Big data analytics :

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ContentslistsavailableatScienceDirect

Computers in Industry

j ou rn a l h om ep a g e :w w w . e l s e v i e r . c o m / l o c a t e / c o m p i n d

Big data analytics: Implementation challenges in Indian manufacturing supply chains

Rakesh D. Raut

^a

, Vinay Surendra Yadav

^b

, Naoufel Cheikhrouhou

^c,∗

, Vaibhav S. Narwane

^d

, Balkrishna E. Narkhede

^e

aDept.ofOperationsandSupplyChainManagement,NationalInstituteofIndustrialEngineering(NITIE),ViharLake,NITIE,Powai,Mumbai,Maharashtra, Pin-400087,India

bDepartmentofMechanicalEngineering,NationalInstituteofTechnologyRaipur,India

cGenevaSchoolofBusinessAdministration,UniversityofAppliedSciencesWesternSwitzerland(HES-SO),1227Carouge,Switzerland

dDept.ofMechanicalEngineering,K.J.SomaiyaCollegeofEngineeringVidyanagar,VidyaViharEast,GhatkoparEast,Mumbai,Maharashtra400077,India

eDepartmentofIndustrialEngineeringandManagementSystems,NationalInstituteofIndustrialEngineering(NITIE),ViharLake,NITIE,Powai,Mumbai, Maharashtra,Pin-400087,India

a rt i c l e i nf o

Articlehistory:

Received24February2020 Receivedinrevisedform 14September2020 Accepted23November2020

Keywords:

Bigdataanalytics DEMATEL

Indianmanufacturingsupplychains Interpretivestructuralmodeling MICMACanalysis

a b s t ra c t

BigDataAnalytics(BDA)hasattractedsignificantattentionfrombothacademiciansandpractitioners alikeasitprovidesseveralwaystoimprovestrategic,tacticalandoperationalcapabilitiestoeventually createapositiveimpactontheeconomicperformanceoforganizations.Inthepresentstudy,twelve significantbarriersagainstBDAimplementationareidentifiedandassessedinthecontextofIndian manufacturingSupplyChains(SC).Thesebarriersaremodeledusinganintegratedtwo-stageapproach, consistingofInterpretiveStructuralModeling(ISM)inthefirststageandDecision-MakingTrialand EvaluationLaboratory(DEMATEL)inthesecondstage.Theapproachdevelopedprovidestheinterrela- tionshipsbetweentheidentifiedconstructsandtheirintensities.Moreover,FuzzyMICMACtechniqueis appliedtoanalyzethehighimpact(i.e.,highdrivingpower)barriers.Resultsshowthatfourconstructs, namelylackoftopmanagementsupport,lackoffinancialsupport,lackofskills,andlackoftechniques orprocedures,arethemostsignificantbarriers.Thisstudyaidspolicy-makersinconceptualizingthe mutualinteractionofthebarriersfordevelopingpoliciesandstrategiestoimprovethepenetrationof BDAinmanufacturingSC.

1. Introduction

Given the highly dynamic business environment, managers prefertakingdata-drivendecisionsratherthantrustingtheirintu- itions(Davenportetal.,2001;Arunachalametal.,2018).Firmsare developingtheirorganizationalandtechnologicalcapabilitiesfor extractingvaluefromthedata,whichwillprovidethemacompet- itiveedgeovertheotherﬁrms.However,analystsfacesigniﬁcant challengesinunderstandingthepotentialofextractingvaluefrom thecollecteddata/information.Thevaluegeneratedfromthedata dependsonthecapabilityoftheorganizationforcapturing,storing andanalyzingdatausingadvancedanalytictools/methodssuchas BigDataAnalytics(BDA)(Huetal.,2014).Indeed,inthelastdecade,

∗Correspondingauthor.

E-mailaddresses:rraut@nitie.ac.in(R.D.Raut),vinaysyadav93@gmail.com (V.S.Yadav),naoufel.cheikhrouhou@hesge.ch(N.Cheikhrouhou),

vsnarwane@somaiya.edu(V.S.Narwane),benarkhede@nitie.ac.in(B.E.Narkhede).

therewassigniﬁcantinterestinvariousinformationandcommu- nicationtechnologiesforthemanagementofsupplychains(SC), whicharegeneratingenormousamountsofdata(Yesudasetal., 2014;Arunachalametal.,2018).

Further,BDAhasemergedfromtwosources,i.e.,BigData(BD) andDataAnalytics(Russom,2011;WallerandFawcett,2013).Big datareferstothestorageandanalysisofcomplex,voluminousdata throughtheuseofaseriesoftechnologies(WardandBarker,2013).

ThesetechnologiesincludebutarenotlimitedtoNoSQL,MapRe- duce,artiﬁcialintelligenceandmachine learning,whichfurther makeuseofseriouscomputingpowertoanalyzemassivedatasets obtainedfromvarioussources(Russom,2011).Dataanalyticsrefers tothescienceofexaminingraw datatoderiveusefulinforma- tion,usinganalyticaltoolssuchasmining/predictiveanalyticsand descriptive analytics.Data analytics canhelp in identifyingthe trendsandpatternsfromthedatatosupportdecision-makingand includesprocessessuchasdatainspecting,cleansing,transforming andmodeling.Thus,thecombinationofbigdataanddataanalytics

https://doi.org/10.1016/j.compind.2020.103368

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formsthebasisofBDA.ItisworthmentioningthatBDAcanimprove services,masscustomization,digitalmarketing,andoverallper- formanceoftheSCinhighlycompetitiveenvironments(Meredith etal.,2012;Tien,2015).ThedetailedapplicationsofBDAinsup- plychaincouldbeseeninthereviewworkofTiwarietal.(2018) andNguyenetal.(2018).Moreover,thefutureimpactsofBDAin supplychainsarediscussedbyRoßmannetal.(2018).Itwasfound thatBDAwouldimprovethemanagementofsupplierperformance, demandforecastsandreducesafetystocks.Moreover,bigdatamay play a significantrole insupply chainsustainabilityassessment (Belaud etal., 2019).Even thoughthebigdata phenomenonis believedtobethelatestglobalsensation,itsemergencehasnot beenprecipitated.Manyorganizationsresisttheimplementation ofBDAtechnologiesduetobehavioralandorganizationalissues, andambivalenceinunderstandingthepotentialbenefits.Signifi- cantresearchintheBDAdomainhasfocusedontheperspective oftechnologyforrationalizingitsfinancialgains,socialmedia,and itsapplicationsinsupplychainmanagement(SCM).However,the presentliteraturelacksresearchonidentifyingtheobstaclestoBDA implementationinthecontextofdevelopingcountrieslikeIndia.

