Measuring the success possibility of implementing ERP by utilizing the Incomplete Linguistic Preference Relations

Incomplete

Linguistic

Preference

Relations

Tsung-Han

Chang

_,

_Shu-Chen

_Hsu

_,

_Tien-Chin

_Wang

_,

_Chao-Yen

_Wu

a_{DepartmentofInformationManagement,KaoYuanUniversity,Taiwan} b_{DepartmentofMarketingDistributionManagement,KaoYuanUniversity,Taiwan}

c_{DepartmentofInternationalBusiness,NationalKaohsiungUniversityofAppliedSciences,Taiwan} d_{DepartmentofInformationManagement,I-ShouUniversity,Taiwan}

a

r

t

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Articlehistory:

Received15January2010

Receivedinrevisedform26March2011 Accepted18December2011

Availableonline2February2012 Keywords:

InLinPreRa

IncompleteLinguisticPreferenceRelations ERP

Multi-CriteriaDecisionMaking Analyticalhierarchyprocess

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b

s

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ThispaperappliesananalytichierarchicalpredictionmodelbasedontheMulti-CriteriaDecisionMaking withIncompleteLinguisticPreferenceRelations(InLinPreRa)tohelptheorganizationsbecomeaware oftheessentialfactorsaffectingtheEnterpriseResourcePlanning(ERP),aswellasidentifytheactions necessarybeforeimplementingERP.Thesubjectivityandvaguenessinthepredictionproceduresaredealt withlinguisticvariablesquantifiedinaninterval[−t,t].Thenpredictedsuccess/failurevaluesareobtained toenableorganizationstodecidewhethertoinitiateERP,inhibitadoptionortakeremedialactionsto increasethesuccesspossibilityofERP.Pairwisecomparisonsareusedtodeterminethepriorityweights ofinfluentialfactors,andthepossibleoccurrenceratingsofsuccessorfailureoutcomeamongstdecision makers.Therearenotanyinconsistencyoccurredinthisproceduresbecausethisproposedapproach allowseverydecisionexperttochooseanexplicitcriterionoralternativeforthewithoutrestriction. Whentherearencriteriainadecisionmatrix,onlyn−1timesofpairwisecomparisonsaretaken.This approachnotonlyimprovestheefficiencyofpairwisecomparisoncomparedwiththetraditionalAHP, butalsoavoidsthecheckingtheconsistencyoflinguisticpreferencerelationwhenthedecisionmakers undertakethepairwisecomparisonprocesses.

©2011ElsevierB.V.Allrightsreserved.

1. Introduction

Companiesfacethetremendouschallengesofexpanding mar-kets and rising customer expectations in such a dynamic and unpredictableenvironment.AsuccessfulEnterpriseResource Plan-ning(ERP)offerscompelsthemtolowertotalcostsinthesupply chain,shortenthroughputtimes,reduceinventories,expand prod-uctchoice,providemorereliabledeliverydates&bettercustomer service,improvequality,andefficientlycoordinateglobedemand, supply and production [1,2]. ERP offers organizations benefits or profits, suchas automatebusiness process, timely accessto managementinformationandimprovesupplychainmanagement throughtheuseofe-commerce[3,4].ERPisanenterprise-wide applicationsoftwarepackagethatintegratesallnecessarybusiness functionsintoasinglesystemwithinacommondatabase.Inorder toimplementanERPprojectsuccessfullyinanorganization,itis

∗ Correspondingauthorat:No.1821,JhongshanRd.,LujhuDist.,KaohsiungCity 821,Taiwan.

E-mailaddresses:t90082@cc.kyu.com.tw(T.-H.Chang),

demi8468@hotmail.com,jania@mail.nzsmr.kh.edu.tw(S.-C.Hsu),

tcwang@cc.kuas.edu.tw(T.-C.Wang),cywu@isu.edu.tw(C.-Y.Wu).

necessarytoselectasuitableERPsystem[5].ThoughanERP sys-temiscostlyandcomplex,butitisvitalforcompaniestofacethe rapidlychangingandcompetitivebusinessenvironment.Itiswell knownthatanERPprojectcostsalargeamountofinvestmentand commitmentbyanorganization.Theirinherentsizeandscopehas oftenleadedtocomplexities.ResearchofERPimplementationhas mainlyfocusedontheirinitialstart-up[6,7],includingindatabase

