Archetypes of Supply Chain Analytics Initiatives—An Exploratory Study

20 Pages·2017·0.81 MB·English

logistics
Article
Archetypes of Supply Chain Analytics
Initiatives—An Exploratory Study
TinoT.Herden* ID andSteffenBunzel
TechnischeUniversitätBerlin,Straßedes17.Juni135,10623Berlin,Germany
* Correspondence:[email protected]
(cid:1)(cid:2)(cid:3)(cid:1)(cid:4)(cid:5)(cid:6)(cid:7)(cid:8)(cid:1)
(cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:6)(cid:7)
Received:27March2018;Accepted:2May2018;Published:8May2018
Abstract: While Big Data and Analytics are arguably rising stars of competitive advantage,
theirapplicationisoftenpresentedandinvestigatedasanoverallapproach. Aplethoraofmethods
andtechnologiescombinedwithavarietyofobjectivescreatesabarrierformanagerstodecidehow
toact, whileresearchersinvestigatingtheimpactofAnalyticsoftentimesneglectthiscomplexity
when generalizing their results. Based on a cluster analysis applied to 46 case studies of Supply
Chain Analytics (SCA) we propose 6 archetypes of initiatives in SCA to provide orientation for
managers as means to overcome barriers and build competitive advantage. Further, the derived
archetypespresentadistinctionofSCAforresearchersseekingtoinvestigatetheeffectsofSCAon
organizationalperformance.
Keywords: supplychainanalytics;businessanalytics;clusteranalysis
1. Introduction
Even before data got their mainstream reputation of being the “new oil” of the 21st century
predestined to shape the digital economy [1], the potential competitive advantages through data
analyticshadalreadybeenrecognized[2]. Toremainwiththisanalogy,dataanalyticsrepresentsthe
refineryprocessturningrawdataintocompetitivestrength. Analyticsinitselfisnotaleading-edge
invention,butincreasedattentionwasrecentlytriggeredbydevelopmentsininformationtechnology
(IT)providingnewaccesstodata,organizations’needforbetterandfasterdecisionmakingandrecent
bigdataandmachinelearningtoolsenablingnewlevelsofinsightfordecisionmakers[3]. Thefield
ofLogisticsandSupplyChainManagement(LSCM),hasbeenidentifiedasoneearlyadopterand
a long-term user of Analytics [4]. LSCM has been employing operations research approaches for
decadesundusespurpose-specificanalyticstoolsforveryparticularproblems. Asitisconcernedwith
effectivelyintegratingsuppliers,manufacturers,warehouses,andstores,suchthatmerchandisewill
beproducedanddistributedtothecustomerwithasatisfyingservicelevelandwithminimalcostsin
therightmannerconcerningtime,locationandquantity[5],thenecessityforanalyticalapproachesto
achievetheseefficiencygoalsisinevitable. Arecentindustryreportunderlinesthelong-termdata
affinityoftheLSCMsectorfromapracticalpointofviewandthepotentialoflogisticsoperations
generatingtheamountsofdataneededtocreatevaluebyusingAnalytics[6]. However,thereport
emphasizesuntappedpotentialinimprovingoperationalefficiency,customerexperienceorcreating
newbusinessmodelsinthesector. ThefitofLSCMandAnalyticsisfavorablesincethesatisfactionof
customerneedsandrequirementsisaleadingthemeinLSCM([7]p. 12)andthemeaningfulinsight
providedbyAnalyticsisusedtoensurerulesandworkflowsstrengtheningsatisfactionofneedsand
requirements[8].
Scholarsandpractitionersalikehaveprovidedevidencethatadvantagestoperformancecanbe
achievedfromthedomainspecificuseofAnalytics,whichwastermed“SupplyChainAnalytics”[9,10].
In research, several authors have investigated the effects of Supply Chain Analytics. Information
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Logistics2018,2,10 2of20
systemsupportedAnalyticscapabilitieshavebeenindicatedtoimproveLSCMperformance[11]and
AnalyticstendstohaveapositiveimpactonLSCMperformancewithhighdependencyonthefitof
AnalyticsinvestmentandLSCMprocessmaturity[12]. ThepositiveeffectofAnalyticsonLSCMwas
further suggested as context contingent, especially on planning processes [13]. Schoenherr and
Speier-Pero [14] provide a wide variety of perceived benefits including improved supply chain
efficiencyanddecreasedsupplychaincosts. Furthermore,severalscholarscallformoreresearchon
thetopic[15,16].
On the practitioners’ side, industry reports have shown high expectations towards Analytics
in LSCM, especially for reducing inventory, risk and improving batch sizes. Among others,
better customer service, higher efficiency and faster reaction to supply chain issues based on
investments in Analytics have been reported [17]. Furthermore, the development of more
customer-orientedvaluechainswithlowerlogisticscostsduetotheriseofdataandAnalyticshasbeen
suggested[18]. However,investmentsaresomewhatreluctant[19]andindustryreportsshowthat
onlyafewfirmsachieveexcellentperformanceinAnalyticsconcerningLSCM[20]. Themajorityof
firmsislaggingorstrugglingwithAnalyticsinLSCM[21]withmanagersreportingmissingexperience
andlackofknowledgeofhowtoapplyAnalytics[22].
Inthestudiessummarizedabove, Analyticsiscustomarilyconsideredasoneoverallconcept
whilemakingconclusionsonitorderivingpotentialvalueandutilityalthoughsingleexampleswith
individualissuesanddiverseanalyticaltechniquesareconsidered. Especiallysingleexamplesare
usedtohighlightAnalyticsprovidingbenefits[11]orsavings[21]therebyprojectingtheexemplary
benefits to Analytics as a general concept or overall approach. What remains unknown are the
differentoutcomesofdifferentapproachesinrelationtotheirintentionsandexecution. Analyticsis
usuallynotsubdividedfurtheralthoughitpresentsawidefieldwithdifferentobjectives,orientations,
andperspectiveswithoutaunifieddefinition[9]. Inourview,itistoowideofafieldtoassumethatall
reportedeffectscanequallybeappliedondifferentSupplyChainAnalyticsinitiatives,allinitiatives
having the same potential of providing value for an organization or all barriers appearing could
be overcome in a single manner. The sole attempt to subdivide Supply Chain Analytics in extant
researchcanbefoundintheframeworkforAnalyticsapplicationsinLSCM[23],whichfocuseson
off-the-shelfITSystems andthereforeignores important aspectsofAnalyticsas wellasinitiatives
which are not system-specific. To derive more sophisticated and reliable research conclusions on
the effects of Analytics on LSCM, a distinction of Supply Chain Analytics approaches is needed.
