An Application of an Integrated ANP-QFD Framework for Sustainable

22 Pages·2017·1.95 MB·English

by  Madjid Tavana

INTERNATIONALJOURNALOFLOGISTICS:RESEARCHANDAPPLICATIONS,2017
VOL.20,NO.3,254–275
http://dx.doi.org/10.1080/13675567.2016.1219702
An application of an integrated ANP–QFD framework for
sustainable supplier selection
Madjid Tavanaa,b, Morteza Yazdanic and Debora Di Capriod,e
aDistinguishedChairofBusinessSystemsandAnalytics,LaSalleUniversity,Philadelphia,PA,USA;bBusiness
InformationSystemsDepartment,FacultyofBusinessAdministrationandEconomics,UniversityofPaderborn,
Paderborn,Germany;cDepartmentofBusinessManagement,UniversidadEuropeadeMadrid,Madrid,Spain;
dDepartmentofMathematicsandStatistics,YorkUniversity,Toronto,Canada;ePoloTecnologicoIISSG.Galilei,
Bolzano,Italy
ABSTRACT ARTICLEHISTORY
This study provides a novel integrated multi-criteria decision-making Received10June2015
approach to sustainable supplier selection problems. Despite the large Accepted27July2016
supply chain management literature on green performance
KEYWORDS
measurement, the need for a systematic analysis of how specific
Sustainablesupplier
sustainable variables develop and affect each other remains mostly
selection;customer
overlooked. The proposed integrated framework allows for such an
requirement;technical
analysis. By combining analytic network process and quality function requirement;criteria
deployment,ourmodelidentifiesaclearhierarchicalstructureforallthe interdependency;
relevantsustainablefactorsandsub-factorswhileweightingthedecision hierarchicalstructure;long-
criteria based on the importance given to customer requirements. runplanning
Finally, suppliers are ranked using a multi-objective optimisation
procedure based on ratio analysis and weighted aggregated sum
product assessment. The proposed framework is used to analyse a case
study of a dairy company, but it can be easily implemented for supplier
selectionbyanyothercompanywithsimilarfeatures.
1. Introduction
The management of social and environmental issues and the necessity to address them in parallel
with the economic factors represents nowadays a top priority for managers and decision makers
(DMs) in many sectors such as manufacturing, business development, tourism and agriculture
(Amindoust et al. 2012). Such a synthesis of economic, environmental and social development is
generallyreferredtoassustainabledevelopmentorsustainability(Gauthier2005;DaiandBlackhurst
2012).
In supply chain management (SCM), sustainability applies to all relevant supply chain aspects:
product design, material purchasing, manufacturing processes, final product delivery and reusing/
recycling design (Sarkis 2006;Srivastava 2007; Lin and Ho2011).
Managers’ goal in sustainable SCM (SSCM) is the long-run improvement of the economic per-
formance of their organisation focusing on what is needed not only to survive but also to prosper
bothin thenear and the distant future.
SSCM requires themanagerstoundertake social andenvironmentalactivitiesthatcan improve
theeconomicperformanceavoidingthosenotdirectlyinlinewiththeorganisations’economicgoals
(Carter and Liane Easton 2011; Sancha, Longoni, and Giménez 2015). Moreover, supply chain
CONTACT MadjidTavana [email protected]
Supplementaldataforthisarticlecanbeaccessedat10.1080/13675567.2016.1219702.
©2016InformaUKLimited,tradingasTaylor&FrancisGroup
INTERNATIONALJOURNALOFLOGISTICS:RESEARCHANDAPPLICATIONS 255
managersmustbeawareoftheenvironmentalconcernsandsocialaspectswhichareaffected,posi-
tively or negatively, by the choices they make in terms of supplier selection and supplier develop-
ment, modal and carrier selection,vehicle routing, location and packaging.
Beingabletoselectanetworkofskillfulandsustainablesuppliersisfundamentalwhendesigning
newdevelopmentandmarketingstrategiesandmoreflexiblemodelsforthelong-termpoliciesofthe
organisation(DotoliandFalagario2012;Pitchipoo,Venkumar,andRajakarunakaran2013),whileit
isessentialwhenonlylimitedresourcesareavailable(CarterandLianeEaston2011).Thus,oneof
themainissuesinSSCM ishowtostructureandimplementaneffective supplier selectionprocess
(Bayrak, Celebi, and Taşkin 2007; Punniyamoorthy,Mathiyalagan, and Parthiban 2011).
