Use of fuzzy AHP for evaluatingthe benefits of

information-sharing decisions in asupply chain

Selcuk PercinDepartment of Business Administration,

The Faculty of Economics and Administrative Sciences,Karadeniz Technical University, Trabzon, Turkey

Abstract

Purpose – The purpose of this paper is to provide a good insight into the use of fuzzy AnalyticalHierarchy Process (fuzzy AHP) approach that is a multi-criteria decision-making methodology inevaluating the benefits of information-sharing decision problems.

Design/methodology/approach – In this study, the integration of AHP with the fuzzy syntheticextent analysis method (fuzzy AHP) is proposed in evaluating the benefits of information-sharingdecision problems as a framework to guide managers.

Findings – Findings demonstrate that the customer requirement and operational informationalternatives are the preferred key decisions, which all supply chain partners might agree to share withone another. Further, it can also be concluded that the planning and financial information alternativeshave almost the same importance.

Research limitations/implications – Fuzzy AHP is a highly complex methodology and requiresmore numerical calculations in assessing composite priorities than the traditional AHP and hence itincreases the effort. In addition, fuzzy methodology could be extended with the other multi-criteriadecision-making (MCDM) methods such as Analytical Network Process (ANP), TOPSIS, ELECTREand DEA techniques in solving such a problem.

Originality/value – There is a lack of research in the literature to deal directly with the uncertaintyof human judgements in evaluating the benefits of various information-sharing decisions in a supplychain. Therefore, fuzzy AHP is an appropriate methodology to select the various types of informationand has the ability to be used as a decision-making analysis tool since it handles uncertain andimprecise data. In addition, the paper is especially of interest to managers as they make decisions onwhich types of information they should share with their supply chain partners.

Keywords Supply chain management, Analytical hierarchy process, Decision making

Paper type Research paper

1. Introduction and backgroundSupply chains are now more aptly described as “supply networks”, or “supply webs”and can involve extremely complex configurations. These complex configurations,which are usually ranging from the dyadic chain to the multi-channel network(Samaddar et al., 2006; McLaren et al., 2002; Huang et al., 2003), require differentinformation needs and create different environments for information sharing.

There are many factors that may affect the information-sharing capabilities ofpartners, such as product and market structure, inter-organizational informationsystems (IOISs) infrastructure, relationships among partners, intra-organizational

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Journal of Enterprise InformationManagement

Vol. 21 No. 3, 2008pp. 263-284

q Emerald Group Publishing Limited1741-0398

DOI 10.1108/17410390810866637

coordination structures (centralization, decentralization and distributed), and singleand multiple-sourcing (Shore and Venkatachalam, 2003). However, evaluating thesefactors require focus on two key parameters. They include collaborative relationshipsand IOISs that contribute information-sharing (Lee and Whang, 2000; Shore andVenkatachalam, 2003).

Supply networks heavily depend on participants’ relationships andinformation-sharing capabilities between the various organizations. The closeness ofrelationships between the various parties through the network may range fromadversarial relationships to partnership or collaborative relationships (Mentzer et al.,2000b). However, collaborative relationships are characterized by greater informationexchange than adversarial relationships. This increases the need for integration andcoordination of activities across organization boundaries. Thus, collaborative supplynetworks bridge the barrier between partners by focusing on the efficient exchange ofinformation.

Collaborative supply networks are examining the value of information-sharing notonly for opportunities to reduce inventory level, and supply chain costs but also foropportunities to improve the flow of goods, services and information and serve theircustomers better, which benefits the overall network (McLaren et al., 2002; Mentzeret al., 2000a; Li et al., 2006; Samaddar et al., 2006). However, a critical issue is how muchinformation can be shared between partners of a supply network. Even though, IOISs,such as extranets, electronic data interchange (EDI), and electronic marketplacesprovide the ability to share information easily, firms may not share information forvarious reasons (Premkumar, 2000). Information-sharing brings concerns of security,privacy, costs, and intellectual property (Li et al., 2006). Therefore, there are strongdisincentives to share information unless supply chain managers are able tounderstand that shared information is equally beneficial to all parties of the supplynetwork.

A number of studies have attempted to evaluate the information-sharing problemswith the use of various theoretical and analytical based models. In the theoreticalstudies, total benefits of information-sharing decisions in collaborative supplynetworks are generally derived by using IOISs. Li et al. (2006) studied the effect ofIOISs strategies on firm level performance under both stable and volatile marketconditions. Samaddar et al. (2006) presented a theoretical framework to contribute thecurrent research on inter-organizational information-sharing and design of supplynetworks. Premkumar (2000) provided an integrated perspective of supply chainmanagement and inter-organizational systems and showed that all organizations in thesupply chain could gain benefits from sharing information by reduction of supply anddemand uncertainties. Huang et al. (2003) reviewed the impacts of sharing informationon supply chain dynamics and concluded that the leveraging information can bebeneficial to all supply chain partners involved. Anand and Mendelson (1997)discussed the relationship between intra-organizational coordination structures andinformation-sharing and applied it to a firm that faces demand uncertainty in multiplehorizontal markets. In the analytical studies, some of the mathematical approaches aredeveloped to understand the behavior of the established models and the effects ofinformation-sharing on supply chain performance. In a statistical/probabilisticapproaches by Lee et al. (1997a, 1997b) they concluded that sharing real demandinformation across the supply chain members reduces the demand variability

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(bullwhip effect) and uncertainty. Yu et al. (2001) investigated the benefits of supplychain partnerships with information-sharing and concluded that both the retailer andmanufacturer can obtain performance improvement with an increasing level ofinformation-sharing. Moinzadeh (2002) studied the benefits of information-sharing in asupply chain and showed how the supplier can benefit from using information about theretailer’s inventory levels. In addition, Chen (1998), Cachon and Fisher (2000), Lee et al.(2000), and Gavirneni et al. (1999) used stochastic and capacitated models to evaluate thesupply chain performance with emphasizing on the benefits of information-sharing. Inthese studies, researchers have investigated various types of information such ascapacity, inventory levels, demand, and costs. Recently, researchers have attempted touse simulation (Zhao and Xie, 2002; Zhao et al., 2002) and fuzzy based decision makingapproaches (Shore and Venkatachalam, 2003; Chantrasa, 2005) in order to evaluate theinformation-sharing potential of supply chain partners.