[8],customerrelationmanagementandsupplychainmanagement

[9], decision [10], select fit supplies [5,11–13] and critical fac-tors,buttherearefewresearchesinmeasuringthesuccess/failure possibility.Thisstudythusproposesaframeworkbasedon Incom-pleteLinguisticPreferenceRelationsunderMulti-CriteriaDecision Makingenvironmenttomeasurethesuccess/failurepossibilityof initiatingtheERPsystem.Thisapproach usessimple calculation andspeedsuptheprocessofcomparisonandselectionoffeasible alternatives.Decision-makingexpertsalwaysobtainthecomplete decisionmatrixbychoosingafiniteandfixedsetofalternatives, andsetapairwisecomparisonbasedontheirdifferentpreferences andknowledge.Whenmakingpairwisecomparisonsbythree algo-rithms: horizontal,vertical, and obliquewill not encounterthe problemofinconsistency,andthedecisionmakersareallowedto chooseanexplicitcriterionoralternativeforindexunrestrictedly. Whentherearencriteriainadecisionmatrix,onlyn−1pairwise

1568-4946/$–seefrontmatter©2011ElsevierB.V.Allrightsreserved. doi:10.1016/j.asoc.2011.12.008

T.-H.Changetal./AppliedSoftComputing12(2012)1582–1591 1583

Fig.1.Typicalstructureanalytichierarchyprocess.

comparisonsareundertaken,andthentheIncompleteLinguistic PreferenceRelationsTheorywillbeutilizedtoobtainthecomplete matrixsoastocalculatethepriorityweightsofalternatives.This approachnotonlygreatlypromotestheefficiencythantraditional AHP,butalsoavoidsthefollowingproblems:timepressure,lack ofcompleteinformation,thedecisionmakerslackrelated profes-sionalknowledge,ortheinformationprovidedisunrealandhard toobtain.

ThenextsectionwilldiscusstheERP,MCDM,fuzzypreference relation,andIncompleteLinguisticPreferenceRelations.An ana-lytichierarchy framework based ontheMulti-Criteria Decision MakingwithIncompleteLinguistic PreferenceRelationsfor pre-dictingtheERPimplementationisderivedinSection3.InSection

4,anempiricalcaseofERPinitiativeinTaiwanispresented.Finally, discussionandconclusionsaregiveninSection5.

2. Literaturereview

2.1. EnterpriseResourcePlanning(ERP)

Intoday’sdynamicand unpredictablebusinessenvironment, companiesfacethetremendouschallengeofexpandingmarkets and rising customer expectations. This compelsthem to lower costsinsupplychain,shortenthroughputtimes,reduce invento-ries,expandproductchoice,providemorereliabledeliverydates andbettercustomerservice,improvequality,andefficiently coor-dinateglobedemand,supplyandproduction[1,14].AnERPsystem isanenterprise-wideapplicationsoftwarepackagethatintegrates allnecessarybusinessfunctionsintoasinglesystemwitha com-mondatabase.SuccessfullyselectandimplementanERPproject becomesmoreandmoreimportanttoanorganization.Asuccessful ERPsystemoffersorganizationsbenefitsanditisthemajorchoice toobtaincompetitiveadvantagefororganizationsorcompanies. However,thesuccessfulimplementationrateislowandmanyfirms donotachieveintendedgoals[15].Onereasonisthatthemanagers donotproperlyassessandmanagetherisksinvolvedintheprojects

[16].Mostprojectmanagersperceiveriskmanagementprocesses asextraworkandexpense;thus,riskmanagementprocessesare oftenexpungedifaprojectscheduleslips.Duetohighcostand complicationoftheimplementingprocess,itisnoteasytoinstall ERPsystemsuccessfully.Thus,formostcompanies,beingableto predicttheratesofsuccessbeforeinstallingthesystem,aswellas findingthefactorsthatinfluencetheERPsuccessrateareimportant. 2.2. Multi-CriteriaDecisionMaking

AnalyticHierarchyProcess(AHP),proposedbySaaty[17,18], is a Multi-Criteria Decision Making (MCDM) approach that has been used in decision science. A hierarchy framework of

analytic hierarchy process is shown in Fig. 1. Multiple Crite-ria Decision-Making is the optimal choice, with different type depended on decision makers’ preference, sorted of Multi-ple Objective DecisionMaking (MODM) and MultipleAttribute Decision Making (MADM). Yoon and Hwang [19] provided that Multiple Criteria Decision-Making is a possible evalua-tionscaleformany charactersorquantitiesofdecision-makers’ evaluation.