Sustainable supplier selection problems (SSSPs) can be regarded as classical supplier selection
problems(SSPs)incorporatingenvironmentalandsocialfactorstoevaluateandrankthesuppliers’
performance in order for the managers to choose thebestsuppliers (Genovese et al. 2010).
Theproblemisthentodefineamethodflexibleenoughtoallowforanoverallevaluationascom-
prehensive and objective aspossibleof allthe available suppliers.
Inordertodoso,itisnecessarytoperformasystematicanalysisoftheinterdependenciesexisting
among the customer requirements (CRs) and the way they influence the technical requirements
(TRs),thatis,thedecisioncriteriatorankthesuppliers.However,thiskindofanalysisremainsover-
lookedintheliteraturegeneratingaresearchgapthataffectsnotonlythetheoreticalstudiesbutalso
themanagerial approaches to decision-making and long-run development planning.
1.1.Researchcontribution
Recently,therehasbeenasteadyincreaseinthenumberofstudiescombiningseveraltechniquesfor
assessingsuppliers’performance(Labib2011;Zhangetal.2012).Severalcontributionstosustainable
supplierselection(SSS)havebeenobtainedbyemployingmulti-criteriadecision-making(MCDM)
tools (see, among others, Bottani and Rizzi 2008; Chen and Wang 2009; Awasthi, Chauhan, and
Goyal 2010; Amindoust et al. 2012). However, developing decision-making approaches to SSS
throughintegratedmethodscanbeverychallengingsincethesinglemethodsinvolvespecificfunc-
tionsthatprovidestablesolutionsonlyifsuitablycombinedtogether.Thus,thetechniquesappliedin
the literature on green supplier selection mostly consist of fuzzy-based single model approaches
(Govindan et al. 2015).
Inordertoprovide a meaningful andpractical solution toSSSPs,we proposetheintegrationof
differentMCDMtools,namely,analytichierarchyprocess(AHP),analyticnetworkprocess(ANP),
qualityfunctiondeployment(QFD),multi-objectiveoptimisationbasedonratioanalysis(MOORA)
andweighted aggregatedsum product assessment (WASPAS).
Selecting the best suppliers is subordinated to both determining the degree of importance
(weight)oftheselectioncriteriaandevaluatingthesupplierswithrespecttothesecriteria(Ordoo-
badi2009;Amindoustetal.2012).Inthisstudy,thesegoalsareachievedthroughafive-phaseinte-
grated MCDM method. Firstly, the sustainable factors/criteria are defined. The main factors are
interpretedasCRsandusedtoidentifytheTRs.Secondly,ANPandQFDareintegratedtodetermine
theweightsofallthecriteriaintroduced.Thirdly,AHPisusedtoweightallsupplierswithrespectto
eachTR.Then,WASPASandMOORAareappliedtorankallthecandidatesuppliers.Finally,the
complexproportionalassessment(COPRAS)methodisusedtotestthevalidityandconsistencyof
therankings obtained.
From thetechnicalviewpoint, themain contributionof ourapproach is inthewayitcombines
ANPandQFD.Oneoftheessential tasksinMCDMmodellingisseekingnewandlogicalwaysto
weightdecisionfactors(attributes). Usually,fuzzylinguisticvariables,AHP,ANP,andentropyare
employed to determine the weights of all the factors. However, in many decision problems the
importance ofthedecision criteria isdictated bythestakeholders’satisfaction and customers’pre-
ferences.TheANP–QFDphaseoftheproposedmethodprovidesasimpletoimplementcustomer-
dependent weighting method for thedecision criteria.
256 M.TAVANAETAL.
The proposed frameworkisusedto analyse a case studyof a dairy company,but itcan be easily
implementedtosolveSSSPsbyanyothercompanywithsimilarfeatures.However,thequestionnaires
andproceduresshouldbeadaptedtothespecificcaseinordertoavoidopportunitycostsintermsof
efficiencylossesandforegoneprofitsderivingfromtheselectionoflessthanefficientsuppliers.
Theremainderofthispaperisorganisedasfollows:Section2providesaliteraturereviewonthe
MCDMmethodsusedinthelastdecadesinSCMtoaddressSSSPsandidentifythesustainablefac-
tors. Section 3 illustrates the research objectives and questions and introduces the proposed five-
phase integrated method. Section 4 shows the results obtained for the case study under analysis.
Finally, Section 5 draws the conclusions and presents the limitations of the proposed method.