Thus,thereisaneedtoaddressthefollowingResearchQuestions (RQ):

RQ1:WhatarethebarriersagainstBDAimplementationinthe IndianManufacturingSupplyChains?

RQ2:Howcantheinterrelationshipbetweenexistingbarriers beobtained?

RQ3:Whatistheintensityoftheseinterrelationships?

Theaboveinformationcanbeutilizedtobuildsuitablestrate- giesforeffectiveimplementationofBDA.Furthermore,noneofthe pastresearchstudieshaveaddressedthemutualinﬂuenceofthe critical barriersofBDAadoptionusingamulti-criteriadecisionmaking(MCDM)approachintheIndianmanufacturingSC.Hence, thepresent studyﬁllsa gapin thecollectivebody of research;

highlightingtheidentiﬁcationofthebarriersagainstBDAimple- mentationfromanexhaustiveliteraturereviewandexpertopinion analysis.Thisisfollowedbydevelopingahierarchicalstructural model of thebarriers exploring theinterrelationships between thembyusingISMandfuzzy-MICMACanalysis.Further,DEMATEL isutilizedtoobtaintheintensityoftheinterrelationshipsbetween theidentiﬁedbarriers.

Thenextsectiondescribesrelevantliterature,whiletheresearch methodology isexplained inSection3.The integratedmodelis developedinSection4.Section5discussesthemanagerialimpli- cations oftheproposedwork, andﬁnally, Section6 detailsthe conclusionandfurtherscopeofthestudy.

2. Literaturereview

BDAhasemerged asoneof themostinterestingstudyareas forresearchers,practitionersandindustrialistsintherecenttimes.

Thereexistseveralpublicationsthatdiscusstheimplementation ofBDAandelaboratethevariousfactorsassociatedwithitsadop- tion.Addo-TenkorangandHelo(2016)discussedtheapplications ofbigdatainoperations/SCM.Zhongetal.(2016)highlightedthe opportunities,challenges,andfutureperspectivesofbigdatafor SCMinthemanufacturingandservicesectors.WallerandFawcett (2013);Wangetal.(2016)andCheikhrouhouetal.(2016)investi- gatedtheapplicationsofBDAinSCandlogisticsmanagement.(Yu etal.,2018)exploredtheinﬂuenceofbigdata-drivencapabilitiesof SContheeconomicperformance,inthecontextoftheChineseman- ufacturingsector,usingstructuralequationmodeling(SEM).Itwas concludedthatthereexistsapositivecorrelationbetweendata- drivenSCandSCcapabilities.Arunachalametal.(2018)alsostudied BDA capabilities inthe SCMwitha focus on challenges,issues andimplicationsforpractitioners.Gangwar(2018)identiﬁedthe

driversofbigdataadoptionintheservicesandmanufacturingsec- torsofIndiausingSEManalysis.Asignificantchangeforacceptance ofbigdataadoptionwasreportedinthisstudy.Yadegaridehkordi etal.(2018)alsoidentifiedandsortedtheadoptionfactorsfavoring bigdataandpredictedtheinfluenceofbigdataadoptionontheper- formanceofmanufacturingfirmsusingahybridapproachthrough

“DEMATELandadaptive fuzzyneuroinferencesystems(ANFIS).

LambaandSingh(2018)foundthat“managementcommitment, ﬁnancialsupportforbigdatainitiatives,bigdatacompetencies, organizationalstructure and changemanagement program”are fundamentalforthesuccessofbigdatainitiativesinIndianoper- ationsandSCmanagementorganizations.TheauthorsusedISM, fuzzyTotalInterpretiveStructuralModeling(TISM)andDEMATEL.

Moktadiretal.(2019)identiﬁedthemainbarriersagainstadopting BDAintheBangladeshimanufacturingSCsthroughaDelphi-based AnalyticalHierarchyProcess(AHP).Itemerged thatthebarriers arerelatedtodataandtechnology,i.e.,“lackofinfrastructure,the complexityofdataintegration,dataconﬁdentiality,andhighcost ofinvestments.”

Fewauthorshavestudiedbigdataforapplicationinbusiness activities.Inthisregard,Gunasekaranetal.(2018)interrogatedthe bigdatabusinessanalytics(BDBA)roleinimprovingthelevelof agilemanufacturingpracticesanddeducedthatmarketturmoilhas anegativeimpactandthatthegradualimplementationofBDBA andhelpstoachievebetterperformanceobjectives.Hazenetal.

(2016)proposedanagendabasedoneighttheoriesforexamining theBDPAimpactonsupplychainsustainabilityintheUSA.Dubey et al. (2019a) investigated BDPA’s influence on“environmental performance (EP)and social performance (SP)”by employing a variance-basedSEMapproachanditwasfoundthatBDPAsignifi- cantlyinfluencedbothEPandSP.Zhongetal.(2015)proposedabig dataapproachforinvestigatingthelogisticstrajectoryfromRFID toenablelogisticsdataintheChinesecontext.ShuklaandMattar (2019)draftedaroadmapforinterventions,prioritizingtofacili- tatetheadoptionofBDAinsustainableauditsystems,usingthe ISMapproach.

Severalemergingvoicesareraisingtheneedforintegratingbig datawithothertechnologiestoleveragethebenefitsofintegra- tion.Yangetal.(2017)reviewedtheconsequencesandadvantages ofusingcloudcomputing(CC)foranalyzingbigdatainthedigital earthandrelated scienceareas.ItwashighlightedthatCCpro- videsmeaningfulsolutionsforbigdata(BD).Hashemetal.(2015) reviewedtheriseofbigdatainCC.Theinterrelationshipbetween CC, BD, Hadoop technologyand big data storage systems was detailed.Also, theresearchchallengeswerehighlighted.Mishra etal.(2017)utilizedsocialmediabigdatatodesignaconsumer- centricbeefSC.Dubeyetal.(2016)identifiedvariousfactorsfor enablingworld-classsustainablemanufacturingthroughbigdata bydeveloping a conceptualframework using confirmatoryfac- toranalysis.Zhong(2018)analyzedRFIDdatasetsforintelligent manufacturingworkshopswithPythoncleaningandclassification algorithms.Byintroducinganintegrativeframeworktoimprove theunderstandingofthecirculareconomy(CE)andarelational matrixillustratingthecomplexityoflarge-scaledatamanagement, Jabbouret al.(2019)demonstrated theCEand BDapplications.