It can be seen from the literature that most of the analytical studies are at conceptuallevel, and focused mainly on movement of materials and inventory cost savings throughcollaborative supply networks with optimization and simulation of activities. In addition,most of the studies focused only on sharing of specific type of information such asdemand forecast, inventory information, and operational information. However, manyinformation-sharing decision problems can not be expressed by analytical models andalgorithms, or they may lack complete or certain data. In addition, as shown in thepublished literature, there is a lack of research to deal directly with evaluating thebenefits of various types of information shared by the participating firms. Theavailability of multiple criteria and the involvement of decision makers will expandinformation-sharing decision problems from one to several dimensions, and hence it willincrease the complexity and effort. For this reason, we need a new approach, which couldhandle multi-criteria decision making (MCDM) problems, and to support these types ofcomplex evaluation problems (Buyukozkan, 2004). Therefore, the integration ofAnalytical Hierarchy Process (AHP) with the fuzzy synthetic extent analysis method(fuzzy AHP) (Chang, 1992; 1996) is proposed. Fuzzy AHP is a relatively newmethodology introduced by Van Laarhoven and Pedrycz (1983) that extends the AHP fordecision making to cases conducted in uncertain and fuzzy environment. So, it has theability to deal with the uncertainty of human judgments in evaluating the benefits ofinformation-sharing decisions in a supply chain. Thus, the objective of this paper is topresent the employment of the fuzzy AHP approach to evaluate the benefits ofinformation-sharing decision problems. In addition, understanding the benefits ofinformation-sharing may help managers to make decisions on which types ofinformation they should share with their supply chain partners. Furthermore, the use ofproposed model is illustrated in Turkish manufacturing firms.

The paper is organized in five sections. The importance of selection criteria andinformation-sharing decisions in a supply chain is explained first. The third sectiondescribes briefly the fuzzy AHP methodology. The fourth section discusses theapplication of the fuzzy AHP to information-sharing decisions. Finally, the last sectioncontains some concluding remarks and perspectives.

2. Selection criteria and information-sharing decisionsEvaluating the benefits of information-sharing decisions is not a well defined orstructured problem in literature. As stated earlier, apart from the research studies on

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information-sharing decisions to date, to our knowledge, only Chantrasa (2005) andShore and Venkatachalam (2003) proposed a fuzzy based decision making approach forinformation-sharing decisions. However, evaluating the benefits ofinformation-sharing decisions has some special characteristics. First, the benefits ofinformation-sharing decisions are intangible in nature. Second, different decisionmakers assign the benefits of information-sharing decisions and their importancedifferently. For this reason, these benefits can only be measured subjectively.Therefore, an appropriate evaluation methodology and evaluation criteria have to beidentified.

In this paper, fuzzy AHP methodology is used to identify evaluation criteria forinformation-sharing decision problem. For this purpose, we have conducted a literaturesurvey (mainly based on the studies of Muckstadt et al., 2001; Huang et al., 2003;Angerhofer and Angelides, 2006; Samaddar et al., 2006). Then, three different levels ofinformation-sharing benefits criteria, namely strategic, managerial, and operationalbenefits, are identified through the related references, which will be detailed in thefollowing parts. The benefits criteria are further refined by interviewing a group of 13managers (i.e. decision makers) from different Turkish manufacturing firms regardingthe logistics, production or information technology management. Then, the focus shiftsto partners and their willingness to reveal which information should be shared in orderto provide long-term success for collaborative relationships. Four decision alternatives,namely operational, planning, customer requirement, and financial information, arechosen to evaluate information-sharing decisions. Then, the influences of variousbenefits criteria on the goal criteria have been evaluated. The goal of our framework isto evaluate the degree to which types of information should be shared with supplychain partners for Turkish manufacturing firms.

2.1 Strategic benefits (SB)Strategic benefits accrue over an extended period of time, and capture the long-termbenefits of information-sharing. It requires an assessment of the direct gains arisingfrom collaboration, market share, conflict resolution, and new product introduction.

2.1.1 Facilitate supply chain collaboration (FC):. Information is a basic enabler fortight coordination (Lee and Whang, 2000). Information-sharing through collaborativesupply networks creates profits, strengthens competitive position and enhances thevalue of the company (Lee, 2000).

2.1.2 Increase market share (IMS). Increasing market share will become moreimportant because of the increasing trend toward collaborative relationships beingdriven by IOISs.

2.1.3 Enhance conflict resolution (EC). Organizations in a supply chain can workclosely for the same goal only if the associated risks and benefits of informationintegration efforts are equitably shared (Lee, 2000). With the increasing trust anddependency, partners can gain more sharing of information, ideas, and technology(Mentzer et al., 2000a).

2.1.4 Increase new product introduction (INP). As product life cycles shorten, theright products must be developed and successfully launched in ever shortertimeframes in order to remain competitive (Lambert and Cooper, 2000). Thus, thesuccess of the company depends on effective management across the supply chain ofboth new product introduction and old product phase-out (Lee, 2000).

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2.2 Managerial benefits (MB)Managerial benefits arise from planning issues that are related to medium term (abouttwo weeks to six months) (Huang et al., 2003). We use gains in communication,capacity allocation, and make better decision on forecasting, planning and resourcecontrol as the key measures of managerial benefits.

2.2.1 Increase communication (IC). Achieving supply chain efficiency requiressharing of accurate and timely information (Lee, 2000). Advances in informationtechnology and IOISs accelerate real-time information-sharing, collaboration, anddecision making among companies.

2.2.2 Increase capacity allocation decision (CD). By using the shared information,each supply chain partner can make better decisions on capacity allocation so that thesupply chain dynamics can be optimized (Huang et al., 2003).

2.2.3 Make better decision on forecasting, planning and resource control (FPR).Collaborative supply networks involve joint decision making among the partners in theareas of collaborative planning, forecasting and resource control (McLaren et al., 2002).The flow of enhanced information through the collaborative networks enables firms todecrease information delay and to improve forecast accuracy (Angerhofer andAngelides, 2006).

2.3 Operational benefits (OB)Operational benefits deal with daily events in a supply chain. The availability oflead-time, cost, inventory, and scheduling information is crucial to gain operationalbenefits.

2.3.1 Reduce inventory level (IL). The sharing of inventory information may lead tolower inventories and inventory costs and at the same time increased flexibility(Angerhofer and Angelides, 2006).

