of customer focus practices. Journal of Operations
Management, 21(1), 1–18.Stanley, L. L., & Wisner, J. D. (2001). Service quality along the supply chain:
Implications for purchasing. Journal of Operations Management, 19(3), 287–306.Stuart, F. I. (1997). Supply chainstrategy: Organizational influence through supplier
alliances. British Academy of Management, 8(3), 223–236.
Sugeno, M., & Terano, T. (1977). A model of learning lased on fuzzy information.Kybernetes, 6 , 157–166.
Sugeno, M. (1974). Theory of fuzzy integrals and its application. PhD thesis, TokyoInstitute of Technology, Tokyo.
Tan, K. C. (2001). A framework of supply chain management literature. European Journal of Purchasing and Supply Management, 7 , 39–48.
Tan, K. C., Kannan, V. R., & Handfield, R. B. (1998). Supply chain management:Supplier performance and firm performance. International Journal of Purchasing and Material Management, 34(3), 2–9.
Tan, K. C., Lyman, Steven B., & Wisner, Joel D. (2002). Supply chain management: A
strategic perspective. International Journal of Operations & ProductionManagement, 22(5/6), 614–632.
Tseng, M. L., Lin, Y. H., Chiu, A. S. F., & Liao, C. H. (2008). Using FANP approach onselection of competitive priorities based on cleaner productionimplementation: A case study in PCB manufacturer, Taiwan. Clean Technologyand Environmental Policy, 10(1), 17–29.
Van Hoek, R. (1998). Reconfiguring the supply chain to implement postponedmanufacturing. International Journal of Logistics Management, 9(1), 95–110.
Wang, H. S., & Che, Z. H. (2007). An integrated modelfor supplier selectiondecisionsin configuration changes. Expert Systems with Applications, 32(4), 1132–1140.
Wang, Z., & Klir, G. J. (1992). Fuzzy measure theory. NY: Plenum PublishingCorporation.
Wang, Z., Leung, K. S., & Wang, J. A. (1999). Genetic algorithm for determining non-additive set functions in information fusion. Fuzzy Sets and Systems, 102(3),463–469.
Ward, P. T., Duray, G. K., Leong, G. K., & Sum, C. (1995). Business environment,operations strategy, performance: An empirical study of Singaporemanufacturers. Journal of Operations Management, 13, 99–115.
Watts, C. A., Kim, Y. K., & Hahn, C. (1992). Linking purchasing to corporatecompetitive strategy. International Journal of Purchasing and MaterialsManagement, 28(4), 2–8.
Webster, J. (1995). Network of collaboration or conflict? Electronic data interchangeand power in the supply chain. Journal of Strategic Information Systems, 4(1),31–42.
White, R. E., Pearson, J. N., & Wilson, J. R. (1999). JIT manufacturing: A survey of implementations in small and large US manufacturers. Management Science,45(1), 1–15.
Wilson, H. N., & McDonald, M. H. B. (1996). Computer-aided marketing planning:The experience of early adopters. Journal of Marketing Management, 12(5),391–415.
Wu, W. W. (2008). Choosing knowledge management strategies by using acombined ANP and DEMATEL approach. Expert Systems with Applications,
35(3), 828–835.Xia, W., & Wu, Z. (2007). Supplier selection with multiple criteria in volume
discount environments. Omega, 35(5), 494–504.Yao, Y., Palmer, J., & Dresner, M. (2007). An inter-organizational perspective on the
use of electronically-enabled supply chains. Decision Support Systems, 43(3),
884–896.Yeh, C. H., Deng, H., & Pan, H. (1999). Multi-criteria analysis for dredger dispatching
under uncertainty. Journal of the Operational Research Society, 50(1), 35–43.Zadeh, L. A. (1975). The concept of a linguistic variable and its application to
approximate reasoning. Information Sciences, 9, 43–80.
M.-L. Tseng et al. / Computers & Industrial Engineering xxx (2009) xxx–xxx 11
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