WeproposeadistinctionofhoworganizationsapplySupplyChainAnalytics, byusingclustering
on46casestudiesonSupplyChainAnalyticsinitiativesconsideringintendedproblemtobesolved,
execution,techniques,andtheresultingAnalyticssolution. Thus,thisresearchinvestigatespatternsin
theactivitiesoforganizationsapplyingAnalyticstobusinessproblemsinLSCMandexplorestheir
endeavorsandmotivationtoformarchetypesofinitiativeswithexclusivecharacteristics. Theoutcome
oftheinitiativesaswellasalignmentofoutcomeandintentionisoutofscopeforresearch. Regarding
MacInnisFrameworkofconceptualcontributions,thegoalofthisstudyisdifferentiation[24]. Thus,
wewillindicatehowtheidentifiedarchetypesaredifferent,whythisdifferentiationmatters,andhow
theycan beusedfurther. The studyisbased onpublicationsabout manufacturingfirms, retailers
andlogisticsserviceprovidersapplyingAnalyticsinLSCM.Theresearchquestionsthereforestates:
HowcanSupplyChainAnalyticsinitiativesbedistinguished?
Theobtainedarchetypescanprovideguidancetomanagersfortheirindividualissues,andpoints
ofreferencesofotherorganizations’previousactivities,andthus,reducesbarrierstoadoptSupply
ChainAnalyticsintheirownorganization. Thearchetypesrepresenttypesthataredesignedtobe
mostdifferentfromeachothertosupportlearningofmanagersandstudentsaboutSupplyChain
Analytics. However,thecombinationofcharacteristicsofdifferentarchetypesinthecreationphase
ofanewinitiativeinanorganizationisnotrelegatedbutratherencouragedwithanindividualand
specificgoalandapproachtobedesignedbytheexecutingmanager. Forresearchers,thearchetypes
formaframeworktoinvestigatethedifferenteffectsofvaryingapproaches.
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Theremainderofthearticleisstructuredasfollows: Section2providesatheoreticalbackground
with the objective to explain the characteristics chosen to form the archetypes. Section 3 presents
the methodology on how archetypes are formed using cluster analysis. Section 4 explains the
suggestedarchetypesanddiscussestheirimpact. Section5concludesthearticleandSection6provides
finalremarks.
2. TheoreticalBackground
In this section, we will summarize Analytics, Supply Chain Analytics, and characteristics of
SupplyChainAnalyticsinitiatives.
2.1. Analytics
Due to its novelty and evolving nature, a wide variety of definitions of Analytics exists.
Holsapple et al. ([9], p. 134) reviewed many of them to develop a collective definition stating
that Analytics is “concerned with evidence-based problem recognition and solving that happen
withinthecontextofbusinesssituations”. ThisdefinitionhighlightstwospecificaspectsofAnalytics.
Thefirstaspect,problemrecognition,indicatestheexperimentalpartofAnalyticstoachieveagoal
whichisuncertainandunclearinthebeginningrequiringfurtherexploration[25],andthusidentifying
whattheactualproblemis. Thesecondaspect,problemsolving,indicatesthatthevalueofAnalytics
is solely provided if a model or application is deployed and used [25]. This aspect of Analytics is
emphasized prominently in the literature, often specified as making decisions and taking actions
(e.g.,[2,8,26,27]). Bothaspectsestablishacleardistinctionfromdataaggregationinitiativessuchas
dashboardsandreports.
Davenport and Harris [2] presented the benefits of applying Analytics to improve internal
processes or an organization’s competitive position. They illustrated that achieving success with
Analyticsisnotbasedondeployingsoftwarebutratheronthreecategoriesoffactors: organizational,
human,andtechnologicalcapabilities. Organizationalcapabilitiesconsideranalyticalobjectivesand
processes,humancapabilitiesconsiderskills,sponsorshipandculture,andtechnologicalcapabilities
considerdataavailabilityandAnalyticsarchitecture. Whilethemodelsandsoftwareareofteninthe
focusofresearchinAnalyticsduetoapparentpresentationofinsightintothespecificopportunities
ofAnalytics,scholarshighlightallthreestatedcapabilitiesascriticaltodevelopandsuccessfullyuse
modelsandsoftware[8,28].
ThemodelsandsoftwareusedinAnalyticsarecommonlydistinguishedasbeingdescriptive,
predictive,orprescriptive(e.g.,[9,23,29]). Themeaningofdescriptiveanalyticsistwofold. Onthe
onehand,itpresentsthesummaryofdatatoreportandmonitor[23]. Ontheotherhand,itdescribes
root cause analysis used to gain insights about the underlying phenomenon or process [30,31].
Predictive analytics estimates unknown values based on known examples. Prescriptive analytics
determinesand,insomecases,subsequentlyautomatesactionsordecisionstoachieveanobjective
givencurrentandprojecteddata,requirementsandconstraints.
Duetorecenttechnologicaladvances,Analyticsgainedadditionalinterestas“BigDataAnalytics”,
referringtoAnalyticsperformedwithBigData,whichhasbeenreportedtohaveapositiveimpact
on firm performance [32]. Big Data originates in data management issues with technology in the
early 2000s due to high volume, velocity, or variety of data [33], which formed the original three
“V’s” of Big Data. Big Data is momentarily under frequent academic investigation including an
increase of “V’s” (e.g., [32,34,35]) considering several issues with Big Data beyond the aspects of
datamanagementandwithouttheneedforadvancedtechnologiessuchasdistributedstorageand
processing, such as Variability, Veracity, Visualization or Value. However, three “V’s” is a leading
theme[14–16,26,28,30,36].