2.3.2 Reduce lead time (LT). It defines the time that is required from the time anorder has begun its production until the time the order is read for shipment. Thegeneral conclusion from the literature is that the longer the lead time, the smaller thebenefit of information-sharing (Huang et al., 2003).

2.3.3 Reduce supply chain costs (SC). Shorter product life cycles and greater productvariety increases the supply chain costs. However, enhanced collaboration andinformation-sharing can lower supply chain related costs and improve responsivenesswithin a chain of organizations.

2.3.4 Improve production/distribution scheduling (IPD). Collaboration and enhancedinformation-sharing improves joint planning of production/distribution schedulingthat creates sustainable value for all involved (McLaren et al., 2002).

2.4 Information-sharing decisionsAn important characteristic of any collaborative relationships is the amount and typeof information that is shared between the partners. Lee et al. (2000) listed a number ofgeneral classifications of information that are currently being shared across a widerange of industries and firms. These include the inventory level/position, salesdata/demand information, order status for tracking/tracing, sales forecast, andproduction/delivery schedule. Huang et al. (2003) proposed six categories of productioninformation include product, process, resource, inventory, order, and planning in theanalysis of information sharing.

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However, the types of information included in the analysis have been grouped intofour categories: operational, planning, customer requirement, and financialinformation. Operational information involves determining production schedules,order status for tracking/tracing, return status, volume of operations and inventorylevels. Planning type of information is comprised of sales forecasts, sales data/demandinformation, promotional plans and production plans. Customer requirementinformation includes a clear statement of the customer requirements relating toproduct desires, customer satisfaction, product availability, service requirements,delivery, and invoices, etc. Financial performance indices are very importantinformation measures that allow companies to know the real benefits of sharinginformation (Huang et al., 2003). The common financial information measures are salesgrowth rate, profit, and return on investment, etc.

We can present the links between the benefits of information-sharing andinformation-sharing decision alternatives. In using AHP to model a decision problem,the first step is to structure the hierarchy. The goal of our model is to evaluate thebenefits of information-sharing decisions in a supply chain. The second level shows thecriteria that must be satisfied to fulfill the overall goal. The general criteria levelinvolved three major criteria: strategic benefits, managerial benefits, and operationalbenefits. Each of these in turn needed further decomposition into specific items in thethird level. As an example, strategic benefits was decomposed into four sub-criteria,which are facilitate supply chain collaboration, increase market share, enhance conflictresolution, and increase new product introduction. We also located increasecommunication, increase capacity allocation decision, and make better decision onforecasting, planning, and resource control in the third level of the hierarchy undermanagerial benefits. The four sub-criteria were also included for operational benefits inthe third level. These are reducing inventory level, reducing lead time, reducing supplychain costs, and improving production/distribution scheduling. The lowest level of thehierarchy comprised of the decision alternatives. In this study, the differentinformation-sharing decision alternatives are operational information, planninginformation, customer requirement information, and financial information. To showthe problem of evaluating the benefits of information-sharing decisions, we make useof the hierarchy illustrated with Figure 1.

3. Fuzzy AHP methodologyThe paper proposes a fuzzy AHP approach to represent decision makers’ comparisonjudgments and to decide the final priority of different information-sharing decisionalternatives. In the following, the literature review on fuzzy AHP methodology is firstlygiven and then the extent analysis method on fuzzy AHP that will be used for theapplication of this method on information-sharing decision problem is presented.

3.1 Literature review for fuzzy AHPFuzzy AHP methodology is designed to an alternative selection and justificationproblem by integrating the concept of fuzzy set theory and hierarchical structureanalysis. The use of fuzzy methodology allows the decision maker to incorporate bothqualitative and quantitative data into the decision model. For this reason, decisionmakers usually feel more confident to give interval judgments rather than fixed valuejudgments. In this approach, triangular fuzzy numbers are used for the preferences of

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one criterion over another, and then by using the extent analysis method, the syntheticextent value of the pairwise comparison is calculated.

The earliest work in fuzzy AHP appeared in Van Laarhoven and Pedrycz (1983),which compared fuzzy ratios described by triangular membership functions. Later,using geometric mean, Buckley (1985) determined fuzzy priorities of comparison ratioswhose membership functions were trapezoidal. By modifying the Buckley’s (1985)method, Boender et al. (1989), presented a more robust approach to the normalization ofthe local priorities. According to the Boender et al. (1989), the triangular approximationof fuzzy operations provides fuzzy solutions with much smaller spread than Buckley’s(1985) method. After that, Ruoning and Xiaoyen (1992) constructed the fuzzy judgmentmatrix by using continuous judgment scale and emphasizing that every element of thismatrix can be presented by a positive bounded closed fuzzy number. Chang (1996)introduced a new approach for handling fuzzy AHP, with the use of triangular fuzzynumbers for pairwise comparison scale of fuzzy AHP and the use of extent analysismethod for the synthetic extent values of the pairwise comparisons. Cheng (1996)proposed another algorithm for evaluating naval tactical missile systems by fuzzyAHP based on grade value of membership function. Kahraman et al. (1998) developed afuzzy weighted evaluation method using objective and subjective measures. Deng(1999) presented a fuzzy approach for dealing with qualitative multi-criteria analysisproblems in a simple and straightforward manner. Lee et al. (1999) introduced theconcept of comparison interval scales and proposed a methodology based on stochasticoptimization to achieve global consistency and to accommodate the fuzzy nature of thecomparison process. Cheng et al. (1999) proposed a new method for evaluating weaponsystems by AHP based on the linguistic variable weight. Zhu et al. (1999) discussed theextent analysis method and applied some practical examples of fuzzy AHP. Leung andCao (2000) proposed a fuzzy consistency definition with consideration of a tolerancedeviation for alternatives in fuzzy AHP. More recently, Kuo et al. (2002) developed a

Figure 1.The hierarchy of theinformation sharing

decision problem

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decision support system for locating a new convenience store. Mikhailov (2002) appliedthe AHP method in conjunction with fuzzy preference programming approach forpartnership selection problem in establishment of virtual enterprises. Yu (2002)incorporated an absolute term linearization approach and a fuzzy rating expression intoan AHP-goal programming (GP) model for solving group decision making fuzzy AHPproblems by employing the property of GP to treat a fuzzy AHP problem (Buyukozkan,2004). Recently, fuzzy AHP has been extensively applied in the literature. Someexamples of these applications include computer integrated manufacturing systemsjustification and selection, quality function deployment, catering service companiesevaluation, e-marketplace selection, software development strategy selection, newproduct development process, technology management, project risk evaluation, andglobal supplier selection (Bozdag et al., 2003; Kwong and Bai, 2003; Kahraman et al.,2004; Buyukozkan, 2004; Buyukozkan et al., 2004; Buyukozkan and Feyzioglu, 2004;Erensal et al., 2006; Tuysuz and Kahraman, 2006; Chan and Kumar, 2007).