The technical details of the MCDM tools used to assemble the model and some sample questions
from the questionnaires conducted inthe case study are available asOnline Appendices.
2. Literature review
Theexistingliteratureonsustainabledevelopmentofsupplierscoversboththeoretical/analyticalset-
tings and empirical ones.
2.1.MCDMapproachesto SSSPs
MCDM techniques, such as AHP, ANP, QFD and TOPSIS (Technique for Order ofPreference by
SimilaritytoIdealSolution),oftenmergedwithafuzzyapproach,havebeenextensivelyconsidered
inmany SSSPs.
TOPSIS(HwangandYoon1981)isapracticalandusefultechniquethatallowstorankandselect
alternatives based ontheirdistances from theideal and the negative ideal solutions.
Awasthi,Chauhan,andGoyal(2010)andGovindan,Khodaverdi,andJafarian(2013)validateda
fuzzy TOPSIS structure to list the best supplier under environmental variables. In particular,
Awasthi,Chauhan,andGoyal(2010)usedfuzzyTOPSIStoevaluatesuppliers’environmentalper-
formanceonthebasisofdecisioncriteriasuchastheusageofenvironmentallyfriendlytechnology,the
employmentofenvironmentallyfriendlymaterials,themanagementcommitmenttogreenpractices
andthepollutioncontrolinitiatives.Erol,Sencer,andSari(2011)proposedamulti-criteriaframework
based on fuzzy entropy and fuzzy multi-attribute utility (FMAUT) toevaluate sustainable suppliers’
performance in a supply chain. Amindoust et al. (2012) used fuzzy logic and a ranking method
based on fuzzy inference system (FIS) to handle the subjectivity of DMs’ assessments. Wen, Xu,
andWang(2013)proposedafuzzyintuitionisticTOPSISmodelforSSSinagroupdecisionenviron-
ment. Orji and Wei (2014) used Fuzzy logic, Decision-making Trial and Evaluation Laboratory
(DEMATEL)andTOPSIStoanalysesustainabilitycriteriaandselectthebestsustainablesupplier.
AHP is a well-known technique used in MCDM to identify and analyse the hierarchy of the
decision factors of a decision problem (Saaty 1980, 1986). ANP is a generalised form of AHP
enablingDMstodefineandstudythefunctionalinterdependencyexistingamongthedecisionfac-
torsbelongingbothtodifferentlevelsofahierarchyandtothesamelevel.(Saaty1996).Somegeneral
features of ANP can befound inthe Online AppendixA.
Leeetal.(2009)providedthefirstattemptstobridgethegapbetweentheabundantresearchexist-
ingonsupplierselectionandtheratherlimitedoneconcerningenvironmentalissues.Theyproposed
agreensupplier selectionmodelusing AHPwithinafuzzy approach.Asafollow-up,Büyüközkan
andÇifçi(2011)developed anovelapproach based onfuzzy ANPwithinamulti-persondecision-
makingschemawithincompletepreferencerelations.Moreingeneral,fuzzyAHP,fuzzyANPand
fuzzymulti-objectiveprogrammingtoolshavebeenwidelyusedtodetermineSSSstrategiestoapply
tocasestudiesofseveralindustries(Shawetal.2012;Azadniaetal.2013,2015;Kannanetal.2013).
QFDisastructuredandwell-knowncustomer-drivenproductdesignapproachwhosebasictask
istotranslateCRsintoTRs(AkaoandKing1990;Bevilacqua,Ciarapica,andGiacchetta2006;Zhang
andChu2011).IntroducedbyYojiAkaointhe1960sandfirstusedin1972atMitsubishiindustries,
this approach has been attentively considered in Japanese and American companies to improve
INTERNATIONALJOURNALOFLOGISTICS:RESEARCHANDAPPLICATIONS 257
product design and development (Luo, Tang, and Wang 2008; Li, Tang, and Luo 2010). For more
details on QFD, see the Online AppendixA.
Amongothers,Hoetal.(2012)usedaQFD-basedapproachtotranslatethecompanystakeholder
requirements into multiple criteria for supplier selection. Dursun and Karsak (2013) developed a
FamilyGroupDecisionMakingapproachusingQFD.Theproposedmethodologyinitiallyidentified
thefeaturesthataproductmustpossesstomeetthecustomerneeds(CNs)andthenestablishedthe
relevant supplier assessment criteria. Asadabadi (2014) presented a hybrid QFD-based approach
where the best supplier was selected considering the changes in CNs and the priorities derived
from the product requirements(PRs).