ItmaybenotedthatanefficientSCenablestheefficientflowof data/informationalongwiththefinanceandmaterialflows,and duetoICTadoption.SCcanmonitortheflowofinformationfor dataanalysisforextractingvaluefromthesame(ChaeandOlson, 2013;Souza,2014).90%ofavailabledatatodayhasbeengener- atedinthelasttwoyears(Loechner,2016), and2.50quintillion bytesdataisbeingcreatedperday(Jacobson,2013).Furthermore, itispredictedthatinafewyears,suchanexponentialincreaseof datawillreachzettabyteperyear(Tiwarietal.,2018).

Inthepresentscenario,theindustryisrecognizingthevalueof bigdataandthelatestanalytictoolstoimprovebusiness,ﬁnan-

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cial, and sustainability performance. Further, to improve costs, performance and consolidate the inﬂuence of big data; Dubey et al. (2019b) developed a model describing theimportance of externalresourcesandpressuresusingapre-testedquestionnaire.

Badiezadehetal.(2018)assessedtheperformanceofsustainable SCinthepresenceofbigdatausing“networkdataenvelopment analysis”forestimatingtheefficiencyofmultistageprocesses.Yu etal.(2018)analyzedtheeffectofsupply-drivenSCcapabilities on financial performance using SEM, and a significantpositive effectandlinkagewereobserved.Positiveandsignificantcoordina- tionexistsbetweenresponsivenessandfinancialperformancetoo.

WithaTacticalProcurementPlanningModel(TPPM),OhandJeong (2019)foundanoptimaltrade-offbetweendelayand profit,an optimalsupplyflowoveraplanninghorizon,called“SmartSupply ChainPerformance(SSCP).”Zakietal.(2019)cameupwithacon- ceptualframeworkreflectingtheemergingconnectionsbetween bigdataand manufacturingbystudyingthepatternsandman- ufacturingfactorsaswellastheroleandimpactofbigdata.To understandbigdataandtheseapplications,Brinch etal.(2018) implementedasequentialmixedtechniquealongwiththeDelphi method,aswellasa“SupplyChainOperationsReference(SCOR)”

adjustedbaselineprocessframework.Theyobservedthatdatacol- lectiondominated themanagementand useofdata,whichwas lessrelevantforprocurementormanufacturingthanforlogistics, serviceandplanning.Xuetal.(2019)analyzedthebigdatainﬂu- enceonassessingthequalityofmanufacturers’decisionsonthe manufacture/repackagingofaﬁxedcollectionfeemechanismand adiscriminatorycollectionfeemechanism.Itwasfoundthatthe manufacturervalues differentchargingmechanismsofthecollec- tionaccordingtothelevelsofvalueperceivedbyconsumersofthe productsusedandtheacquisitioncostofbigdata.

3. Researchmethodology

Thepurposeofthepaperistoidentifyanddevelopastructural modelofthecriticalbarriersagainstBDAimplementationandto setuptheirinterrelationshipinthecaseofmanufacturingsupply chainsusinganISMapproach.Furthermore,MICMACanalysiswas utilizedtoidentifythedrivinganddependencepowerofeachbar- rier.Moreover,DEMATELwasalsousedtoovercomethelimitation ofISMmethodologyinestimatingtheintensityofinterrelationship between identiﬁed barriers.The research methodologyis illus- tratedinFig.1.

3.1. InterpretiveStructuralModeling

ISM isone ofthewell-known techniquesforidentifyingthe interrelationshipbetweenvariouslinkedparametersofacomplex system(Warfield,1974;Mudgaletal.,2009).ISMpossessesthe uniquepropertyoftransferringunclearenunciatedmentalmod- elsintoawell-definedstructure.Thus,ithelpsinunderstanding inflictingorderaswellasthedirectionofcomplicatedrelationships (Gardasetal.,2015).Forthis,ISMmakesuseofthegraphtheory.

Therelationshipsofcomplexsystemsareanalyzedbydecompos- ingthemintodifferentlevels.Thus,itmaybeseenasa“structural relationshipdiagram,”whichillustratesthesimpliﬁedformatof acomplexsystem(Sage,1977;Sagheeretal.,2009;Singhetal., 2003).

TheISMprocedurecanbeexplainedthroughthefollowingsteps (Sagheeretal.,2009;Ravi,2015;FaisalandTalib,2016;Gardasetal., 2017;Rautetal.,2017):

Step1:Theconstructofthesystemunderstudyareidentiﬁed andlisted.

Step 2: Identiﬁed constructs are evaluated for the contex- tualrelationship,whichresultsintheformationofa“structural

self–interactionmatrix(SSIM)”torepresentapair-wiseinterrela- tionshipamongstthem.

Step3:AbinarymatrixisdevelopedfromSSIMbasedonrules (explainedlater),andiscalledas“initialreachabilitymatrix(IRM).”

Step4:ThetransitivityisevaluatedinIRMtoobtainthe“ﬁnal reachabilitymatrix(FRM).”Thisfollowstherelation“ifaconstruct

‘A’islinkedto‘B’and‘B’islinkedto‘C’then‘A’iscertainlycorrelated to‘C’.TheobtainedFRMispartitionedintodifferentlevels.

Step5:Thelevel-wiseconstructsareconnectedgraphicallywith thedirectlinksandtransitiveisdroppedtoget“Digraph”andthe ﬁnalmodelisobtainedbyreplacingwithconstructname.

Step6:Finally,theobtainedmodelisevaluatedforanyconcep- tualdisagreementsandmodiﬁedaccordingly.