3.2 Extent analysis method on fuzzy AHPThe outlines of the extent analysis method on fuzzy AHP (Chang, 1992, 1996; Zhu et al.,1999) can be summarized as follows:

Let X ¼ {x1, x2, . . . , xn} be an object set, and U ¼ {u1, u2, . . . , um} be a goal set.According to the Chang’s extent analysis method, each object is taken and extentanalysis for each goal gi is performed, respectively. Therefore, m extent analysisvalues for each object can be obtained and shown as follows:

M 1gi; M

2gi; . . . ; M

mgi ; i ¼ 1; 2; . . . ; n ð1Þ

where all the Mjgi (j ¼ 1; 2, . . . ,m) are triangular fuzzy numbers (TFNs) whose

parameters are l, m, and u. They are the least possible value, the most possible value,and the largest possible value respectively. A TFN is represented as (l,m,u). The stepsof the extent analysis method can be given as follows (Buyukozkan, 2004):

3.2.1 Step 1. The value of fuzzy synthetic extent with respect to the ith object isdefined as:

Si ¼Xmj¼1

Mjgi^

Xni¼1

Xmj¼1

Mjgi

" #21

ð2Þ

To obtainXmj¼1

Mjgi , we perform the fuzzy addition operation of m extent analysis values

for a particular matrix such that:

Xmj¼1

Mjgi ¼

Xmj¼1

lij;Xmj¼1

mij;Xmj¼1

uij

!ð3Þ

and to obtainPn

i¼1

Pmi¼1M

jgi

h i21, we perform the fuzzy addition operation of

Njgi ð j ¼ 1; 2; . . . ; mÞ values such that:

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Xni¼1

Xmi¼1

Mjgi ¼

Xni¼1

lij;Xni¼1

mij;Xni¼1

uij

!ð4Þ

where li ¼Xmj¼1

ly; mi ¼Xmj¼1

mij; ui ¼Xmj¼1

uij

Then, the inverse of the vector in equation (5) is computed as:

Xni¼1

Xnj¼1

Mjgi

" #21

¼1Xn

i¼1

ui

;1Xn

i¼1

mi

;1Xn

i¼1

li

0BBBB@

1CCCCAwhere;ui; mi; li . 0 ð5Þ

Finally, to obtain the Si in equation (2), we perform the following multiplication:

Si ¼Xmj¼1

Mjgi^

Xni¼1

Xnj¼1

Mjgi

" #21

¼Xmj¼1

lij £1Xn

i¼1

mi

;Xmj¼1

uij £1Xn

i¼1

li

0BBBB@

1CCCCA ð6Þ

3.2.2 Step 2. The degree of possibility of M 2 ¼ ðl2, m2, u2) $ M 1 ¼ ðl1, m1, u1) isdefined as:

V ðM 2 $ M 1Þ ¼y$x

sup½min ðmM 2ð yÞÞ�: ð7Þ

which can be expressed equivalently as follows:

V ðM 2 $ M 1Þ ¼ hgtðM 1 >M 2Þ ¼ mM 2ðd Þ ¼

1 if m2 $ m1

0 if l1 $ l2ðl12u2

ðm2¼u2Þ2ðm1¼u1Þ; otherwise

8>><>>: ð8Þ

where d is the ordinate of the highest intersection point D between mM 1and mM 2

>(see Figure. 2). To compare M1 and M2, we need both the values of V(M1 $ M2) andV(M2 $ M1). The intersection between M1 and M2 is shown in Figure 2.

3.2.3 Step 3. The degree possibility for a convex fuzzy number to be greater than kconvex fuzzy numbers Mi(i ¼ 1, 2, . . . , k) can be defined by:

V ðM $ M 1; M 2; . . . ; MkÞ ¼ V ½ðM $ M 1Þ and ðM $ M 2Þ and . . . and ðM

$ MkÞ� ¼ minV ðM $ MiÞ; i ¼ 1; 2; . . . ; k: ð9Þ

Assume that:

D0ðSiÞ ¼ minV ðSi $ SkÞ ð10Þ

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For k ¼ 1, 2, . . . , n; k – i. Then the weight vector is given by:

W 0 ¼ D0ðS1Þ; D0ðS2Þ; . . . ; D

0ðSnÞÞT ð11Þ

where Si (i ¼ 1, 2, . . . , n) are n elements.2.2.4 Step 4. After normalization (the elements of each column is divided by the sum

of that column and the elements in each resulting row are added and this sum isdivided by the number of elements in the row), the normalized weight vectors areobtained as follows:

W ¼ ðDðS1Þ; DðS2Þ; . . . ;DðSnÞÞT ; whereW is not a fuzzy number: ð12Þ

The issue of consistency in fuzzy AHP is another subject that needs to be examined.The consistency index (CI) and consistency ratio (CR) are calculated as follows:

CI ¼lmax 2 nÞ

ðn2 1ÞandCR ¼ CI=RI ð13Þ

where lmax is the largest eigenvalue of the comparison matrix, n is the number of itemsbeing compared in the matrix, and RI is a random index. If the CR$ 0.10, the decisionmaker has to make the pairwise judgements again (Saaty, 1990).

4. Application of the fuzzy AHP to information-sharing decisionsIn this study, decision makers’ comparisons are described by linguistic terms, whichare expressed in triangular fuzzy numbers. The comparison of the importance of onecriteria, sub-criteria, or alternative over another can be done by with the help of thequestionnaire (Appendix 2). The responses collected from the questionnaires are inputto the fuzzy AHP model. Fuzzy AHP combines these comparisons obtained from theanswers’ averages to analyze criteria and alternatives weights. Based on this approach,the weight vectors are calculated, and then the normalized weight vectors can be

Figure 2.The intersection betweenM1 and M2

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determined. As a result, the evaluation matrices are obtained showing the final set ofscores of information-sharing decision alternatives. The method of calculating priorityweights of the different information-sharing decision alternatives using fuzzy AHP isdiscussed below.