2.2.Data envelopment analysis approachesto SSSPs
Dataenvelopmentanalysis(DEA)(Charnes,Cooper,andRhodes1978)isanon-parametriclinear
programming technique for evaluating the relative efficiency of decision-making units rigorously
applied over the past decades by managers and practitioners to solve SSSPs. For instance, Shi
et al. (2015) showed that large Chinese conglomerates producing air conditioners, refrigerators,
washingmachines,andelectricbicyclescanevaluategreenandsustainablesuppliersusingsystema-
ticDEAformulationssuchasCharnes-Cooper-Rhodesmodelandsuperefficiencyapproaches.Bai
and Sarkis (2014) adopted a two-stage structure combining rough set theory and DEA to detect,
benchmarkandevaluatethesuppliers’relativeperformanceintermsofkeyperformanceindicators.
Dobos and Vörösmarty (2014) used a mixed DEA and a composite indicator method to rank the
decision-making units (suppliers) on the basis of sustainable performance indicators. Kumar, Jain,
andKumar(2014)proposedaunifiedgreenDEAmodeltorankefficientsustainablesuppliers.Mir-
hedayatian, Azadi, and Saen (2014) proposed a network DEA model for green SCM (GSCM)
accounting for dual-role factors, undesirable outputs, andfuzzy data. Azadietal. (2015)measured
the effectiveness, efficiency, and productivity of sustainable suppliers in a Resin Production Com-
pany incorporating a fuzzy DEA model.
2.3.Integrated and intelligent approaches to SSSPs
Alltheaforementionedmethodshavebeenwidelyusedformulti-factoranalysisandappliedtosup-
plierselection.Theyarerelativelyeasytouse,flexibleandlogicallyconsistent.However,inorderto
be successfully implemented, they also require some specific assumptions to be satisfied. For
instance,AHPandANPrelyonthepossibilityofdefiningacommonhierarchicalstructureforfac-
tors and decision criteria.
The integration of different MCDM tools allows to overcome the inadequacy of the individual
methods to deal with real-world limitations such as processing capacity, incomplete information
and fuzzy evaluations. In particular, combining AHP or ANP with other methods usually offers a
systematic and easy-to-implement approach to SSPs. See, among others, Cebi and Bayraktar
(2003), Ghodsypour and O’Brien (1998), Wang, Haung, and Dismukes(2004, 2005).
Several integrated models have been investigated to optimise supplier selection processes. The
most recent studies have focused on integrated analytic frameworks combining:
. AHPand/orANPwithQFD(Choy,Lee,andLo2004;Bhattacharya,Geraghty,andYoung2010;
Amin, Razmi, and Zhang 2011; Rajesh and Malliga 2013; Taghizadeh and Ershadi 2013; Dey,
Bhattacharya, and Ho2015);
. DEMATL, fuzzy ANP and fuzzy TOPSIS (Bayrak, Celebi, and Taşkin 2007);
. QFD and TOPSIS (Kumaraswamy et al. 2011).
Anumberofmulti-stakeholderandmulti-perspectiveapproacheshavebeenproposedforstrategic
supplierselection.Ho,Dey,andBhattacharya(2015)outlinethemostrecentandsignificantresults.
258 M.TAVANAETAL.
Finally,severalintelligent approachesandstrategy-orientedstudieshavebeenconsidered inthe
lastdecade.Forexample, ÇelebiandBayraktar (2008)exploredtheintegrationofDEAandneural
networksfortheevaluationofsuppliersunderincompleteinformation.Mahdiloo,Saen,andTavana
(2012)consideredreal-worldproblemswheretheinformationisnotreadilyavailableandproposeda
modelabletomeasuresuppliers’efficiencyalsointhepresenceofundesirableoutputsand/orwhen
input variables are missing or take non-positive values. Toloo and Nalchigar (2011) proposed an
integrated DEA model for determining the most efficient suppliers with imprecise data. Yeh and
Chuang(2011)usedamulti-objectivegeneticalgorithmtosolveanSSSPinaGSCMsystem.Validi,
Bhattacharya, and Byrne (2014, 2015) considered two case analyses of the sustainable food supply
chain distribution system. They combined TOPSIS with genetic algorithms such as NSGA-II
(Non-dominated Sorting Genetic Algorithm II), MOGA-II (Multiobjective Genetic Algorithm II)
andHYBRIDtodefineagreenmulti-objectiveoptimisationmodelminimisingboththeCO emis-
2
sions from transportation and thetotalcostsof the distribution chain.