3.2. FuzzyMICMACanalysis

ThestepsforFuzzyMICMACareasfollows(BhosaleandKant, 2016):

Step1:“Binarydirectrelationshipmatrix(BDRM)”isacquired byconsideringalldiagonalelementsaszeroandrestareunchanged intheIRMmatrix.

Step 2: Develop a “linguistic assessment direct reachability matrix.”

Step3:Developa“FuzzyMICMAC-stabilizedmatrix.”

Step4:Obtainthe“drivinganddependencepowers”ofeach constructanddrawtheMICMACplot.

3.3. DEMATEL

TheDecision-MakingTrialandEvaluationLaboratory(DEMA- TEL)techniquewasdevelopedbytheGenevaResearchCentreof theBattelleMemorialInstituteforunderstandingcausalrelation- shipstructureofacomplexsystem.DEMATELnotonlyevaluates themostcriticalfactorsofthestudiedsystemviaanimpactrela- tiondiagrambut alsotransformsinterdependencyrelationships intocauseandeffectgroupsthroughdigraphandmatrices.

StepsofDEMATEL:

Step1:Compute the“direct relationship matrix”: For this,sup- posetherearemexpertsandnconstructstobestudied.Foreach construct,theexpertgives theiropinionabouttheinfluence of constructionconstructjandsimilarlyforallconstructsusingan integerscaleof“noinfluence(0),”“lowinfluence(1),”“medium influence(2),”“highinfluence(3),”and“veryhighinfluence(4).”

Supposeeachexpertdecisionmatrixisgivenby

x_ij^k

n∗n,thenaver- age“directinﬂuencematrix”(X=x_ijn_∗n)isgivenbyEq.1:

x_ij= 1 m

m k=1

x_ij^k_n*ni,j= 1,2,...n (1)

Step2:Normalizethe“directinﬂuencematrix”:Thenormalized average“directinﬂuencematrix”(A=

a_ij

n∗n)isgivenbyEq.2:

A=X

s (2)

Wheres=max

⎛

⎝

max

1≤i≤n

n j=1

x_ij,max

1≤i≤n

n i=1

x_ij

⎞

⎠

Every element in matrix A obeys the rule 0≤a_ij≤1,0≤

n j=1

a_ij≤1andwehaveatleastoneisuchthat

n j=1

x_ij≤s.

Step3:Findthe“totalinﬂuencematrix”:The“totalinﬂuence matrix”T=

t_ij

n*niscalculatedbyEq.3:

T=A+A²+A³+...A^h=A(I−A)⁻¹ (3)

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Fig.1.Researchmethodology.

Whereh→∞,and“Iisdenotedasidentitymatrix.”

Step4:Formulatethe“inﬂuencerelationshipmap”:Thisisformed usingtwovectorsRandC,wheretheyarethesumsofrowsand columns,respectively,inthe“totalinﬂuencematrix”asshownin followingEq.4and5:

R=[r_i]_n*1=

⎡

⎣

ⁿ

j=1

t_ij

⎤

⎦

n*1

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C=[c_i]_1*n=

_n

i=1

t_ij

1*n

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Where“r_i isthesumofithrowintotalinﬂuencematrix”T and depictsthetotalimpactsofconstructn_i onallotherconstructs.

Similarlyc_jisthesumofthejthcolumnin“totalinﬂuencematrix”

T anddepicts totalimpactsthatconstructn_i is gettingfromall otherconstructs.Withthehelpofthesetwovectors,“inﬂuence relationshipmap”isdrawnwhere(R+C)istakenasxaxisand(R− C)asyaxisforeachconstruct.Onthehorizontalaxis,(R+C)value (alsocalledas“Prominence”)ofaconstructdenotesitsstrengthand isalsothecentralpartofthesystem.Ontheverticalaxis,(R−C) value(alsoknownas“Relation”)istheneteffectofconstructwhich

itputsonthesystem.If(r_i−c_j)valueofaconstructispositive,it showsthatthisconstructhasanetinﬂuenceontheotherconstructs andcanbeclusteredintothecausegroup.Ontheotherhand,if (r_i−c_j)isnegative,theparticularconstructisinﬂuencedbyanother constructinthesystemandcanbeclusteredintotheeffectgroup.

4. Applicationoftheproposedapproachand interpretationsofresults

Theconstructsusedfordevelopingthestructuralmodelswere identiﬁedthroughanexhaustiveliteraturereview.Thesearchkey- words used were “challenges, hindrances, barriers,constraints, obstacles,bigdata,bigdataanalytics,businessanalytics,manufacturingsupplychains,andmulti-criteriadecisionmaking(MCDM).”

ThesekeywordswerefoundonlineindatabasessuchasScopus, WebofScience,ScienceDirect,EmeraldinsightandIEEEexplorer.

ThesearchislimitedtotheEnglishlanguagearticlesonlyandomit- teddissertations.Afterreviewingthearticlesfromtheaboveonline databasesandfromtheopinionofexperts,15barrierswerelisted.

Theexpert team consistedof 47 members dividedinto two groups:theInternetofThingsgroupandtheSCgroup;threedata analysts,twobigdatavisualizers,twobigdatasolutionarchitects, threebigdataengineers,threebigdataresearchers,twodataware-

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Table1

IdentiﬁedbarrierstoBDAimplementation.

S.N Barrier Explanation References

1 Poordataqualityand lackoftrustindata

Dataisintangible,andmeasuringitsqualityisamulti-dimensionalproblem.Poor qualityofthedataisabigbarriertotheactivitiesoftheanalytics,andit signiﬁcantlyaffectsthedecisionsofthemanagement.Thequalityofthedatacan becategorizedintotwodimensions,namelyintrinsic(completeness,consistency, timeliness,andaccuracy),andcontextual(datareputation,accessibility, believability,quantity,value-added,andrelevancy).Itmaybenotedthatthe trustworthinessofthedataobtainedfromsocialmediaisaseriousissue.

WangandStrong(1996);Leeetal.(2002);

Hazenetal.(2014);Hashemetal.(2015);

Tanetal.(2015);Arunachalametal.(2018)

2 Time-consuming activity

Theactivityofpredictiveanalysisisatime-consuminginitiativeandcomprises variousphasesofdevelopment,testing,andadoption.Itisalongtasktobring expertstogetherfromdifferentsectionswithdifferentmindsets.Itmaybenoted thatthecommitmentfromthetopmanagementisrequiredforthe

implementationofBDA,whichmaytakeoneyeartooneandhalfyears.Also, collectingdatafromvarioussectionsoftheorganizations,combining,validating, andcleansingthesameandtrackingthedevelopmenttogetherconstitutea tediousactivity.