4.1 Evaluation of criteria weightsIn order to perform a pairwise comparison among the parameters, the linguistic scalefor the triangular numbers shown in Figure 3 and fuzzy conversion scale given inTable I are used in our proposed model. Throughout this study, we have used fivemain linguistic terms to evaluate the importance of the benefits of information-sharingcriteria, sub-criteria, and also to rate the information-sharing decision alternatives: “EI:equally important”, “WMI: weakly more important”, “SMI: strongly more important”,“VSMI: very strongly more important”, and “AMI: absolutely more important”. Wehave also considered their reciprocals: “ALI: absolutely less important”, “VSLI: verystrongly less important”, “SLI: strongly less important”, and “WLI: weakly lessimportant”. These linguistic terms are chosen with the expectation that decisionmakers will feel more comfortable using such terms in their assessments. For example,someone may consider that element i is “very strongly more important” as comparedwith the element j under certain criteria; he/she may set aij ¼ ð2, 5/2, 3). If element j isthought to be “very strongly less important” than element i, the pair wise comparisonbetween j and i could be presented by using fuzzy number, aij ¼ ð1=u1, 1/m1,1=l1Þ ¼ ð1=3, 2/5, 1/2).

Linguistic scale for importance Triangular fuzzy scale Triangular fuzzy reciprocal scale

Equally important (EI) (1/2, 1, 3/2) (2/3, 1, 2)Weakly more important (WMI) (1, 3/2, 2) (1/2, 2/3, 1)Strongly more important (SMI) (3/2, 2, 5/2) (2/5, 1/2, 2/3)Very strongly more important (VSMI) (2, 5/2, 3) (1/3, 2/5, 1/2)Absolutely more important (AMI) (5/2, 3, 7/2) (2/7, 1/3, 2/5)

Table I.Triangular fuzzyconversion scale

Figure 3.The linguistic scale of the

triangular numbers forrelative importance (RI)

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To create pairwise comparison matrices, a group of 13 managers from differentTurkish manufacturing firms have been interviewed. Then, the fuzzy evaluationmatrix relevant to the goal has been obtained with the consensus of them and they arelocated to a spreadsheet as shown in Table II. Some examples of decision makers’answers in the form of linguistic expressions about the importance of the benefits ofinformation-sharing criteria are given in Appendix 2. In this appendix, we also explainhow we collected the inputs coming from the respondents’ answers. Furthermore, theconsistency of the pairwise comparison matrices were examined and it was determinedthat all the matrices were consistent.

By applying formula (2) given in Step 1:

SSB ¼ ð3:0; 4:0; 5:0Þ^ð1=12:5; 1=9:33; 1=7:17Þ

¼ ð0:24; 0:44; 0:70Þ

SMB ¼ ð2:0; 2:67; 3:5Þ^ð1=12:5; 1=9:33; 1=7:17Þ

¼ ð0:16; 0:20; 0:49

SOB ¼ ð2:17; 2:67; 4:0Þ^ð1=12:5; 1=9:33; 1=7:17Þ

¼ ð0:17; 0:29; 0:56Þ are obtained

Using these vectors and formula (8), we can calculate the following values:

V ðSSB ¼ SMBÞ ¼ 1:00; V ðSSB ¼ SOBÞ ¼ 1:00; V ðSMB ¼ SOBÞ ¼ 1:00;

V ðSOB ¼ SMBÞ ¼ 1:00; V ðSMB ¼ SSBÞ ¼ 0:63; and; V ðSOB ¼ SSBÞ ¼ 0:69:

Finally, by using formula (10), we obtain:

D0ðSBÞ ¼ VðSSB $ SMB; SOBÞ ¼ min ð1:00; 1:00Þ

¼ 1:00

D0ðMBÞ ¼ VðSMB $ SSB; SOBÞ ¼ min ð0:63; 1:00Þ

¼ 0:63

D0ðOBÞ ¼ VðSOB $ SSB; SMBÞ ¼ min ð0:69; 1:00Þ

¼ 0:69

Therefore, the weight vector is calculated as W 0 ¼ ð1:00, 0.63, 0.69)T. Afternormalization, the normalized weight vectors of objective with respect to the benefitscriteria SB, MB and OB from Table II is obtained as WObjective ¼ ð0:43, 0.27, 0.30)T.According to the answers by the decision makers; we conclude that strategic benefits

SB MB OB

Strategic benefits (1, 1, 1) (1, 3/2, 2) (1, 3/2, 2)Managerial benefits (1/2, 2/3, 1) (1, 1, 1) (1/2, 1, 3/2)Operational benefits (1/2, 2/3, 1) (2/3, 1, 2) (1, 1, 1)

Table II.The fuzzy evaluationmatrix with respect to thegoal

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and operational benefits are more important than managerial benefits. Moreover, wealso observe that operational benefits are more important than managerial benefits. Asa consequence, the strategic and operational benefits of information-sharing decisionscan result in much greater efficiency for ongoing success of supply chain partners. In asimilar fashion, the managers now compare the sub-criteria with respect to the maincriteria. First, they compare the sub-criteria of strategic benefits. Table III gives therelative importance of strategic benefits sub-criteria.

From Table III, we calculate SFC ¼ ð0:18, 0.36, 0.60), SIMS ¼ ð0:12, 0.25, 0.44),SEC ¼ ð0:10, 0.19, 0.33), SINP ¼ ð0:11, 0.22, 0.48), V(SFC $ SIMSÞ ¼ 1:00, V(SFC $

SECÞ ¼ 1:00, V(SFC $ SINPÞ ¼ 1:00, V(SIMS $ SFCÞ ¼ 0:70, V(SIMS $ SECÞ ¼ 1:00,V(SIMS $ SINPÞ ¼ 1:00, V(SEC $ SFCÞ ¼ 0:47, V(SEC $ SIMSÞ ¼ 0:78, V(SEC $

SINP Þ ¼ 0:88, V (SINP $ SFCÞ ¼ 0:68, V(SINP $ SIMSÞ ¼ 0:92, V(SINP $

SECÞ ¼ 1:00. Then, the normalized weight vector from Table III is calculated asWSB ¼ ð0:35, 0.25, 0.16, 0.24)T. Based on these results, we conclude that in order toincrease the benefits of strategic sub-criteria; facilitate supply chain collaboration andincrease market share appear to be more important than enhance conflict resolutionand increase new product introduction. This result shows that cost effective, timelyand reliable flows of materials, information and finance to satisfy customerrequirements and to increase market share motivates supply chain partners to shareinformation (Lee, 2000; Muckstadt et al., 2001). Now, the other two matrices relevant topairwise comparisons of the sub-criteria of managerial and operational benefits and,the relative importance of each matrix are given in Table IV and Table V, respectively.