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2.2. SupplyChainAnalytics
LikeAnalytics,nounifieddefinitionofSupplyChainAnalytics(SCA)exists,whilerarelyoneis
proposed. Souza([10],p. 595)describesitas“focus[ing]ontheuseofinformationandanalyticaltools
tomakebetterdecisionsregardingmaterialflowsinthesupplychain”. WallerandFawcett([15],p.79)
propose a definition while describing the field as (L)SCM data science: “( ... ) is the application
ofquantitativeandqualitativemethodsfromavarietyofdisciplinesincombinationwith(L)SCM
theory to solve relevant (L)SCM problems and predict outcomes, taking into account data quality
andavailabilityissues.”IncorporatingaspectsofbothdescriptionsandthedefinitionofAnalytics[9],
weproposetodefineSCAasfollows: SCAisconcernedwithevidence-basedproblemrecognitionand
solvingwithinthecontextoflogisticsandsupplychainmanagementsituations.
Consequentially,SCAisneitherasingleandclearstep-by-stepapproachtosolvesupplychain
problemsnorlimitedtocertaintasksandprocessesinLSCM.Souza[10]systemizesanddistinguishes
several techniques by the type of Analytics and the SCOR processes affected. The origin of the
list of techniques is not explained and it is not exhaustive. Furthermore, another attempt on
systemization results for an investigation on in-memory technology used in LSCM by grouping
in-memorysoftwareapplicationsforLSCManddesigningaframeworkforanalyticalapplications[23].
ByconsideringthetypeofAnalyticsapplied,whethertheconceptisdatadrivenormodeldrivenand
methodologicalrequirements,off-the-shelfsoftwareapplicationswithanalyticalcapabilitiesusedfor
LSCMfunctionalitiesweregroupedintomonitor-and-navigate,sense-and-respond,predict-and-act,
and plan-and-optimize. However, this categorization ignores objectives, organizational aspects,
andhumanaspects.Finally,examplesofpotentialapplicationsofAnalyticsinLSCMweresummarized
from the perspectives of the user and the tasks [15]. In summary, scholars have stretched a wide
rangeofapplicationsofSCAwithvarioususecasesfordifferentfunctionalitiesandusers,providing
evidencethatSCAistoocomplextoevaluateitsimpactasageneralconcept.
Thegeneralizationhasfurtherimpactonmanagersbycreatingbarriers,whichwewanttoaddress
withthisstudy. Thus, thisresearchfocusesonbarriersrelatedtoamissingunderstandingofhow
toapplySCAonindividualproblemsofanorganizationandsubstantiaterelevantSCAinitiatives.
Sanders [28] provides an extensive overview on barriers of Analytics in the context of LSCM and
presents several barriers of which the following are related to the interest of this research. First,
managers,especiallyinleadershippositions,maynotseethevalueprovidedbyAnalyticsresulting
inmissingvision,understandingofthefullcapacityandhowtochangetheorganizationtoapply
Analytics successfully. Second, so called analysis paralysis hinders organizations from applying
Analyticsbecausetheycannothandletheoverwhelmingopportunities,thespeedoftechnological
changewhatresultsintheinabilitytodefineastartingpoint. Organizationsmaythustrytorandomly
analyze data for some eventual causation, some business units may optimize their sub processes
withlittleglobaleffectororganizationstrytomeasureeverythingatoncewithoutunderstanding
whattofocuson. Third,insteadofexperiencingalackofdata,manyorganizationsdrownindata.
Besides technological issues to handle these amounts of data, organizations do not know how to
leveragetheexistingdatacapabilityandhowtobasedecisionsonit.
2.3. DismantlingSupplyChainAnalyticsInitiatives
ThissubsectiondescribesthecharacteristicsusedtoanalyzeSCAinitiativestoformarchetypes.
Weidentified34characteristicsinanextensivereviewofAnalyticsliteraturewhicharepresentedin
sixcategories. ThecharacteristicsandcategoriesarepresentedinFigure1. DrawingonChaeetal.
(2014)[13],weconsiderSCAinitiativesas(onetime)projectsaimingtoachievesupplychainobjectives
usingevidence-basedproblemsolvingandrecognitionwithafocusoninducingprocessredesign,
tooldevelopmentorlong-termprocesschangessuchasautomationorcontinuingdecisionsupport.
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Supply Chain Objective Analytics Objective
(SCO1) Cost (AO1) Transparency
(SCO2) Quality (AO2) Root cause identification
(SCO3) Time (AO3) Evaluation of performance and
(SCO4) Flexibility environment
(SCO5) Sustainability e (AO4) Segmentation
v
(SCO6) Innovativeness ati (AO5) Support of human decision-making
(SCO7) Customer relationship ti (AO6) New source of revenue
ni
(SCO8) Supplier reliability & risk i
cs Technology
yti (TEC1) Spreadsheet software
Human involvement al
n (TEC2) Statistical software
(HUM1) Internal Analytics experts A
(HUM2) External Analytics experts n (TEC3) Specific algorithms
ai (TEC4) Purpose-built data storage
(HUM3) Internal IT experts h
C (TEC5) Non-purpose-built data storage
((HHUUMM45)) EInxtteerrnnaal l pIrTo ceexspse erxtsperts ply (TEC6) Virtualization
p
(HUM6) External process experts Su Data Management
(DAT1) High Velocity
Type of Analytics (DAT2) High Volume
(TA1) Descriptive Analytics (DAT3) High Variety
(TA2) Predictive Analytics (DAT4) Internal data
(TA3) Prescriptive Analytics (DAT5) External data

FigFuigreur1e. 1C. Chahraaractceterrisisttiiccss ooff aa SSuupppplyly CChahiani nAnAanlyatliycst iicnsitiinatitiviaet. ive.