InthisstudytheimportanceoftheTRs(i.e.decisioncriteria)isassumedtodependonthatofthe
CRs, that is, the weights assigned to the CRs influence those assigned to the TRs. In real-life situ-
ations, the weights of the TRs are assigned considering a series of technical difficulties and target
values that can be too complex to be analysed using a AHP comparison matrix. In other words, it
maybetoorestrictivetostructuretheCRsandTRsusingacommonhierarchy.Itfollowsourchoice
ofintegratingANPwithQFD.Intheproposedintegratedframework,ANPisusedtoweightonlythe
CRs.TheweightsoftheTRsareassignedafterwardssoastoreflecttheimportanceoftheCRsand
account for the issues relevantto stakeholders and customers’satisfaction.
Once the TRs are weighted, it is necessary to rank the suppliers. AHP can be applied to weight
eachsupplierwithrespecttoeachdecisioncriterion,butthenoneormoreMCDMtoolsareneeded
to evaluate the overall performance of the suppliers and validate the ranking process. We will use
WASPAS, MOORA and COPRAS since they are all based on a comparison matrix that can be
derived from AHP and are not complex from the computational viewpoint. However, other
MCDM tools could be implemented without changing the main objective of this study.
2.4.Sustainable factors forSSSPs
There are many studies aiming at identifying the main criteria for SSPs. The traditional supplier
selection approaches have considered economic attributes and factors like cost (Büyüközkan and
Çifçi 2011; Punniyamoorthy, Mathiyalagan, and Parthiban 2011; Amindoust et al. 2012), quality
(Kuo, Wang, and Tien 2010; Büyüközkan and Çifçi 2011; Mafakheri, Breton, and Ghoniem
2011),flexibility(Humphreysetal.2006;Lu,Wu,andKuo2007;Büyüközkan2012)andtechnology
capability (Lu, Wu, and Kuo 2007; Kuo, Wang, and Tien 2010; Zhu, Dou, and Sarkis 2010; Mafa-
kheri, Breton, and Ghoniem 2011)in their supplier evaluationpolicy (Dickson 1966).
Inthesustainableapproachtosupplierevaluation,environmentalandsocialfactorsmustalsobe
considered in order to measure the suppliers’ performance and its effects on all the stages of the
supply chain.
Environmental and social factors usually include social responsibility (Hirsch, Kuhlmann, and
Schumacher 1998; De Brito, Carbone, and Blanquart 2008; Tsai and Hung 2009; Büyüközkan
2012), cleaner/environmental production and technologies (Tsoulfas and Pappis 2006; Matos and
Hall 2007; Lu, Wu, and Kuo 2007) and environmental management systems (Simpson and Power
2005; Verghese and Lewis 2007; Azadnia, Saman, and Wong 2015). Environmental criteria such
aspollutionproduction,resourceconsumption,eco-designandenvironmentalmanagementcompe-
tency have been employed by Kannan et al. (2013). Recently, Govindan, Khodaverdi, and Jafarian
(2013)andAzadniaetal.(2013)carriedoutSSSstudiesonemploymentpractices,healthandsafety,
localcommunityinfluences,andcontractualstakeholders,highlightingtheinfluenceofsocialcriteria
and economic factors on suppliers’ performance. Bhattacharya et al. (2014) considered organis-
ational commitment, eco-design, and green-SC processes as the leading criteria and social and
INTERNATIONALJOURNALOFLOGISTICS:RESEARCHANDAPPLICATIONS 259
sustainableperformancesasthelaggingones.Theyarticulatedeachcriterioninsub-criteriaandsub-
sub-criteriacollectedformtheliterature.Bhattacharya,Dey,andHo(2015)provideareviewofthe
recentresearchdevelopmentshighlightingthecriticalaspectsofchoosinggreen/sustainablefactors
insupply chain design.
2.5.Researchgaps
AlthoughtheSCMliteratureongreenperformancemeasurementandsupplierselectionisquitevast
(Björklund,Martinsen,andAbrahamsson2012),itusuallybuildsonstandardperformancemeasure-
mentapproachesoverlookingtheneedforaspecificanalysisofthesustainabledevelopmentelements
(CuthbertsonandPiotrowicz2008).Thisproducesaconsiderablegapintheliterature.Inparticular,
while the literature on supplier selection has experienced a significant growth, the one concerning
specificenvironmentalissuesisratherlimitedandgenerallyregardedaspartoftheformer.