Blackburnetal.(2015);Hashemetal.

(2015);Arunachalametal.(2018)

3 Lackofsufﬁcient resources

Inasupplychainnetwork,theresourcecapabilitiesofthedataandanalyticsvary significantlyacrossorganizations.LackofsufficientITcapabilitiesforsharing informationanddatacancauseconsiderabledifferences.Fortheeffective implementationofBDA,formationofcross-functionalteamsandcollaboration betweenthevariouselementswithinthefirmisnecessary.However,while formingtheteam,issuessuchaspoliciesofdatasharing,arrangementsof incentives,etc.needtobetakencareof.FortheefficientutilizationofBDAand businessvaluecreation,fact-basedmanagementanddata-drivencultureneedto bemotivated.

Seahetal.(2010);DuttaandBose(2015);

Hashemetal.(2015);Arunachalametal.

(2018)

4 Lackofsecurityand privacy

TherearevariousissuesassociatedwiththeBDAsystemsimplementation,i.e., ineffectivedataprocessing,unethicalusageofdata,security,andprivacy,which couldinﬂuencetheﬁnalresultsoftheinvestigation.

Tien(2012);Huetal.(2014);Alfaroetal.

(2015);Hashemetal.(2015);Richeyetal.

(2016);Arunachalametal.(2018) 5 Lackofﬁnancial

support

LackoffinancialsupportinfluencestheBDAimplementationdecision significantly.Itmaybenotedthatsupportingandleveragingthecapabilityof cloudcomputingforstoringthedatacouldaddcosttothefirm.Increasingdata generationdemandsmorecloudspace,whichfurtherincreasesthefinancial burdenontheorganization.

Hashemetal.(2015);Rehmanetal.

(2016);Arunachalametal.(2018)

6 Behavioralissues BDAimplementationmayleadtosomebehavioralissueslikeoverand

underestimationduetoreal-timedataandinformationavailability.Here,overand underestimationreferstoanydeviationfromthestandardoperatingprocedure.

ThismayleadtoincreasedinventorycostsandSCrisk.Theemployeemayalsofear joblossduetotechnologyupgradation.Thesebehavioralissuesmaysometime resultinacceptanceofstatisticallysigniﬁcantandunconnectedcorrelations.

Moreover,BDAimplementationleadstobehavioralissuesduetoculturechange.

Hashemetal.(2015);RadkeandTseng (2015);Tachizawaetal.(2015);

Arunachalametal.(2018)

7 Returnoninvestment (ROI)issues

AmbiguityandunclearbeneﬁtsonROImakestakeholdersreluctanttoimplement bigdata.Further,fearofBDAimplementationfailureresultsinlossofconﬁdence torecovertheinvestmentmade.

Davenportetal.(2001);Hashemetal.

(2015);Richeyetal.(2016);Sanders (2016);Arunachalametal.(2018) 8 Lackoftop

managementsupport

ThetopmanagementleadershipshouldleverageandsupportBDAacrosstheﬁrm, asBDAimplementationcanimprovetheoverallperformanceofSC.Lackof motivationfromtopmanagementresultsinlowmomentumforBDA implementation.

Seahetal.(2010);DuttaandBose(2015);

Hashemetal.(2015);Wambaetal.(2015);

Gunasekaranetal.(2017);Arunachalam etal.(2018)

9 Lackofskills Arecentinvestigationhashighlightedthatalackofexpertsinthebigdatadomain isasigniﬁcantissue.Thedatascientistneedstohaveexpertiseinbothanalytical anddomainskills.Duetoinsufﬁcientprofessionalskills,therecouldbeaninability toidentifyappropriatedataanddevelopadaptedmodels.

Russom(2011);WallerandFawcett (2013);Hashemetal.(2015);Schoenherr andSpeier-Pero(2015);Richeyetal.

(2016);Arunachalametal.(2018) 10 Datascalability Datascalabilityreferstotheabilityorcapacitytoaccommodatechangeindata

sizesbyusingextraresources.Itisoftenobservedthatdatasizekeepson increasingwithtime.Afteraparticularperiod,theﬁrmsneedtodumptheirdata forstoringnewlygenerateddata.Fortacklingthescalabilityissues,cloud computingcapabilitymaybeconsidered.

Huetal.(2014);Hashemetal.(2015);

Kangetal.(2016);Richeyetal.(2016);

Rehmanetal.(2016);Arunachalametal.

(2018)

11 Lackoftechniquesor procedures

Lackofefﬁcienttechniquesorproceduresleadstopoorqualityofobtaineddata.In BDAimplementation,lousydataqualityisabigproblem.

MeixellandWu(2001);Fildesetal.

(2009);Blackburnetal.(2015);Hashem etal.(2015);Arunachalametal.(2018) 12 Lackofdataintegration

andmanagement

Dataintegrationmanagementisthecapabilityoftheorganizationtoutilizethe techniquesandtoolsfor“collecting,integrating,transforming,andstoringdata fromvariousdatasources.”DuetothecomplexnatureofBD,theconventional databasemanagementsystemssuchasRDBMSarenotcompatible.Therefore, cloud-basedorweb-basedelectronicdatainterchangesystemsmaybeemployed forenhancingthedataintegrationcapability.Theintegrationcapabilityimproves responsivenessandvisibility.

ChaeandOlson(2013);GeandJackson (2014);Huetal.(2014);RadkeandTseng (2015);Stefanovic(2015);Tanetal.

(2015);Wambaetal.(2015);Sanders (2016);Arunachalametal.(2018)

housemanagers,twodataarchitects,twodatabasemanagers,three businessintelligenceanalysts,twodatawarehouseanalysts,two datamodelers,twodatabasedevelopers,twobusinesssystemana- lysts,twodatamininganalysts,twobusinessdataanalysts,two datascientists,threeprofessorsfromthedepartmentofcomputer engineering,threeprofessorsfromthedepartmentofoperations andSCmanagement,andﬁvesupplychainandlogisticsmanagers

fromthemanufacturingindustriesmadeuptheteam.According toRobbins(1994),thenumber of expertsbetween5and 50 is statisticallyappropriate.Moreover,MurryandHammons(1995) advocatedtheideaof10–30expertsinthedecision-makingpro- cess.TworoundsofDelphiwerecarriedoutamongsttheexperts toreachconsensusabouttheselectionofthebarriersagainstBDA

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Table2

“Structuralself-interactionmatrix”ofbarriers.

implementation.Thisfurtherreducedthenumberofbarriersto12.