First, the reasoning behind any initiative should be a shortcoming in a supply chain objective
First, the reasoning behind any initiative should be a shortcoming in a supply chain
(SCO). Either because there is a deficiency in comparison to the theoretic potential or because higher
objective(SCO).Eitherbecausethereisadeficiencyincomparisontothetheoreticpotentialorbecause
performance is aspired. In the literature, several frameworks of performance dimensions indicating
highseurpppelyr fochrmaina nocbejecistivaessp iarreed p.roInpotsheed l[i3te7r–a40tu] rwe,ithseovuet roanlef rbaeminegw uonraknsimoofupselyr faocrcmepatnedce. Sdeivmereanl sions
indicoapteinragtiosunaplp mlyetcrhicasi nreoapbpjeecatri vine smaorest pfrraompoewseodrk[s3 7w–e4 0in]vwesittihgoatuetdo, nbuetb tehien gcautengaonriizmatoiouns ldyifafecrcse pted.
Sevetrraelmoepnedroautsiolyn. aTlhem feotllroiwcsinrge aopbjpeectaivreisn hmavoes bteferna emlaebworoartekds bwaseedin ovne as triegvaietewd o,fb thuetsteh feracmaetwegoorkrisz: ation
diffecrosstt r(eSmCOen1d), oquusallyit.y T(ShCeOf2o)l, ltoimwein (gSCoOb3je),c ftlievxeibsilhitayv (eSCbOee4n), seulastbaoinraabteildityb (aSsCeOd5o),n inanorevvatiievweneosfs these
(SCO6), customer relationship (SCO7) and supplier reliability (SCO8).
frameworks: cost (SCO1), quality (SCO2), time (SCO3), flexibility (SCO4), sustainability (SCO5),
Second, we return to the concept of core capabilities of an analytics competitor: organization,
innovativeness(SCO6),customerrelationship(SCO7)andsupplierreliability(SCO8).
humans, and technology. The organizational aspects can be represented by the analytics objective
Second, wereturntotheconceptofcorecapabilitiesofananalyticscompetitor: organization,
(AO) of an analytics initiative. In accordance with Manyinka et al. [41] and Holsapple et al. [9] we
humans, andtechnology. Theorganizationalaspectscanberepresentedbytheanalyticsobjective
identified six analytics objectives. Based on evidence-based approaches these include the creation of
(AO)troafnsapnaraennaclyy tbiyc sdienmitoiactriavtiez.inIgn daactcao r(dAaOn1c)e, twhei thideMntaifnicyaitniokna oeft arol.o[t4 c1a]uasnesd bHy oelxspaeprpimleenettaatilo.n[9 ]we
ident(iAfiOed2),s tihxea envaallyutaitcisono bojfe bcutisvineses.sB paesrefdoromnanecvei danendc een-vbiarsoendmaenptp (reo.ga.c, hefefsictihenecsye oinr crlisukd aestshesesmcreenatt)i onof
trans(pAaOre3n),c tyheb ysedgemmenotcartaiotinz ionfg pdoaptuala(tAioOn1s )(,inthceluiddienng tpifircoadtuioctns oafndro soetrvcaicuesse) s(AbyO4e)x, ptheeri msuepnptoartti oannd(A O2),
theerveaplluacaetmioennto offb huusminaens sdepceisrifoonr mmaaknicneg a(AndO5e)n, vanirdo nthme ednevte(leo.pgm.,eenffit cainedn icnynoovrartiisokn aosf sneeswsm soeunrtc)e(sA O3),
theseogf mreveenntauteiso (ne.ogf.,p boupsiunleastsi omnosd(einlsc, lpurdoidnugctpsr oord suecrvtsicaens)d (AseOrv6)ic. eIns )a( gAivOe4n) ,inthiteiastiuvpe,p tohret faunlfdilmreepnlta ocfe ment
one objective can be necessary to pursue a consecutive objective. For example, a transparent business
ofhumandecisionmaking(AO5),andthedevelopmentandinnovationofnewsourcesofrevenues
process may be necessary for further analytics approaches leading to supported decision making.
(e.g.,businessmodels,productsorservices)(AO6). Inagiveninitiative,thefulfilmentofoneobjective
Third, going forward with analytics capabilities, the human involvement (HUM) in a specific
canbenecessarytopursueaconsecutiveobjective. Forexample,atransparentbusinessprocessmay
initiative can be incorporated by distinguishing the business functions bringing expertise into the
benecessaryforfurtheranalyticsapproachesleadingtosupporteddecisionmaking.
initiative. Considering Davenport and Harris [2], Bose [8] and Dietrich et al. [42] we identified several
sTpheicridfic, grooliensg info arnwalayrtdicsw iintihtiaatnivaelsy t(iec.sg.c, aspevaebrialli trioelse,st hfreomhu pmroavnidiinnvgo alvcceemsse tnot d(HataU tMo )thine daastpa ecific
initiamtiavneagceamnenbte asin wcoelrlp aos rbautesidnebssy fudnicsttiionng ufriosmhi nusgert htoe spbounssionre sosf tfhuen icntiitoiantisveb) rwinhgicihn gcaenx bpoetrht ibsee into
theininitteiarntiavl eto. tChoe nosrgidanerizinatgioDn aovr eexntperonratllayn cdonHtraarcrteisd.[ 2W],eB aoggsere[g8a]teadn tdheD rioeltersi clehadeitnagl t.o[ 4th2r]ewe geroiduepns tified
severaanlds spixe criofilecs:r oinlteesrninal aannaallyyttiiccss eixnpietriat t(iHvUesM(1e).,g e.,xtseervnealr aalnarolylteiscsf reoxpmerpt r(HovUiMdi2n)g, inatcecrensasl ItTo edxapteartt othe
data(HmUanMa3g),e mexetenrtnaals ITw eexllpaerst b(HuUsiMne4s)s, ifnutenrcntaiol nbufsrionmessu psreorcetsos sepxopnersto (rHoUfMth5)e, ienxitteirantaivl eb)uswinheiscsh can
process expert (HUM6). As we have seen in our analysis, external and internal expertise is not
both be internal to the organization or externally contracted. We aggregated the roles leading to
three groups and six roles: internal analytics expert (HUM1), external analytics expert (HUM2),
internal IT expert (HUM3), external IT expert (HUM4), internal business process expert (HUM5),
external business process expert (HUM6). As we have seen in our analysis, external and internal
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expertise is not mutually exclusive. Depending on the complexity of the initiative, organizations
combineavailableexpertiseinvariousformstoachievesuccess.