Tobridgethisgap,itisnecessaryasystematicanalysisofthelinksbetweengreenconstructsand
sub-constructs of SCM and all the inter-organisational elements contributing to the performance
measurement (Bhattacharya et al. 2014).
Moreover,eventhoughmanystudiesemphasisetheinfluenceofCRsanddecisioncriteriaonthe
supplierselectionprocess,tothebestofourknowledge,noneofthemdealswithasystematicevalu-
ation of howthese variables affect each other.
Theproposedintegratedframeworkaimsatbridgingbothgapshighlightedabovebyprovidinga
systematic analysis of the interdependencies existing among customer variables and technical
decisioncriteria.Ouranalysisallowsnotonlytoidentifyaclearhierarchicalstructureforalltherel-
evantsustainablefactorsandsub-factors,butalsotoweightthedecisioncriteriasoastoreflectthe
importance of theCRs.
3. Proposed five-phase framework
3.1.Researchobjectives and questions
SSSPscomprisethefollowingmaintasks:(1)fixingthesustainabilityfactors(CRsandTRs)relevant
to supplier selection, (2) analysing the interdependencies among these factors, (3) determining a
method to evaluate the overall performance of the suppliers and, finally, (4) ranking the suppliers
toselect thebest onesin terms of performance.
Theresearchobjectiveofthisstudyistodefineaflexibleandeasy-to-implementmethodtosolve
SSSPsthatallowsnotonlyforacoherentandreliablesolution,butalsoforabroadrangeofappli-
cations to real-life situations.
Given the subjectivity inherent to the identification of CRs and TRs as well as the interdepen-
dency among their weights, thefollowing operative questions arise.
Q1.Howcanallrelevantsustainablefactors,i.e.CRsandTRs,beidentified?
Q2.Whatisthemostsuitableandversatileweightingmethodforthefactors?Shouldthefactorsbeallused
withinacommonhierarchicalstructure?
Q3.Howcantheweightingmethodbemademoreobjectiveandsystematic?
Q4.Howcanthereliabilityoftheweightingmethodbetested?
Q5.Wheredosuppliersstandinthefactorhierarchy?Whichsustainablefactorsshouldbedirectlyinvolvedin
theevaluationoftheoverallperformanceofthesuppliers?
Q6.Whichsupplierperformancerankingmethodshouldbeusedtoreflectthefactorweightscorrectly?
Q7.Howcanthechoiceofthesupplierrankingmethodbevalidated?
To deal with all these questions, we propose an integrated framework consisting of five phases:
Phase 1 accounts for Q1; Phase 2 deals with Q2, Q3 and Q4; Phase 3 addresses Q5; Phases 4–5
accounts for Q6 andQ7.Figure 1provides a graphical outline ofthese phases.
260 M.TAVANAETAL.
3.2.Phase I: identifying all relevant sustainable factors
Inthisphase,therelevantsustainablefactors/criteriaareselectedconsideringthecitedliteratureand
thespecificfeaturesofthecompanyunderanalysis.Expertsfromthecompanyareconsultedinorder
togather information and dataand identify both CRsand TRs.
TheCRsarethecustomervariables,thatis,thecriteriaonwhichthecustomersbasetheirchoice.
TheCRsaredividedinthreemainclusters:economic,environmentalandsocialfactors.Eachcluster
consists of several sub-factors/sub-criteria.These criteria are the WHATs in QFD.
Figure1.Proposedfive-phasemethod.
INTERNATIONALJOURNALOFLOGISTICS:RESEARCHANDAPPLICATIONS 261
TheTRsincludeallthecriteriatorankthecandidatesuppliers,thatis,allthecriteriathatmustbe
considered for theCRs to besatisfied.The TRs are theHOWs in QFD.
3.3.Phase II:weightingcustomerand TRs
This phase consists of two sub-phases:
. Phase II.1: the CRs are assigned a global weight using ANP.
. Phase II.2: the weights of the CRsare used in QFD toweight theTRs.