FC IMS EC INP

Facilitate supply chain collaboration (1, 1, 1) (1, 3/2, 2) (3/2, 2, 5/2) (1, 3/2, 2)Increase market share (1/2, 2/3, 1) (1, 1, 1) (1, 3/2, 2) (1/2, 1, 3/2)Enhance conflict resolution (2/5, 1/2, 2/3) (1/2, 2/3, 1) (1, 1, 1) (1/2, 1, 3/2)Increase new product introduction (1/2, 2/3, 1) (2/3, 1, 2) (2/3, 1, 2) (1, 1, 1)

Table III.The relative importance

of strategic benefitssub-criteria

IC CD FPR

Increase communication (1, 1, 1) (1, 3/2, 2) (3/2, 2, 5/2)Increase capacity allocation decision (1/2, 2/3, 1) (1, 1, 1) (1, 3/2, 2)Make better decision on forecasting, planning andresource control (2/5, 1/2, 2/3) (1/2, 2/3, 1) (1, 1, 1)

Table IV.The relative importance

of managerial benefitssub-criteria

IL LT SC IPD

Reduce inventory level (1, 1, 1) (1, 3/2, 2) (1/2, 1, 3/2) (3/2, 2, 5/2)Reduce lead time (1/2, 2/3, 1) (1, 1, 1) (1/2, 1, 3/2) (1, 3/2, 2)Reduce supply chain costs (2/3, 1, 2) (2/3, 1, 2) (1, 1, 1) (1, 3/2, 2)Improve production/distribution scheduling (2/5, 1/2, 2/3) (1/2, 2/3, 1) (1/2, 2/3, 1) (1, 1, 1)

Table V.The relative importance

of operational benefitssub-criteria

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The normalized weight vector from Table IV is calculated as WMB ¼ ð0:53, 0.35,0.12)T. We can observe that for the managerial benefits of a company, increasecommunication and increase capacity allocation decisions play a much more importantrole than other criteria.

The normalized weight vector from Table V is calculated as WOB ¼ ð0:32, 0.25,0.28, 0.15)T. Consequently, we can deduce that the most important criteria for theoperational benefits of a company are reducing inventory level, reducing supply chaincosts and reducing lead-time. In Table VI, we present the composite priority weightsobtained by the evaluation of information-sharing benefits with respect to main criteriaand sub-criteria.

4.2 Evaluation of alternativesAt the following step of the evaluation procedure, the managers compare theoperational, planning, customer requirement and financial information alternativeswith respect to each of the sub-criteria separately. These results in the matrices areshown in Tables VII-XVII. As it seen in the Tables VIIXVII, operational, and except for

OP PD CRD FD WIMS

Operational (1, 1, 1) (2/5, 1/2, 2/3) (2/5, 1/2, 2/3) (3/2, 2, 5/2) 0.21Planning (3/2, 2, 5/2) (1, 1, 1) (2/3, 1, 2) (2/5, 1/2, 2/3) 0.26Customer requirement (3/2, 2, 5/2) (1/2, 1, 3/2) (1, 1, 1) (1, 3/2, 2) 0.31Financial (2/5, 1/2, 2/3) (3/2, 2, 5/2) (1/2, 2/3, 1) (1, 1, 1) 0.22

Table VIII.Evaluation of thealternatives with respectto increasing marketshare

OP PD CRD FD WFC

Operational (1, 1, 1) (1/2, 2/3, 1) (1/2, 1, 3/2) (2/5, 1/2, 2/3) 0.16Planning (1, 3/2, 2) (1, 1, 1) (1, 3/2, 2) (2/3, 1, 2) 0.30Customer requirement (2/3, 1, 2) (1/2, 2/3, 1) (1, 1, 1) (2/5, 1/2, 2/3) 0.19Financial (3/2, 2, 5/2) (1/2, 1, 3/2) (3/2, 2, 5/2) (1, 1, 1) 0.35

Table VII.Evaluation of thealternatives with respectto facilitating supplychain collaboration

Main criteria Local weights Sub-criteria Local weights

Strategic benefits 0.43 Facilitate supply chain collaboration 0.35Increase market share 0.25Enhance conflict resolution 0.16Increase new product introduction 0.24

Managerial benefits 0.27 Increase communication 0.53Increase capacity allocation decision 0.35Make better decision on forecasting, planningand resource control. 0.12

Operational benefits 0.30 Reduce inventory level 0.32Reduce lead time 0.25Reduce supply chain costs 0.28Improve production/distribution scheduling 0.15

Table VI.Composite priorityweights forinformation-sharingbenefits evaluationcriteria

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two sub-criteria with respect to reduce lead-time and improve production/distributionscheduling, customer requirement information alternatives show a good performancein terms of all criteria. Planning information alternative is the weakest except for threesub-criteria in which it shows a highest performance level. This means that managersconsider the operational and customer requirement information as being moreimportant than planning and financial information.

OP PD CRD FD WCD

Operational (1, 1, 1) (1/3, 2/5, 1/2) (1/2, 2/3, 1) (1, 3/2, 2) 0.18Planning (2, 5/2, 3) (1, 1, 1) (2/5, 1/2, 2/3) (2/5, 1/2, 2/3) 0.24Customer requirement (1, 3/2, 2) (3/2, 2, 5/2) (1, 1, 1) (1, 3/2, 2) 0.34Financial (1/2, 2/3, 1) (3/2, 2, 5/2) (1/2, 2/3, 1) (1, 1, 1) 0.24

Table XII.Evaluation of the

alternatives with respectto increasing capacity

allocation decision

OP PD CRD FD WIC

Operational (1, 1, 1) (3/2, 2, 5/2) (1/2, 2/3, 1) (1, 3/2, 2) 0.32Planning (2/5, 1/2, 2/3) (1, 1, 1) (2/5, 1/2, 2/3) (2/5, 1/2, 2/3) 0.05Customer requirement (1, 3/2, 2) (3/2, 2, 5/2) (1, 1, 1) (1, 3/2, 2) 0.38Financial (1/2, 2/3, 1) (3/2, 2, 5/2) (1/2, 2/3, 1) (1, 1, 1) 0.25