Fourth,forthetechnological(TEC)aspectsandfinalcapabilitiestheinfrastructurecanbeamajor
barrier[28]. However,DavenportandHarris[2]directthefocusontoolsandanalyticsarchitecture.
Theyidentifysmallandshortinitiativesdonewithspreadsheetsoftware(TEC1)whichcanbeapplied
byanalyticalamateurs. Analyticalprofessionalshoweverwilleitherusestatisticalsoftware(TEC2)
forexperimentalpurposeordefineandrefinespecificanalyticalalgorithms(TEC3)buildingnewand
oftenpurpose-specifictools. Wedistinguishthelasttwo,sincethisalgorithmmightbeboughtfroma
third-partyvendor.Ontheotherhand,DavenportandHarris[2]discusstheimportanceofdatastorage
andaccess. However,sincetheirinitialwork,thefieldhasseensignificantdevelopments. Opposedto
statisticalsoftwaremodelsgatheringdatafromexistingsystemsorsendingittoathird-partytoexecute
the analytics methods, the classical mode is a newly purpose-built data storage (TEC4). Recently,
theconceptofanon-purpose-builtdatastoretogatherdatafrom(TEC5)isemergingfollowingthe
ideaofcreatingasinglestorageforanalyticalpurposestobedefinedlater. Thisconceptisoftencalled
“DataLake”[43]. Finally,theconceptofvirtualization,asprominentlyknownduetocloudcomputing,
allowsaccesstoanalyticalmethodsandresultsdisconnectedfromtheactualdatainfrastructure(TEC6),
e.g.,withmobiledevices[44].
Fifth,datamanagement(DAT)foranalyticalpurposescanfaceseriouschallengesdemanding
supplementary effort [33]. As explained above, the big data concept represents serious data
management challenges, which we have thus incorporated into the evaluation. This includes a
highvelocityofdata(DAT1)beinganalyzedorcollected,ahighvolumeofdata(DAT2)tobeincluded
in Analytics and a variety of data sources, data structures and data semantics (DAT3). Further,
besidesinternaldata(DAT4),managersaresupposedtobecreativeabouttheinclusionofexternal
data(DAT5)sources[27].
Sixth,reconsideringtheworksofHahnandPackowski[23],andHolsappleetal.[9],thetypeof
analytics(TA)canbedescriptive(TA1),predictive(TA2),prescriptive(TA3)orseveralofthematonce
duetochainingorcombination.
As mentioned in the introduction, the initiative’s outcome has been neglected in the
analysis process. The outcome, which is presented in a percentage or absolute value for savings
orimprovementsinamonetaryvalue,timeorquantityishighlydependentontheindividualcase,
organization,andindustry. Thus,itwasnotconsideredmeaningfulforthederivationofarchetypes.
Additionally, this research aims to recognize what organizations aspire, the intention and the
consequentialexecutioninaqualitativemanner. Quantitativeoutcomesdonotfitthisaim. Wefurther
omitted firm size and organizational form, since our interest is in the initiatives presented by
single projects, which can be a relative small size compared to the size of the organization due
totheintentiontosolveasmallproblem.
Nocharacteristicdescribedaboveismutuallyexclusiveandsomewillcorrelatesincethepresence
ofonecharacteristicmaylikelydemandthepresenceofanother.
3. Methodology
To identify SCA initiative archetypes, we used the machine learning method of clustering.
Clustering is a descriptive or explorative data analysis technique which relies on interpretation
bytheanalystbasedoninsightintotheoriginaldata[45]. ThisfitsMacInnis[24]requirementtouse
analyticalreasoningforfacilitatingtheaspireddifferentiation. Below,wepresentthedatacollection,
analysis,andevaluationprocess.
3.1. DataCollection
Since this research considers case studies from organizations, research databases did not
provide a sufficient source. Based on the insight we gained from the publications presented
above we used key words and synonyms of Analytics (“Data Science”, “Business Intelligence”,
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“Big Data” or “Data Mining”) as well as Analytics Objective (see Section 2.3) in combination with
LSCM(“transport*”,“operationmanagement”,“deliver*”,“valuechain”,“warehous*”,“supplier”,
“resourceplanning”,“inventory”,“materialflow”,“producthandling”,“distribut*”,“shipping”)to
conductanextensivesearchviathegooglesearchengine(withcustomizedsearchresultsdeactivated).
Besides case studies from organizations, we identified several third-party websites, software and
solutionvendorsandorganizationsapplyingAnalytics,aswellasnewswebsites,expertwebsites,
andblogswhichwefurtherusedforsnowballsampling. Finally,weapproachedorganizationsfor
cases. Intotal,weidentifiedashortlistof49Initiativeswithpromisinginformationrichnesstoevaluate
theircharacteristics.
3.2. DataAnalysis
Toidentifyarchetypes,welookedatpreviousresearchsimilartoourintent. (L)SCMarchetypes
aimed at providing managers with understanding of organizational adaptation and performance
evaluationhavebeenidentifiedbynon-hierarchicalclusteringonsupplychainITandorganizational
structurevariables[46]. ThevariableswerecollectedviaasurveyincludingvariablessuchasB2B
e-commercesupplychainintegration,ERPapplications,operationalcentralizationaswellasmarket
and financial performance. Supply chain integration archetypes were investigated to understand
the relationship between integration and performance and to provide parsimonious descriptions
useful for discussion, research, and pedagogy as well as to reveal insight into the underlying
structure [47]. Survey-based collection of variables of customer integration, supplier integration,
internalintegration,businessperformanceandoperationalperformanceandthedataanalysiswith
hierarchical and subsequent non-hierarchical clustering determined the archetypes. The authors
brieflydescribefivearchetypeswiththreebalancedintegrationarchetypesandtwocustomer-leaning
integrationarchetypesindifferentnuances. LSCMjobtypearchetypesweredeviatedfromcollected
job descriptions from a major employment website to provide suggestions on how training and
professional development should occur [48]. Text analysis was used to mechanically code the
job descriptions and hierarchical cluster analysis using Ward’s method was applied to identify
eightarchetypes.Concluding,clusteringhasprovenasresearchmethodinLSCMtoidentifyarchetypes
withthemethodadaptedtotheindividualdataset. Thus,focusingonclusteringasthemethodforour
researchissupportedbypreviousLSCMresearch.