TheﬁnalidentiﬁedbarriersarelistedinTable1.

After identifying the twelve critical barriers, the interre- lationships between them are established in the “Structural Self-InteractionMatrix (SSIM)”inTable2bytakinginputsfrom theexperts.Forinterpretationofinterrelationship, fourtypesof symbolswereutilized:

V-“Barrierileadstobarrierj.”

A-“Barrierjleadstobarrieri.”

X-“Barrierileadstobarrierjandviceversa.”

O-“Norelationshipbetweenthebarriers.”

TheSSIMistransformedinto“InitialReachabilityMatrix(IRM)”

throughtheapplicationofthefollowingrules:

“If(i,j)entryinSSIMisV,thenitisreplacedby1,andcorre- sponding(j,i)entrybecomes0inIRM.

If(i,j)entryinSSIMisA,thenitisreplacedby0,andcorrespond- ing(j,i)entrybecomes1inIRM.

If(i,j)entryinSSIMisX,thenitisreplacedby1,andcorrespond- ing(j,i)entryalsobecomes1inIRM.

If(i,j)entryinSSIMisO,thenitisreplacedby0,andcorrespond- ing(j,i)entryalsobecomes0inIRM.”

TheobtainedIRM(Table3)wascheckedfortransitivityissues andthesamewasincorporatedtogetaFRM(Table4).Thispro- cedure was followed by level partitioning which makes use of threesetsi.e.“reachability,antecedentandintersectionset.”The reachability setcontains itself and anotherconstruct onwhich it hasanimpact. However,the “antecedent set”contains itself and allother constructs that help in attaining it. The intersection of these two sets forms the intersectionset. Also, for the construct whose reachability set and intersection set are the same arecategorized aslevel 1and form“thetop levelof ISM hierarchy”(Sage,1977).

Furtherforthenextiteration,theseconstructswereeliminated and asimilarprocedurewasfollowed untilthelast levelofthe hierarchywasreached(Warfield,1974;Rautetal.,2017;Jhaetal., 2018).Table5shows“thereachabilityset,antecedentset,intersectionset,andfinallevels”ofallbarriers.Thelevelpartitioningofall constructswascompletedinsixiterations.WiththehelpofFRM andlevelpartitioning,aninitialdiagraphwasdrawn.Transitivity waseliminatedfromtheobtaineddiagraphtogetafinaldiagraph andthefinalinterpretivestructuralmodel(ISM)wasobtainedby replacingtheconstructnameinthefinaldiagraph.Theinterpretive structuralmodeloftheBDAimplantationbarriersispresentedin

Fig.2,whereBDABxdenotestheBigDataAnalyticsBarriersnumber x.

AFuzzy-MICMACanalysiswasdevelopedtoidentifythemost criticalbarriersagainstBDAimplementationinthemanufactur- ingSCbasedon“thedrivinganddependencepower.”Traditional MICMACconsidersanequal relationshipbetweentheidentiﬁed constructs,whichmayormaynotdepictactualscenariosinreal practice.Forexample,supposetherearethreeconstructsA,Band CwhereAisassociatedwithBaswellasC.However,theextent ofassociationofAwithBandAwithCmayvarydependingon anactualcaseofthesystemunderstudy.Insomecases,Amight haveagreaterassociationwithBascomparedwithCandvice- versa.TraditionalMICMACalwaysconsidersanequalassociation betweeneachassociatedconstructs,whichdoesn’tresembleactual circumstances.Thus,toincreasethesensitivityofMICMAC,theuse offuzzyMICMACispreferredoverthetraditionalone(Bhosaleand Kant,2016).

FuzzyMICMACisafour-stepmethodology,wheretheﬁrststep consistsoftheconstructionofa“binarydirectreachabilitymatrix (BDRM)”andisobtainedbymakingalldiagonalelementsaszeroin IRM.Thesecondstepisthedevelopmentof“linguisticassessment directreachabilitymatrix(LADRM)”.Weconsiderfuzzy“triangu- larmembershipfunction(TFN)”(showninFig.3),whichisdeﬁned byanupperlimit u,amiddlevaluemandlowervalue l,where l<m<u.Theu,mandlarex-coordinateverticesofamembership function(_A(x)),whichmapsfuzzysetAinputintorealnumber interval[0,1]. Themembershipfunction (_A(x)) isgiven bythe followingEq.6:

_A(x)=

⎡

⎢ ⎢

⎢ ⎣

0x<l x−l

m−ll≤x≤m u−x

u−mm≤x≤u 0x>u

⎤

⎥ ⎥

⎥ ⎦

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ThelinguisticscaleforassessmentispresentedinTable6.Torate therelationshipbetweentheconsideredconstructs,theresponses ofthesameexpertswhoparticipatedinanearlierphaseofstudies wereconsidered.TheseresponsesweresuperimposedonBDRMto getLADRM,asshowninTable7.Thedefuzziﬁcationmethodusedis the“bestnon-fuzzyperformance(BNP).”TheformulatoobtainBNP

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Table3

“Initialreachabilitymatrix”ofbarriers.

S.N Barriers 1 2 3 4 5 6 7 8 9 10 11 12

1 Poordataqualityandlackoftrustondata 1 1 0 1 0 0 1 0 0 0 0 0

2 Timeconsumingactivity 0 1 0 0 0 1 1 0 0 0 0 0

3 Lackofsufﬁcientresources 1 1 1 1 0 0 0 0 0 0 0 1

4 Lackofsecurityandprivacy 1 0 0 1 0 1 1 0 0 0 0 0

5 Lackofﬁnancialsupport 0 1 1 1 1 1 1 1 0 0 0 0

6 Behavioralissues 0 0 0 0 0 1 0 0 1 0 0 1

7 Returnoninvestment(ROI)issues 0 0 0 0 1 1 1 0 0 0 0 0

8 Lackoftopmanagementsupport 0 0 1 0 1 1 1 1 1 0 0 0

9 Lackofskills 1 1 0 1 0 0 1 0 1 1 1 1

10 Datascalability 1 1 0 0 0 0 0 0 0 1 0 1

11 Lackoftechniquesorprocedures 1 1 0 1 0 0 0 0 1 0 1 1

12 Lackofdataintegrationandmanagement 1 1 0 1 0 0 1 0 0 0 0 1

Table4

“Finalreachabilitymatrix”ofbarriers.