Table XI.Evaluation of the

alternatives with respectto increasing

communication

OP PD CRD FD WINP

Operational (1, 1, 1) (2/5, 1/2, 2/3) (1/2, 2/3, 1) (1/2, 2/3, 1) 0.15Planning (3/2, 2, 5/2) (1, 1, 1) (1, 3/2, 2) (1/2, 1, 3/2) 0.32Customer requirement (1, 3/2, 2) (1/2, 2/3, 1) (1, 1, 1) (1/2, 1, 3/2) 0.25Financial (1, 3/2, 2) (2/3, 1, 2) (2/3, 1, 2) (1, 1, 1) 0.28

Table X.Evaluation of the

alternatives with respectto increasing new product

introduction

OP PD CRD FD WEC

Operational (1, 1, 1) (2, 5/2, 3) (1, 3/2, 2) (3/2, 2, 5/2) 0.49Planning (1/3, 2/5, 1/2) (1, 1, 1) (1/2, 2/3, 1) (1/2, 2/3, 1) 0.05Customer requirement (1/2, 2/3, 1) (1, 3/2, 2) (1, 1, 1) (1, 3/2, 2) 0.29Financial (2/5, 1/2, 2/3) (1, 3/2, 2) (1/2, 2/3, 1) (1, 1, 1) 0.17

Table IX.Evaluation of the

alternatives with respectto enhancing conflict

resolution

OP PD CRD FD WFPR

Operational (1, 1, 1) (1, 3/2, 2) (1, 3/2, 2) (2, 5/2, 3) 0.41Planning (1/2, 2/3, 1) (1, 1, 1) (2/5, 1/2, 2/3) (2/5, 1/2, 2/3) 0.05Customer requirement (1/2, 2/3, 1) (3/2, 2, 5/2) (1, 1, 1) (1, 3/2, 2) 0.32Financial (1/3, 2/5, 1/2) (3/2, 2, 5/2) (1/2, 2/3, 1) (1, 1, 1) 0.22

Table XIII.Evaluation of the

alternatives with respectto making better

decisions on forecasting,planning, and resource

control

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4.3 Final scores of alternativesIn Tables XVIII-XX we present the last computations in order to obtain the alternativepriority weights of our information alternatives. This is accomplished by aggregatingthe weights over the hierarchy for each decision alternative. To do this, we multiply theweights along the path from the top of the hierarchy to a decision alternative, and then

OP PD CRD FD WIL

Operational (1, 1, 1) (3/2, 2, 5/2) (1/2, 2/3, 1) (1, 3/2, 2) 0.35Planning (2/5, 1/2, 2/3) (1, 1, 1) (1/3, 2/5, 1/2) (1/2, 2/3, 1) 0.00Customer requirement (1, 3/2, 2) (2, 5/2, 3) (1, 1, 1) (3/2, 2, 5/2) 0.47Financial (1/2, 2/3, 1) (1, 3/2, 2) (2/5, 1/2, 2/3) (1, 1, 1) 0.18

Table XIV.Evaluation of thealternatives with respectto reducing inventorylevel

OP PD CRD FD WLT

Operational (1, 1, 1) (1/2, 1, 3/2) (3/2, 2, 5/2) (2/5, 1/2, 2/3) 0.26Planning (2/3, 1, 2) (1, 1, 1) (2, 5/2, 3) (2, 5/2, 3) 0.45Customer requirement (2/5, 1/2, 2/3) (1/3, 2/5, 1/2) (1, 1, 1) (1/2, 2/3, 1) 0.00Financial (3/2, 2, 5/2) (1/3, 2/5, 1/2) (1, 3/2, 2) (1, 1, 1) 0.29

Table XV.Evaluation of thealternatives with respectto reducing lead time

OP PD CRD FD WSC

Operational (1, 1, 1) (3/2, 2, 5/2) (1/2, 2/3, 1) (1, 3/2, 2) 0.31Planning (2/5, 1/2, 2/3) (1, 1, 1) (2/5, 1/2, 2/3) (2, 5/2, 3) 0.21Customer requirement (1, 3/2, 2) (3/2, 2, 5/2) (1, 1, 1) (2, 5/2, 3) 0.48Financial (1/2, 2/3, 1) (1/3, 2/5, 1/2) (1/3, 2/5, 1/2) (1, 1, 1) 0.00

Table XVI.Evaluation of thealternatives with respectto reducing supply chaincosts

OP PD CRD FD WIPD

Operational (1, 1, 1) (1/2, 1, 3/2) (3/2, 2, 5/2) (1, 3/2, 2) 0.36Planning (2/3, 1, 2) (1, 1, 1) (3/2, 2, 5/2) (2, 5/2, 3) 0.42Customer requirement (2/5, 1/2, 2/3) (2/5, 1/2, 2/3) (1, 1, 1) (2/5, 1/2, 2/3) 0.00Financial (1/2, 2/3, 1) (1/3, 2/5, 1/2) (3/2, 2, 5/2) (1, 1, 1) 0.22

Table XVII.Evaluation of thealternatives with respectto improvingproduction/distributionscheduling

FC IMS EC INP Alternative priority weights

Weights 0.35 0.25 0.16 0.24AlternativesOperational 0.16 0.21 0.49 0.15 0.22Planning 0.30 0.26 0.05 0.32 0.26Customer requirement 0.19 0.31 0.29 0.25 0.25Financial 0.35 0.22 0.17 0.28 0.27

Table XVIII.Priority weights of thealternatives with respectto strategic benefits

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sum those results over all the different pathways to that decision alternative. Finally,the combination of weights for sub-criteria and alternatives is calculated to determinethe final priority weights for main criteria (Buyukozkan, 2004). The final score resultscan be observed from the final priority weights presented in Table XXI. Our mainresult is that customer requirement and operational information alternatives are thepreferred key decisions, which all supply chain partners might agree to share with eachother. Moreover, we can also conclude that the planning and financial information hasalmost the same importance.

5. ConclusionIn a competitive environment, the success of organizations will increasingly depend ontheir information to share with partners in their strategic decisions. However,managers are often uncertain about how to share the key information to enhance theirbusiness. This research proposes a methodology for both managers and a group oforganizations in a supply chain to make decisions on which types of information theyshould share with their partners.