We used the 34 characteristics of SCA initiatives discussed above as binary measures to
systematicallydescribethefoundinitiatives.Tworesearcherscodedthecasesindependently.Thecases
werecodedfromtheperspectiveoftheanalyticsresult’sfinaluserandwiththesupplychainobjective
focused on the value creation process. If a case provided inconclusive evidence for a variable,
the information was sought from the organization or the case was rejected. Thus, three of the
shortlisted case studies were rejected. Both researchers discussed the coding regularly to align
the interpretation of the variables. After coding, each difference was discussed and resolved by
consensuses. TheresearcherscalculatedCohen’sKappaas0.65[49],indicatingasubstantialagreement
onthescaleofLandisandKoch[50].
Asthecollecteddataisbinary,commonmethodstodeterminedissimilaritiesbetweentwoobjects
suchascomputingtheEuclideanorManhattandistancescannotbeemployed. Todealwiththistype
ofthedata,theapproachproposedbyKaufmanandRousseeuw[45]buildingonanadaptedversion
of the similarity coefficient defined by Gower [51] was used to calculate the dissimilarity matrix.
Allvariablesweretreatedasasymmetricbinaryastheydidnotrepresentthepresenceorabsence
of a characteristic but the presence and non-presence due to missing evidence for presence [52],
thusleadingtoadaptionsofthedistancemeasurement. Weusedthestatisticalsoftware‘R’toperform
theclusteringanditsevaluation.
We decided for hierarchical clustering due to the advantage of visually inspecting the
agglomerationsviaadendrogram.WetestedUPGMA,Ward’smethod,completelinkageandWPGMA
andevaluatedthedendrogramsbyoutlierinfluenceduetolateagglomerationsofsingleobservations.
Logistics2018,2,10 8of20
The most promising method in our evaluation has been Ward’s Method visualized in Figure 2.
Duringtheprocess,wehadtoomit(DAT_4),sinceeverycasereliedoninternaldata. AsHairetal.[53]
point out, there is no completely objective way to determine the number of clusters and the final
choicLeogriestmicsa 2i0n18s, 2t,o x tFhOeR PreEsEeR aRrEcVhIEeWr. H owever,theresearchershallbeguidedbyhisresearch8o obf j1e9c tive.
SinceLowgiestiwcs 2a0n18t, t2o, x cFrOeRa PteEErRe aREsoVInEaWb l eclusterswhilekeepingthemconceptionallyknowledg8e aofb 1l9e and
objective. Since we want to create reasonable clusters while keeping them conceptionally
insightful, welimitedourconsiderationstoamaximumofeightclustersanddecidedforthebest
okbnjoecwtilvede.g eSaibnlcee anwde inwsiagnhtt futol, wcree alitme itreeda soounra bcolen scidluesrtaetrios nsw thoi lae mkaexepiminugm thofe mei ghcot nccluepstteiorsn aalnlyd
numberofclustersinthatrangebasedonseveralcriteria: Thechangeinagglomerationdistances
kdneociwdleedd gfeoar btlhe ea nbdes tin nsiugmhtbfuerl, owf ec lliumstietersd ionu rth caotn rsaidnegrea tbioanses dt oo an mseavxeimraul mcr iotefr eiaig: hTth cel ucshtearnsg ea nidn
betwedaeegncgildmoemedre grfoaintri gotnhc eldu ibssetteasntr scnepuse mbaebkteswra eotefnt h cmeluecsrhtgeairnnsg gi encsl utfhsrtoaetmr sr apthnegraeeke sb taaots fethodeu orcnhc alusnesgvteeesrr asfrl oacmnrdi ttehfrrrioeame: Ttofih vfeo eucthro acnsluigxset ceilrnus sters
(non-aacgnogdnl sofemroceumrta iftviivoeeni ntdoti essrtiaxsne ccclteuisso tbneersts)w .(nBeeoanns -emcdoenorsgneinctught eicvldue esitnnetdresrr ospegecartakiomsn as,t)s .t ihBxeac scleuhdas ntoegnre stsh rfeer podmreen stdhernroetgear taorme fa,o ssuoirxn ccallubuslsetteercrsus t-off.
The Darneupdnr nfersoeimnntd faeiv xree [at5os4o s]nixais bclmleu csatuxetir-mso f(ifnz. oeTndh-ecf ooDnrusenficnvu etiinvcdele uixns tt[ee5r4rss]e ,icswt imohnaislx)ei.m Bthiazeseedidn fodonre xftihvreee dmceluansidntresorsgc,r oawnmhs,it lasein xtth cefl uoinsrtdeserisxx and
sevenrreecpmlureassitneensr tcs o.an TrsehtaaesnoStn ifloahbro lseui xce uattnte-doi fsnfe.d vTeehxne c[ D5lu5us]ntesnru sig.n Tgdheesext Ss[i5ls4he]o viusee nmtteca lxiunimsdteeizxre s[d5w 5f]oi strhu fgisvgiexe scctlsluu ssestevteresrn,s cwalushsislteee rctsoh wne diitnhbd seeisxxt and
fivecrcleulmusstateeinrrsss caaossn stshetcaiorndnt dfbo ebr sestsixtc ahannoddic sfeeiv.veAe ncs lcuslsuhtsoetrwesr sna.s Ti nthheFi rSidgil uhboreesute 3tc,theto hiinecded.e xiAf f[se5 5srhe] nsouwcgeng beienstt swF sigeevueenrne fi c3vl, uetshtaeen rdsd iwffseiitrxhe ncslicuxe sters
ismocbrleeutspwtereroesnn ao sfui vsneecc eoadnnddt hbsaiexns tc tlahunasdtte bfrisev teiws cemlueosntreesr ispx raaosnn todhuirsndec vebeden stthc calhunos ittcheera.st A.bWse tsewhdeoewencn is diinxe dFanifgodur rsseei vx3e, cntlh ucesl utdesitrfefser,srs.e inWncceee itis
bdeetcwideeedn ffoivr es iaxn cdlu ssitxe rcsl,u ssitnecres iits ims tohree vpirsounaolluyn mceods t trheaans otnhaabt lbe,e townecee na msixo nagn dth es ebveesnt acnluds toenrsc.e Wthee
thevisuallymostreasonable,onceamongthebestandoncethesecondbestconsideringtheindexes.