Under several alternatives and several evaluation criteria, MCDMquantifieseachevaluationcriterionandappliesscientific methodsandskillstocarryonmulti-criteriadecision-making anal-ysis, so as to conduct a quality order and evaluation for each alternative,thenthebestalternativethatconformtothedecision maker’sidealaredecided.

2.3. ThedecisionmakingmatrixofIncompleteLinguistic PreferenceRelations

Herrera-Viedmaetal.[20]proposedFuzzyPreferenceRelations tosolvetheinconsistentproblemsinanalytichierarchyinwhich havemulti-decision-makers,multi-criteriaandmulti-alternatives. Preferencerelationmeansthatthedecisionmakercountersasetof criteriaoralternativesaccordingtothelinguisticvariablessoasto carryoutthepairwisecomparison,andthenamappingvaluecan bederived.Inmanyresearches[20–29]utilizedfuzzypreference relationstocriticizethefuzzyanalyticalapproachtopartnership selection.WangandChang[29]appliedfuzzypreferencerelations toforecasttheprobabilityofsuccessfulknowledgemanagement. Linguisticpreferencerelationsareusuallyusedbydecision mak-ers to expresstheir linguisticpreference information based on pairwisecomparisons[30].Xu[31]proposedtheIncomplete Lin-guisticPreferenceRelationsmethodthatmakessufficientlyusing oftheprovidedpreference informationand maintainsthe deci-sionmaker’sconsistencylevelavoidscheckingtheconsistencyof linguisticpreference relations. Duringthe pairwisecomparison, eachexpert canselectanyoneoftheexplicititemsasthe stan-dardaccordingtohis/herpreferenceorrecognition,andthenthe pairwise comparisonwill becarried outbetweentheadjoining items in order to obtain the original preference matrix; com-plete linguisticpreference relation countersthe fact that allof theattributedecision-makingexpertscarryoutthepairwise com-parisonthrough preference matrix.The relevantdefinitions are describedasfollows:

Themethodnotonlyrelievesthedecisionmakeroftime pres-sure and makes sufficiently using of the provided preference information,butalsomaintainsthedecisionmaker’sconsistency levelandavoidscheckingtheconsistencyoflinguisticpreference relation.

makerscancarryoutpairwisecomparisonforattributessoasto

satisfyEq.(1)

aij∈S, aij⊕aji=S0, aii=S0 (1)

Definition2(.). IncompleteLinguisticConsistentAdditive Prefer-enceRelation:

LetA=(aij)n×nbeacompleteconsistentadditivepreference

rela-tion,whichcountersallofthei,j,kdecisionmakersforpairwise comparison.Ifaik>S0,itrepresentsxiisbetterthanxk;whileakj>S0

representsxkisbetterthanxj,thenaij>S0canbederivedthe

equa-tionofxibetterthanxj.

aij=aik⊕akj (2)

aij=S0,aij=0representsxiandxjarethesame,bothofthemsatisfy

aik=akj=aij=S0.

Definition3(.). IncompleteLinguisticPreferenceAdjoining Rela-tion

LetA=(aij)n×nbealinguisticpreferencerelation,ifAisan

incom-pletelinguisticpreferencerelation,if(i,j)∩(k,l) /= ∅,attributesaij

andaklarecalledadjoiningrelation.

Definition4(.). IncompleteLinguisticPreferenceIndirectRelation Let A=(aij)n×n be a linguistic preference relation, if Ais an

incompletelinguisticpreferencerelation,weassumeai0j0tobethe

unknownvalueinpreferencematrixA.Theattributeai0j0iscalled

“IndirectRelation” which is derived from the adjoiningknown attributesai0kandakj0.