S.N Barriers 1 2 3 4 5 6 7 8 9 10 11 12 DrivingPower

1 Poordataqualityandlackoftrustondata 1 1 0 1 1* 1* 1 0 0 0 0 0 6

2 Timeconsumingactivity 0 1 0 0 1* 1 1 0 1* 0 0 0 5

3 Lackofsufﬁcientresources 1 1 1 1 0 1* 1* 0 0 0 0 1 7

4 Lackofsecurityandprivacy 1 1* 1* 1 1* 1 1 0 1* 0 0 0 8

5 Lackofﬁnancialsupport 1* 1 1 1 1 1 1 1 1* 0 0 1* 10

6 Behaviouralissues 1* 1* 0 1* 0 1 1* 0 1 1* 1* 1 9

7 Returnoninvestment(ROI)issues 0 1* 1* 1* 1 1 1 1* 1* 0 0 1* 9

8 Lackoftopmanagementsupport 1* 1* 1 1* 1 1 1 1 1 0 0 1* 10

9 Lackofskills 1 1 0 1 1* 1* 1 0 1 1 1 1 10

10 Datascalability 1 1 0 1* 0 1* 1* 0 0 1 0 1 7

11 Lackoftechniquesorprocedures 1 1 0 1 0 1* 1* 0 1 1* 1 1 9

12 Lackofdataintegrationandmanagement 1 1 0 1 1* 1* 1 0 0 0 0 1 7

DependencePower 10 12 5 11 8 12 12 3 8 4 3 9 95

*transitivelinks.

Fig.2.TheISMmodelforBDAimplantationbarriers.

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Table5

Levelpartitionsofthe“ﬁnalreachabilitymatrix”(IterationItoVI).

IterationNo. ReachabilitySet AntecedentSet InteractionSet Level

1. 1,2,4,5,6,7 1,3,4,5,7,8,9,10,11,12 1,4,5,7

2. 2,5,6,7,9 1,2,3,4,5,6,7,8,9,10,11,12 2,5,6,7,9 I

3. 1,2,3,4,6,7,12 3,4,5,7,8 3,4,7

4. 1,2,3,4,5,6,7,9 1,3,4,5,6,7,8,9,10,11,12 1,3,4,5,6,7,9

5. 1,2,3,4,5,6,7,8,9,12 1,2,4,5,7,8,9,10,11,12 1,2,4,5,7,8,9,12

6. 1,2,4,6,7,9,10,11,12 1,2,3,4,5,6,7,8,9,10,11,12 1,2,4,6,7,9,10,11,12 I

7. 2,3,4,5,6,7,8,9,12 1,3,4,5,7,8,9,10,11,12 3,4,5,7,8,9,12

8. 1,2,3,4,5,6,7,8,9,12 5,7,8 5,7,8

9. 1,2,4,5,6,7,9,10,11,12 2,4,5,6,7,8,9,11 2,4,5,6,7,9,11

10. 1,2,4,6,7,10,12 6,9,10,11 10

11. 1,2,4,6,7,9,10,11,12 6,9,11 6,9,11

12. 1,2,4,5,6,7,12 3,5,6,7,8,9,11,12 5,6,7,12

1. 1,4,5,7 1,3,4,5,7,8,9,10,11,12 1,4,5,7 II

3. 1,3,4,7,12 3,4,5,7,8 3,4,7

4. 1,3,4,5,7,9 1,3,4,5,7,8,9,10,11,12 1,3,4,5,7,9 II

5. 1,3,4,5,7,8,9,12 1,4,5,7,8,9,10,11,12 1,4,5,7,8,9,12

7. 3,4,5,7,8,9,12 1,3,4,5,7,8,9,10,11,12 3,4,5,7,8,9,12 II

8. 1,3,4,5,7,8,9,12 5,7,8 5,7,8

9. 1,4,5,7,9,10,11,12 4,5,7,8,9,11 4,5,7,9,11

10. 1,4,7,10,12 9,10,11 10

11. 1,4,7,9,10,11,12 9,11 9,11

12. 1,4,5,7,12 3,5,7,8,9,11,12 5,7,12

3. 3,12 3,5,8 3

5. 3,5,8,9,12 5,8,9,10,11,12 5,8,9,12

8. 3,5,8,9,12 5,8 5,8

9. 5,9,10,11,12 5,8,9,11 5,9,11

10. 10,12 9,10,11 10

11. 9,10,11,12 9,11 9,11

12. 5,12 3,5,8,9,11,12 5,12 III

3. 3 3,5,8 3 IV

5. 3,5,8,9 5,8,9,10,11 5,8,9

8. 3,5,8,9 5,8 5,8

9. 5,9,10,11 5,8,9,11 5,9,11

10. 10 10 10 IV

11. 9,10,11 9,11 9,11

5. 5,8,9 5,8,9,10 5,8,9 V

8. 5,8,9 5,8 5,8

9. 5,9,11 5,8,9,11 5,9,11 V

11. 9,11 9,11 9,11 V

8. 8 8 8 VI

Table6 Linguisticscale.

Linguisticconstruct TFN

“Noinﬂuence(No)” “(0,0,0)”

“Verylowinﬂuence(VL)” “(0,0.1,0.3)”

“Lowinﬂuence(L)” “(0.1,0.3,0.5)”

“Mediuminﬂuence(M)” “(0.3,0.5,0.7)”

“Highinﬂuence(H)” “(0.5,0.7,0.9)”

“Veryhighinﬂuence(VH)” “(0.7,0.9,1)”

“Completeinﬂuence(C)” “(1,1,1)”

isgiveninEq.7.Thedefuzziﬁed“Fuzzydirectreachabilitymatrix (FDRM)”ispresentedinTable8.