dseeccoidnedd b feosrt scioxn csliudsetreinrsg, sthinec ien dite ixse tsh. eIn v tihsue avlilsyu malo esvt arleuaastoionna,b wlee, ocnonces iadmeroendg t hthe ec hbaenstg easn din odnicstea nthcee
Inthevisualevaluation,weconsideredthechangesindistancefromfivetosixclusterorsixtoseven
sferocomn dfi vbee stto c soinx scilduesrtienrg o trh sei xin tdoe sxeevse. nIn n tohte b veiisnuga lc oevnasleucauttiiovne, iwntee rcsoencstiidonerse. dT hthee r cehsualntginegs sinix d cilsutasntecres
notbeingconsecutiveintersections. Theresultingsixclusterswereinterpretedbyconsideringthe
fwroemre fiinvtee rtop rseitxe dcl ubsyt ecro onrs isdixe rtion gse vtheen innoitti abteivinegs cinon esaecchu tcivlues itnetre rasnedc tbioyn sc.o Tmhpea rreinsugl ttihneg asvixe rcalugsetse rosf
initiawtviaverreieas biinlnetsee raapmcrheotncelgdu csbltuye srcteoarnnss.d idbeyrincgo mthpea irniintigattihveesa ivne reaagche scolufsvtearr iaanbdl ebsya cmoomnpgarcilnugs ttehres .averages of
variables among clusters.

Figure 2. Dendrogram of Cluster Analysis with Ward’s method.
Figure2.DendrogramofClusterAnalysiswithWard’smethod.
Figure 2. Dendrogram of Cluster Analysis with Ward’s method.

Figure 3. Cluster Evaluation (best evaluated in grey).
Figure 3. Cluster Evaluation (best evaluated in grey).
Figure3.ClusterEvaluation(bestevaluatedingrey).
4. Results and Discussion
4. Results and Discussion
4. ResultsIna ntdhiDs isseccutisosnio, nwe describe the six found clusters by highlighting characteristics and
combInin atthioisn ss oecf tcihonar, acwteer isdteicssc roibf eth et hine itsiaixti vfeosu innd e acclhu cstluersst erb iyn choigmhpliagrhistoinng w cithha rthacet einriistitaictisv easn idn
Ictnohmet hboiistnhsaeetricsot. nioTsn ho,ef w cfihenaddriaencsgtcesrr iitbshteeicrtseh foeofrs etihx pef roienusientindatt icvtlheuess tirneer seseaabcrhyc hcheliurgssh’t eliirng tihnert cpinormgetacpthaioarinrsa.o cnAt ewllr iiistnhtii tctihsaetai vninedist icawotmiivthebisin ni naa t ions
of chtachlruea scottetehrr ewirsest.ri ceT sthhoee fnf itanhndeainliyngzsiet ditah tteoirvgeeeftoshreeinr ptoerea escxehtnratc clttuh ceso tmerermseinoanrccaholiemtriesps’ .a iCnritlsueorsptnerrewst ahittaihovnet. hb Aeeelinl n inintaiimtaiateitdviv eteoss uiwnnditthehrielnin oaet hers.
cthluesitre rm wajeorre ttrhaeints a. nTahlye zreeds utoltgse athree rv tios uexatlrizaecdt c ionm Fmigounrael it4i ews.i Cthl ukseteyr sp hoainvtes bfeoern e nvaemrye dch taor uacntdeerrislitnices
tchaetierg omrayj oinr tthraei tosr.d Tehr ep rreesseunlttse da rine Sveiscutiaolniz 2e.d3 . in Figure 4 with key points for every characteristics
category in the order presented in Section 2.3.
Logistics2018,2,10 9of20
Thefindingsthereforepresenttheresearchers’interpretation. Allinitiativeswithinaclusterwerethen
analyzedtogethertoextractcommonalities. Clustershavebeennamedtounderlinetheirmajortraits.
TheresultsarevisualizedinFigure4withkeypointsforeverycharacteristicscategoryintheorder
pLroegsisetincst e2d018in, 2S, xe cFtOioRn PE2E.3R. REVIEW 9 of 19
Educating the LSCM process by Alerting LSCM process owner
including new Information as process automatically on indicators of pre-
input for improved process output... defined critical conditions and events…
SCO toimprove quality of process output SCO toimprove various objectives
and subsequent customer relationship AO byproviding (knowledge-based) means
AO byimproving human decision support of (automatic) evaluation, triggering
HUM withexternal Analytics experts and decision support on alert
mixed IT and process teams, who HUM withmixed teams of experts, who
TEC implement models or specific TEC create purpose-built tools (attached to
algorithms in existing systems (existing existing systems or stand-alone), for
in the unchanged process), and DAT high velocity data analysis
DAT introducing (new) external data TA usingdescriptive and predictive
TA usingpredictive techniques techniques
Observing of LSCM process conditions Advancing the LSCM process and/or
indicating causes for process deficiencies business model based on newly gained
based on newly gained process insight… process dataavailability andinsight…
SCO to(insightfully) improve quality of the SCO tochange and thus improve the process,
process and its costs and its quality and create innovations
AO byidentifying (data-based) causes of AO byproviding (data-availability-based)
quality deficiencies and providing transparency and causes to enable
evaluation means based on causes (formerly unavailable) evaluation and
HUM withmixed teams of experts, who decisions
TEC use statistical tools to create purpose- HUM with(mostly) internal experts, who
built tools (attached to existing systems TEC create (stand-alone) purpose-built tools
or stand-alone), by connected to data storages without
DAT analyzing internal data with high predefined purpose (“data lakes”), by
volume and variety DAT analyzing data with high velocity,
TA usingpredictive techniques volume and variety
TA usingdescriptive techniques
Refining LSCM process by faster,
broader, and more frequent guidance for
Investigating LSCM process and asset
workers or decision makers to act on…
deficiencies to identify causes and
SCO toimprove costs and reduce time of
enable solution search and design…
actions based on process innovations
SCO toimprove costs and quality of
AO byproviding human decision support
processes and assets
HUM withmixed teams of experts but strong
AO byidentifying causes
internal expertise, who
HUM withmixed teams but strong external
TEC create purpose-built tools combined
Analytics expertise, who
with new storage systems, for
TEC apply statistical tools, for
DAT analyzing data with high velocity and
DAT analyzing internal data
volume
TA usingdescriptive techniques
TA usingprescriptive techniques

FFiigguurree4 4.. PPrrooppoosseeddS SuuppppllyyC ChhaaiinnA Annaalylytticicssa arrcchheetytyppeess( n(nooc chhrroonnoolologgyyo orrs seeqquueenncceei nintetennddeedd).).