Definition5 (.). AcceptableAlternativeofIncompletelinguistic Preference

Let A=(aij)n×n be a linguistic preference relation, if Ais an

incompletelinguisticpreferencerelation,itiscalled“Acceptable Alternative”byobtainingallunknownvariable“×”through adjoin-ingknownvariables.Therefore,ifAisanacceptablealternativeof incompletelinguisticpreference,itistheknownvalueinacolumn orrow,andhavingn−1contrastingvaluesbypairs(Table1). 2.3.1. ApplicationofdecisionmakingmatrixofIncomplete LinguisticPreferenceRelations

Xu[31]appliedincompletelinguisticpreferencerelationto con-structdecision makingmatrix.Theproceduresof establishinga completedecisionmakingmatrixareasfollows:

Step1(:). LetD={d1,d2,...,dm}beasetofdecisionmakers,where

mdenotes theamount of decision makers,ω=(ω1,ω2,...,ωm)T,

ωk≥0betheweightvectorofdecisionmakerswherek=1,2,...,m,



k−1ωk=1.Thedecisionmakerdk∈Dutilitieslinguisticvariables

tocompareallalternatives,wherethepreferencerelationmatrix Ak=(akij)_n×nwillbederivedthroughn−1pairwisecomparisons,

amongwhich_ak_ijrepresentsthekthexpertexpressesthepreference relationvaluesbetweenalternativesiandalternativesj.

Fig.2. TheprocedurestoforecasttheprobabilityofsuccessfulERP.

Step2(:). InpreferencerelationmatrixAk(k=1,2,...,m),allofthe

unknownvariablesarederived indirectlythroughthetransitive relationofEq.(2)aij=aik⊕akj,alloftheexperts’decisionmaking

matrixareintegratedandshownasfollows:

¯Ak=( ¯a(ijk))_n×n (k=1,2,...,m) (3) Step3(:). Multiplythedecisionmakingpreferencematrixofeach expertwiththeweightvectorofdecisionmakerinorderto inte-grateacompletedecisionmakingmatrix.

¯

aij=ω1a¯(1)_ij ⊕ω2a¯(2)_ij ⊕···⊕ωma¯(m)_ij (4) Step4(:). Averagethelinguisticvalue

¯

ai= 1_na¯i1⊕1_na¯i2⊕...⊕1_na¯in (5)

Toaverageallthepreferencedegrees ¯aij(j=1,2,...,n)inthe

ithrow,andthengetthefinaldecisionmakingpreferencematrix ¯A.

Step5(:). Rankallthealternatives

3. Frameworkforpredictingtheprobabilityofsuccessful ERPimplementation

Thissectionconstructsaframeworktopredicttheprobability ofsuccessfulERPprojectbyusingtheIncompleteLinguistic Prefer-enceRelations(seeFig.2).Thecontentcomprisesfoursubsections: investigatingtheinfluentialfactorsonERPinitiative,determining thepriorityweightsofinfluentialfactors,determiningthepriority ratingsforpossibleoutcomesregardingfactors,andobtainingthe priorityweightforprediction.

T.-H.Changetal./AppliedSoftComputing12(2012)1582–1591 1585

Fig.3.ThehierarchydiagramforpredictingERPimplementation.

3.1. InvestigatingtheinfluentialfactorsonERPimplementation The hierarchical structure for dealing with the problem of forecastingthechanceofsuccessfulERPisshowninFig.3.The influentialfactorsarederivedthoughwidespreadinvestigations andconsultationswithseveralexperts,includingtwo profession-als,twoseniormangers,twosystemanalysts,towMISfinance,one purchasing,onemanufacturingandonerandomsampling man-agerofsalesstaff.Identifycritical factorsaffectingERPsystems implementationsuccesswithmanyresearches[1,4,32–37]. Syn-thesizingtheliteraturereview,theopinionsoftheseexpertsare utilizedtoyieldthesevenkeyinfluentialfactorsusedinthisstudy. (C1)Projectproceduretime,(C2)functionofinformationsystem,

(C3)user’scooperation,(C4)theseniormanagersupportdegree,

(C5)coordination,(C6)organizational,(C7)cost.