Strategic Managerial Operational Final priority weights

Weights 0.43 0.27 0.30AlternativesOperational 0.22 0.28 0.32 0.27Planning 0.26 0.12 0.23 0.21Customer requirement 0.25 0.36 0.29 0.29Financial 0.27 0.24 0.16 0.23

Table XXI.Final scores of the

alternatives

IL LT SC IPD Alternative priority weights

Weights 0.32 0.25 0.28 0.15AlternativesOperational 0.35 0.26 0.31 0.36 0.32Planning 0.00 0.45 0.21 0.42 0.23Customer requirement 0.47 0.00 0.48 0.00 0.29Financial 0.18 0.29 0.00 0.22 0.16

Table XX.Priority weights of the

alternatives with respectto operational benefits

IC CD FPR Alternative priority weights

Weights 0.53 0.35 0.12AlternativesOperational 0.32 0.18 0.41 0.28Planning 0.05 0.24 0.05 0.12Customer requirement 0.38 0.34 0.32 0.36Financial 0.25 0.24 0.22 0.24

Table XIX.Priority weights of the

alternatives with respectto managerial benefits

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Fuzzy AHP allows managers to answer a variety of questions such as whichinformation has the biggest impact on evaluating the benefits of information-sharingdecisions or how to guide partners who want to improve shared information in acollaborative relationships area. Use of fuzzy AHP methodology offers a number ofbenefits. Firstly, it is a more systematic method than the other MCDM methods and itis more capable of capturing a human’s appraisal of ambiguity when complexmulti-criteria decision making problems are considered. Because of this ability,managers can use this method in making their strategic decisions in case of uncertainand imprecise data. Secondly, since humans are comparatively efficient in makingqualitative forecasting, fuzzy methodology is an excellent tool to handle qualitativeassessments about information-sharing decision problems. However, fuzzy AHP is ahighly complex methodology and requires more numerical calculations in assessingcomposite priorities than the traditional AHP and hence it increases the effort. Thatdrawback certainly limits its applicability to real world problems.

In this study, qualitative assessments of fuzzy AHP methodology are developedbased on integrating the total benefits of information-sharing with various types ofinformation for Turkish manufacturing firms. Our evaluation framework demonstratethat the customer requirement and operational information alternatives are thepreferred key decisions, which all supply chain partners might agree to share with eachother. Further, we can also conclude that the planning and financial informationalternatives have almost the same importance. As a result, successful organizationswill use these results in managing, benchmarking and continuously improving theirsupply chains.

For the future research, fuzzy methodology could be extended with the other MCDMmethods such as Analytical Network Process (ANP), TOPSIS, ELECTRE and DEA.These methods have been recently developed to use in a fuzzy environment. Furtherresearch may be the application of these methods to the information-sharing decisionproblems and the comparison of the results.

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Appendix 1. The definition of the triangular fuzzy number and fuzzy operationallawsA fuzzy number is a special fuzzy set F ¼ x [ RjmF(x)}, where x takes its values on the real lineR1: 2 1 , x , 1 and mF(x) is a continuous mapping from R1 to the close interval[0, 1]. Atriangular fuzzy number can be denoted as M ¼ ðl, m, u). Its membership function mM(x): ! [0,1] is equal to:

mM ðxÞ ¼

0; x , l or x . u

ðx2 l Þ=ðm2 l Þ l # x # m;

ðx2 uÞ=ðm2 uÞ m # x # u:

8>><>>:

where l # m # u, l and u stand for the lower and upper value of the support of M respectively,and m is the mid-value of M. when l ¼ m ¼ u, it is a non-fuzzy number by convention. The mainoperational laws for two positive triangular fuzzy numbers M1 and M2 are as follows(Triantaphyllou, 2000):

M 1 þM 2 ¼ ðl1 þ l2; m1 þm2; u1 þ u2Þ;

M 1^M 2 < ðl1l2; m1m2; u1u2Þ;

l^M 1 ¼ ðll1; lu1; lu1Þ; l . 0; l [ R;

M211 < ð1=u1; 1=m1; 1=l1Þ:

Appendix 2. QuestionnaireThe questionnaire is composed of questions relating to the benefits of information-sharing andinformation-sharing decision alternatives. The questionnaire was conducted with 30 of the top500 Turkish firms operating in various manufacturing industries within the city of Istanbul inTurkey. This sample was selected randomly from the database of Istanbul Chamber ofCommerce. Questionnaires were sent by fax to the selected companies. It was requested that thequestionnaire be completed by functional executives such as logistics manager, production

Use of fuzzyAHP

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manager and information technology manager. However, of the 30 questionnaires sent, 4 of thequestionnaires were returned after one follow-up. Then, managers of the selected companieswere invited to answer the questions by phone and provided with an explanation of the purposeof the study. With the collaboration of 9 managers, the overall responses were received for a netresponse rate of 43 percent (13/30), which was considered satisfactory for fuzzy AHP analysis.Then, the answers’ averages are computed and rounded to their closest linguistic scale (Table I)in order to analyze these data by using fuzzy AHP. This method is basically similar to the oneproposed by Erensal et al. (2006). A sample of questions from the questionnaire can be given asfollows:

If a criterion on the left is more important than the one on the right, put cross mark “X” to theleft of the equally important “EI” column, under the importance level (column) you prefer. On theother hand, if a criterion on the left is less important than the one on the right, put cross mark “X”to the right of the equally important “EI” column under the importance level (column) you prefer.

With respect to the overall goal “evaluating the benefits of information-sharing decisions in asupply chain”

Q1. How important is the strategic benefits (SB) when it is compared tomanagerial benefits (MB)?

Q2. How important is the strategic benefits (SB) when it is compared tooperational benefits (OB)?

Q3. How important is the managerial benefits (MB) when it is compared tooperational benefits (OB)?

The answers related for this sample questions are presented in Table AI.

About the authorSelcuk Percin holds a PhD from Ankara University, Ankara, Turkey. He is presently working asan Assistant Professor in the Faculty of Economics and Administrative Sciences, KaradenizTechnical University, Trabzon, Turkey. His research interests focus on business performance,supply chain management, structural equation modeling (Lisrel) and operations researchestechniques (AHP, DEA, Linear and mixed-integer programming, Goal programming, etc.). Hecan be contacted at: spercin@ktu.edu.tr

Answers AMI VSMI SMI WMI EI WLI SLI VSLI ALI

A1 XA2 XA3 X

Table AI.Answers to some of thesample questions fromthe questionnaire

JEIM21,3

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