BNP_ij= (u−l)+(m−l)

3 +l (7)

Thethirdstepistoobtainthefuzzy-stabilizedmatrix,which followstheruleasgiveninEq.8.

C=A,B=maxk

min(a_ik,b_kj

whereA=[a_ik]andB=

b_kj

(8) Where Ais theBDRM,B isthe defuzziﬁedLADRM andC is theresultantstabilizedmatrix. Finally,each rowandcolumnis summedtoobtainthedrivinganddependencepower,respectively, ofeachconstructs,asshowninTable9.Thefourthstepistouse thisinformationtoplotFuzzyMICMACdiagramasdependenceon x-axisanddrivingony-axis,whichisfurtherdividedintofourclus- ters,i.e.autonomous,dependence,linkagesanddrivingclusteras illustratedinFig.4.

Fig.3. TriangularFuzzyNumber(TFN).

TheoutputoftheISMmodelintheformofFRMwasutilizedfor consideringdirectinﬂuencefortheDEMATELcase.Theresponsesof expertsweregatheredfortheconstructswhereinterrelationship existedinFRMoftheISMmodelusinganintegerscale(seethe ﬁrststepofDEMATELintheresearchmethodologysection).The

“averagedirectinfluencematrix”isillustratedinTable10,while the“normalizeddirectmatrix”ispresentedinTable11.Moreover, the“totalinfluencematrix”isshowninTable12,whichpresentsthe detailsaboutthecasualandeffectclusters.Basedonthesedata,the influencerelationshipplotwasdrawnshowingprominence(R+C) onthex-axisandrelation(R-C)onthey-axis(Fig.5).Forevaluating thesignificantrelationshipbetweentheidentifiedconstructs,the

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Fig.4.“FuzzyMICMACPlot”.

thresholdvalueconceptwasadoptedfromtheworkof(Gardas etal.,2019).

4.1. ISMresults

Fig.2showstheISMmodelofBDAimplementationbarriersin manufacturingSC.TheobtainedISMhierarchyisasix-levelstruc- ture,whichdemonstratesthevariousinterrelationshipsbetween the identified barriers.Fig. 2 is also embeddedwiththe result obtainedfromDEMATEL,intheformofintensitiesbetweenthe differentbarriers.Forinstance,therelationshipintensityofBDAB1 withBDAB2is0.1188.ThebarriersintheISMmodelcanbecate- gorizedintothreegroups,i.e.,mostsignificant,mediumsignificant andleastsignificantbarriers.Thebarriersatthebottomtwolev- elsaremostsignificant,followedbymediumsignificantbarriers in thenexttwo levels.Thetop two levelsaretheleast signifi- cantbarriers.Thus,itisevidentthatthe“LackofTopManagement Support (BDAB8)”atthesixth level and “Lackof financialsup- port (BDAB5)”,“Lackofskills (BDAB9)”and“Lackoftechniques orprocedures(BDB11)”atthefifthlevelisthemostcriticalbar-

Fig.5. DEMATELRelationshipMap.

riersagainstBDAimplementationinmanufacturingﬁrms.Thus, thesebarriersdemandthemaximumattentionofthedecisionand policymakersfortheirsuccessfuleliminationandimprovingthe performanceoftheentiresupplychain.Moreover,thesebarriers drivetheotherbarrierspresent intheabove levels.Thefourth leveloftheISMmodelconsistsofthebarriers“LackofSufﬁcient Resources(BDAB3)”and“DataScalability(BDAB10),”whichfur- therdrivestheothertopthreelevelbarriers.Thethirdlevelconsists ofasinglebarrieri.e.,“LackofDataIntegrationandManagement (BDAB12)”thatdrivesthebarrierspresentinthetoptwolevels.

“PoorDataQualityandLackoftrustonData (BDAB1)”,“Lackof SecurityandPrivacy(BDAB4)”and“ReturnofInvestmentIssues (BDAB7)”areatthesecondleveldrivesthebarriersattheﬁrstlevel, i.e.,“Time ConsumingActivity (BDAB2)” and “BehavioralIssues (BDAB6).”

4.2. FuzzyMICMACanalysis

TheBDAimplementationbarriersinmanufacturingSCinthe Indian contextwere clusteredintofour groups aspresented in Table7

“LinguisticAssessmentDirectReachabilityMatrix”.

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12

B1 0 H 0 VL 0 0 M 0 0 0 0 0

B2 0 0 0 0 0 M M 0 0 0 0 0

B3 C C 0 VH 0 0 0 0 0 0 0 C

B4 VL 0 0 0 0 VH H 0 0 0 0 0

B5 0 L VH M 0 M M L 0 0 0 0

B6 0 0 0 0 0 0 0 0 M 0 0 L

B7 0 0 0 0 H M 0 0 0 0 0 0

B8 0 0 VH 0 VH M VH 0 L 0 0 0

B9 H VH 0 VH 0 0 L 0 0 VL VH H

B10 VH C 0 0 0 0 0 0 0 0 0 C

B11 M M 0 H 0 0 0 0 H 0 0 H

B12 VH L 0 M 0 0 M 0 0 0 0 0

Table8

“DefuzziﬁedFDRM”.

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12

B1 0 0.7 0 0.1333 0 0 0.5 0 0 0 0 0

B2 0 0 0 0 0 0.5 0.5 0 0 0 0 0

B3 1 1 0 0.8667 0 0 0 0 0 0 0 1

B4 0.1333 0 0 0 0 0.8667 0.7 0 0 0 0 0

B5 0 0.3 0.8667 0.5 0 0.5 0.5 0.3 0 0 0 0

B6 0 0 0 0 0 0 0 0 0.5 0 0 0.3

B7 0 0 0 0 0.7 0.5 0 0 0 0 0 0

B8 0 0 0.8667 0 0.8667 0.5 0.8667 0 0.3 0 0 0

B9 0.7 0.8667 0 0.8667 0 0 0.3 0 0 0.1333 0.8667 0.7

B10 0.8667 1 0 0 0 0 0 0 0 0 0 1

B11 0.5 0.5 0 0.7 0 0 0 0 0.7 0 0 0.7

B12 0.8667 0.3 0 0.5 0 0 0.5 0 0 0 0 0