3.2. Determiningthepriorityweightsofinfluentialfactors

Subjectivityandvaguenesswithinthemeasuringprocessare dealtwithusinglinguisticvariablesquantifiedina scaleof[−t, t].Thisstudyuseslinguisticvariablestoexpressdecisionmakers’ strengthofpreferenceamonginfluentialfactors.

3.2.1. Linguisticvariables

Linguisticvariablearecommonlyusedindailylife.Inthe lit-erature,wefindmanyapplicationsoflinguisticdecisionanalysis tosolvereal-worldactivities,e.g.,groupdecisionmaking[38,39], Multi-CriteriaDecisionMaking[40]consensus[41].Thisstudy pro-videstheevaluatorssimplelinguistictermsquantifiedonascale of[−8,8]toexpresstheirstrengthofpreferenceamonginfluential factors(seeTable2).Linguisticvariablesaresimultaneouslyusedto measurethelikelihoodofsuccess/failureregardingeachinfluential factor(seeTable3).

3.2.2. Obtainingpriorityweightsofinfluentialfactor

Thefollowingdescribestheproceduresforobtainingthepriority weightsofinfluentialfactors.

(1) Constructpairwisecomparisonmatricesamongstthe influen-tialfactors(Cr,r=1,2,...,k).Theevaluators(Ee,e=1,2,...,n)used

threetypesofpairwisecomparisonsalgorithmwhichare hor-izontalverticalandobliquetoconstructpairwisecomparison matrices.Threekindsofmatricesarebelow.

Table2

Linguistictermsfortheimportanceweightsofinfluentialfactors.

Definition Intensityofimportance

Absolutelymoreimportant(AB) 8

BetweenABandVSIntermediation(AV) 7

Verystronglymoreimportant(VS) 6

BetweenVSandSTIntermediation(VT) 5

Stronglymoreimportant(ST) 4

BetweenSTandWKIntermediation(SW) 3

Weaklymoreimportant(WK) 2

BetweenWKandEQIntermediation(WE) 1

Equallyimportant(EQ) 0

BetweenEQandLWK Intermediation(ELW) −1

LessWeaklymoreimportant(LWK) −2

BetweenLWKandLST Intermediation(LWLS) −3

LessStronglymoreimportant(LST) −4

BetweenLSTandLVSIntermediation(LSLV) −5 LessVerystronglymoreimportant(LVS) −6 BetweenLVSandLABIntermediation(LVLA) −7 LessAbsolutelymoreimportant(LAB) −8

Table3

Linguisticvariablesforthepriorityratingsofpossibleoutcome.

Definition Intensityofrating

Veryhigh(VH)5 4

BetweenVHandHIntermediation(VHG) 3

High(H) 2

BetweenHandFIntermediation(HF) 1

Fair(F) 0

BetweenFandLHIntermediation(LHF) −1

LessHigh(LH) −2

BetweenLHandLVHIntermediation(LVHG) −3

LessVeryhigh(LVH) −4

Horizontalcomparisonofeachpairs

2

1 3 4

(e) (e) (e) (e)

1 12 13 14 1k 2 3 (e) (e) 4

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ingbusinessprocess,timelyaccesstomanagement information

andimprovingsupplychainmanagementthroughtheuseof

e-commerce.Theempiricalresultsnotonlydemonstratethesenior

managersupportdegree,organizationalandcoordinationarethe

threemostimportantinfluentialfactorsintheERPinitiative

pro-cess,butalsorevealtheapplicabilityandfeasibilityofreciprocal

IncompleteLinguisticPreferenceRelationforsolvingcomplicated

hierarchicalmultipleattributepredictionproblems.Subsequently,

organizationsorenterprisesplanningtoinitiateERPsystemcan

applytheproposedpredictionmodeltoenhancetheir

decision-makingprocessandtakeproperactionstoavoidwastingtimeand

moneybeforeERPImplementation.Subsequently,organizationsor

enterprisescanapplytheproposedpredictionmodeltoenhance

theirdecision-making processand takeproperactionstoavoid

pitfallsbeforeinitiatingERPmanagementproject.

Acknowledgements

TheauthorwouldliketothanktheNationalScienceCouncil

ofthe RepublicofChina, Taiwanforfinancially supportingthis

researchunderContractNo.NSC100-2410-H-151-008.

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