Forthesakeofcompleteness,recallthatQFDtransformationsareusuallyrepresentedbyamatrix,
knownashouseofquality(HoQ).ThismatrixexpressestherelationshipbetweentheCRs(WHATs)
and the TRs (HOWs) (Tang et al. 2005) and it consists of the following matrices: (A) WHATs
matrix, (B) HOWs matrix, (C) central relationship matrix between the WHATs and the
HOWs, (D) matrix of relative weights of the WHATs, (E) interrelationship matrix among the
HOWs, and (F) matrix of weights of the HOWs. A graphical representation of an HoQ is shown
inFigure 2. More detailson QFD are given in theOnline Appendix A.
Inthefirstsub-phase,aquestionnaireisconductedbytheexpertsofthepurchasingdepartment
tocollectthedatathatwillbeusedtoconstructthecomparisonmatricesofANPanddeterminethe
global weights of all the CRs. The ANP pairwise comparisons are carried out using the 1–9 scale
reported in Table 1.
Inthesecond sub-phase, anotherquestionnaire isconductedtoconstruct theHoQmatrix.The
reciprocalinfluencebetweentheCRsandtheTRsisevaluatedusinga1,3,6,and9scale.Thisscaleis
reported in Table 2.
3.4.Phase III: rankingthe suppliers per each TR
Inthisphase,aquestionnaireisconductedbyexpertsofthepurchasingdepartmenttodeterminethe
performance rating of the candidate suppliers. The questionnaire is used to establish how much a
supplierispreferredtotheotherswithrespecttoeachTR.Thedatacollectedareusedforthecom-
parisonmatricesofAHPandavectoroflocalweights(aranking)forthecandidatesuppliersispro-
ducedforeachTR.TheAHPpairwisecomparisonsarecarriedoutusingagainthe1–9scalereported
inTable 1.
Figure2.GeneralQFDmodel.
262 M.TAVANAETAL.
Table1.AHP/ANPscaleforpreferencesandpairwisecomparisons.
Intensityofimportance Definition Explanation
1 Equalimportance Twoactivitiescontributeequallytotheobjective
3 Moderateimportance Experienceandjudgmentslightlyfavouroneoveranother
5 Strongimportance Experienceandjudgmentstronglyfavouroneoveranother
7 Verystrong Activityisstronglyfavouredanditsdominanceisdemonstratedin
importance practice
9 Absoluteimportance Importanceofoneoveranotheraffirmedonthehighestpossible
order
2,4,6,8 Intermediatevalues Usedtorepresentcompromisebetweentheprioritieslistedabove
Reciprocalofabovenon-zero ifactivityihasoneoftheabovenon-zeronumbersassignedtoitwhencomparedwith
numbers activityjthenjhasthereciprocalvaluewhencomparedwithi
3.5.Phase IV:rankingthe suppliers with WASPAS and MOORA
In this phase, WASPAS and MOORA are applied to rank the candidate suppliers. To implement
these methods, both the weights of the TRs from Phase II and the supplier rankings (one per
each TR)obtained inPhase III are needed.
For the sake of completeness, recall that both WASPAS and MOORA algorithms start with a
t×n matrix whose generic element is the performance rating of the kth supplier (k=1, ..., t)
withrespecttothejthdecisioncriterion(j=1, ..., n).Theelementsofthismatrixarethennor-
malisedandusedtocalculateatotalperformanceindexofeachsupplier.FurtherdetailsonWASPAS
andMOORA are available in the Online Appendix A.
3.6.Phase V:validationof results
COPRASisappliedtorankthesuppliers.Theresultingrankingisusedtovalidatethoseobtainedin
Phase IV.
In MCDM problems, it is customary to compare the results obtained by two methods using a
third method as a ‘mediator’. In this study we use the COPRAS algorithm due to its capability in
managing complex decision systems. Further details on COPRAS and its main features are given
inthe Online AppendixA.
3.7.Implementation
Eventhoughtheproposedproceduremayseemlongtoimplementandtime-consuming,ithassev-
eral desirable characteristics.
(a) Itisnotdifficulttoimplementanditdoesnotrequiremuchcomputationaleffortsinceitcanbe
put into practice using anycommon spreadsheet software such asExcel.
(b) It is based on a clear distinction between CRs and TRs interpreting them as independent and
dependent sustainable variables, respectively (Phase II).
(c) The weights of CRs and TRs are assigned so as to systematically reflect stakeholders’ wishes
while respecting experts’ opinions and criteria (Phase II).
Table2.NumericalscaleforevaluatingtherelationshipsbetweenWHATsandHOWs.