4.1. Cluster 1—Educating
The initiatives in the Educating cluster focus on gathering data (sources) new to the organization
and process, that are used as more advanced input in the decision-making of the LSCM process to
improve output but will not lead to process redesign. This cluster contains mainly forecasting
initiatives with the specific goal of providing the right amount of goods without storing excess
inventory or having stock-outs such that the consumer of goods is served best (e.g., the use of data
on weather, income, historical sales, or regional marketing campaigns to determine inventory
allocation to individual stores). Thus, the objective is to improve process quality and customer
relationship. The tools used in these initiatives are dominantly predictive and aim to produce more
Logistics2018,2,10 10of20
4.1. Cluster1—Educating
TheinitiativesintheEducatingclusterfocusongatheringdata(sources)newtotheorganization
and process, that are used as more advanced input in the decision-making of the LSCM process
to improve output but will not lead to process redesign. This cluster contains mainly forecasting
initiatives with the specific goal of providing the right amount of goods without storing excess
inventoryorhavingstock-outssuchthattheconsumerofgoodsisservedbest(e.g.,theuseofdata
onweather,income,historicalsales,orregionalmarketingcampaignstodetermineinventoryallocation
to individual stores). Thus, the objective is to improve process quality and customer relationship.
Thetoolsusedintheseinitiativesaredominantlypredictiveandaimtoproducemorepreciseinputs
fordecision-makinginconsecutiveprocessesofinventoryallocation. Insomeinitiatives,noevidence
onhowtheconsecutiveprocessesarefurtheraffectedbythetoolcanbefound. Thereportonone
initiativespecificallystatesthatthisisintended,sinceemployeesshallusetheoutputofthemodel—the
prediction—andnotquestionit. Thefocalorganizationusuallybuysacustom-builtorcustomized
advancedtoolorsystemextensiondevelopedbyexternalAnalyticsexperts,whichincludesexternal
data sources new to the forecasting organization and combinesit with internaldata fromexisting
systemstointegrateitwiththesesystems.
4.2. Cluster2—Observing
The initiatives in the Observing cluster concentrate heavily on predicting LSCM process
deficiencies in the short-term or medium-term future with a newly developed tool observing and
monitoringtheprocessesgainreactiontimetoeitherpreventthedeficienciesorenablecounteraction.
Thepredictionisbasedonprocessconditionsindicatingthosedefinitelyidentifiedintheinitiative.
Thus,observingindicateswatchingwithknowingwhattopayattentionto.Thesupplychainobjectives
aremixedbuttendtofocusoncostreductionandqualityimprovement. Theprocessissoughttobe
improvedbyobservingandavoidingidentifiedcausesofqualitydeficienciesbutnotessentiallyby
changingorredesigningtheprocess. TheinitiativesinCluster2describeavarietyofdataexperiments
toimproveprocessaccuracyandquality(e.g.,identifyingproductionqualityindicatorsthatmustbe
monitored,estimationofproductweightbasedonpackageweightstosequentiallyusepackageweight
asqualityindicatorforcorrectitems,identifyinfluencingfactorsonpunctualityofarrivaltoadjust
planswhenfactorsarepresent). Inadistinctproportionofinitiatives, themaintenanceprocessof
anasset,machine,orvehiclewasunderinvestigation. Theactorsintheanalyticsteamaremixedfrom
internalandexternalexpertsforAnalyticsandIT.Processexpertsareusuallyinhouse. Thesoftware
usedincludesspreadsheets—theclustercontainstheonlyinitiativetheauthorscouldidentifyusing
spreadsheet software (in combination with statistical software)—and statistical software but no
specificallydesignedalgorithm. Thetechniquesusedareprimarilypredictiveandanalyticallyaimed
atanticipatingthebehaviorandevaluatingtheperformanceofaprocessaswellasunderstanding
thecausesoftheprocessbehavior. Thus,theyusuallyexploitpatternsinthedataopposedtoprocess
knowledgeasinAlertinginitiatives. Dataiseitherstoredinpurpose-builtdatastoragesorgathered
fromexistingsystems. Inaddition,cloudcomputingwasusedinsomeinitiativestoprovideaccessto
theautomaticallyevaluatedprocessestofacilitatemonitoring.Externaldataisbarelyused,butinternal
datacomesinhighvolumesandvarietytocreatesophisticatedprocessinsighttools.
4.3. Cluster3—Alerting
The initiatives in Cluster 3 use similar approaches as the initiatives of Cluster 2. However,
theproductoftheinitiativedivergeswithCluster3initiativesaimingtoproduceasupportsystem
for process owners. These are supposed to call for attention or alert in certain, especially critical,
processconditionsorevents,whicharemostlypredefinedratherthanidentified. Criticalrefersto
negativeprocesseffectsorpossiblelossofrevenue. Theon-demandattentioncontributestomeet
varioussupplychainobjectivesincludingcostreduction,qualityimprovement,flexibilityincrease