Intensityofreciprocalinfluence Definition Explanation
1 Weakrelationship TheWHATfactorisweaklyrelatedtotheHOWcriterion
3 Moderaterelationship TheWHATfactorismoderatelyrelatedtotheHOWcriterion
6 Strongrelationship TheWHATfactorisstronglyrelatedtotheHOWcriterion
9 Verystrongrelationship TheWHATfactorisverystronglyrelatedtotheHOWcriterion
INTERNATIONALJOURNALOFLOGISTICS:RESEARCHANDAPPLICATIONS 263
(d) Itprovidesthreeparallelsupplierrankings(PhasesIIItoV)eachofwhichcanbeusedtocontrol
and validate the others and assurethe stability of thesolutions.
Thus, the proposed five-phase procedure provides an approach to SSSPs which may be time-con-
sumingintheshorttermbutissurelytime-savinginthelongterm.Infact,thefive-phaseintegrated
designreducesthesubjectivityintrinsictothesupplierassessmentprocessenhancingthequalityof
managers’ choices in viewof future policies.
4. The case study: results and analysis
Inthis section, we show theresultsobtainedby applying the proposed framework to a real SSSP.
Theproposedmethodhasbeenappliedtoareputabledairycompanywithmorethan20yearsof
experienceindiversetypesofdairy,meat,beverageandfoodproducts,namely,theKallehCompany,
establishedin1991inAmolinthenorthofIran.KallehdairyproductsincludeUHTprocessedmilk,
yoghurt,cheese,dairydrinks,industrialdrymilk,industrialpowders,buttermilkanddessertsfora
total of more than 150 types of products.
TheprocurementandlogisticsdepartmentoftheKallehDairyCompany,calledPOLE,isincharge
ofprovidingawiderangeofrawmaterialsofthedesiredqualityatthelowestpossibleprices.Further-
more,POLEisresponsibleforpurchasingtheentireequipmentrequiredforthecompany,including
machinery,spareparts,technicalparts,laboratoryequipmentandmaterial-handlingequipment.
In particular, POLE plays a key role in the procurement of raw milk which is a strategic raw
material in the dairy industry. By purchasing more than 2500 tons of milk per day, the company
is one of the greatest industrial consumers of raw milk in Iran. In addition, POLE supplies raw
material,packagingmaterialsandproductiontechnologyandprocess.Inordertobemorecompeti-
tiveandimproveproductivity,POLEmustassesssuppliersandtheirsustainability.Therefore,devel-
oping a system to evaluate and identify the top suppliers is an important component of this
company’s objectives.
InPOLE,weconsultedthechiefexecutivemanagerandwereintroducedtheexpertstohelpusto
collecttheinformationrequiredforourstudy.Tensupplierswereconsideredfortheimplementation
ofour method. Inthe following, thesuppliers are denoted byA , ..., A .
1 10
PhaseI:Basedonaliteraturereviewandtheinformationsharedbytheexperts,weidentifiedthe
customerindicators(i.e.CRs)andthedecisioncriteria(i.e.TRs)relevanttothecasestudy.Thiscom-
pletedPhaseI.Thefactorsandsub-factorsidentifiedastheCRsandtheirhierarchicalstructureare
displayed inFigure 3togetherwith the TRs.
To implement Phases II and III, three questionnaires were conducted by three anonymous
experts ofthe purchasing department. The Online Appendix B provides some sample questions.
Phase II.1:One questionnaire wasused togather information on howthecustomer factors and
sub-factorsinfluenceeachotherandinterrelatesoastobeabletoapplyANPandweighttheCRs.
Theexperts’ judgments were translated into numericalvalues using the 1–9 scale ofTable1.
Tables3–6showthepairwisecomparisonmatricesformedinthisphaseforthecustomerfactors
and sub-factors. The last column of each matrix displays the local weights. Table 3 represents the
comparison matrix and the local weights for the customer main factors, while Tables 4–6 show
thecomparison matrices and local weights ofthe sub-factors within their corresponding clusters.
Subsequently, the interdependence among the main factors (see Figure 3) was analysed by
measuring the impact of one factor on the others using pairwise comparisons. Tables 7–9 provide
thethreepairwisecomparisonmatricesobtainedfortheeconomic,environmentalandsocialfactors,
respectively, while Table10 reports the resulting relative importanceweights.
The ‘0’ value in Table 10 means that there is no dependence between two factors. The other
numerical values show the degree of relative impact between two factors. For example, the degree
ofrelative impactof theenvironmental factors on the social ones is 0.381.