Forecasting and Risk Analysis in Supply Chain Management

MMaassssaacchhuusseettttss IInnssttiittuuttee ooff TTeecchhnnoollooggyyEEnnggiinneeeerriinngg SSyysstteemmss DDiivviissiioonn

Working Paper Series

ESD-WP-2008-20

FORECASTING AND RISK ANALYSIS INSUPPLY CHAIN MANAGEMENT

Shoumen Datta1, Clive W. J. Granger2, Don P. Graham3and Olli-Pekka Hilmola4

1Research Scientist, Engineering Systems Division, Department of Civil and Environmental Engineering,

Research Director & Co-Founder, MIT Forum for Supply Chain Innovation, School of Engineering,

Massachusetts Institute of Technology, Cambridge MA [email protected]

2Research Professor, Department of Economics, University of California at San Diego, La Jolla CA 92093

[email protected]

3Founder and CEO, INNOVEX [email protected]

4Professor of Logistics, Lappeenranta University of Technology, Kouvola Research Unit, Prikaatintie 9, FIN-45100 Kouvola, Finland

[email protected]

October 2008

of19Dattaetalshoumen@mit.eduMITForumforSupplyChainInnovation,ESDCEE,SchoolofEngineering

ForecastingandRiskAnalysisinSupplyChainManagement

ShoumenDatta1,CliveW.J.Granger2,DonP.Graham3andOlliPekkaHilmola4

1ResearchScientist,EngineeringSystemsDivision,DepartmentofCivilandEnvironmentalEngineering,ResearchDirector&CoFounder,MITForumforSupplyChainInnovation,SchoolofEngineering,MassachusettsInstituteofTechnology,[email protected]

2ResearchProfessor,DepartmentofEconomics,UniversityofCaliforniaatSanDiego,[email protected],[email protected],LappeenrantaUniversityofTechnology,KouvolaResearchUnit,Prikaatintie9,FIN45100Kouvola,[email protected]

Abstract

Forecastingisanunderestimatedfieldofresearchinsupplychainmanagement.Recentlyadvancedmethodsarecomingintouse.Initialresultsareencouraging,butoftenrequirechangesinpoliciesforcollaborationandtransparency. In this paper we explore advanced forecasting tools for decision support in supply chainscenariosandprovidepreliminarysimulationresultsfromtheir impactondemandamplification. Itappearsthatadvancedmethodsmaybeusefultopredictoscillateddemandbuttheirperformance isconstrainedbycurrentstructuralandoperatingpolicies. Improvementstoreducedemandamplification, forexample,maydecreasetheriskofoutofstockbutincreaseoperatingcostorriskofexcessinventory.Keywords

Forecasting,SCM,demandamplification,riskmanagement,intelligentdecisionsystems

1. Introduction

Uncertainty fuels theneed for riskmanagement although risk, if adequatelymeasured,maybe less than

uncertainty,ifmeasurable.Forecastingmaybeviewedasabridgebetweenuncertaintyandriskifaforecast

peels away some degrees of uncertainty but on the other hand, for example,may increase the risk of

inventory. Therefore, forecasting continues topresent significant challenges.Boyle et al (2008)presented

findings from electronics industry,where Original EquipmentManufacturers (OEM) could not predict its

demandbeyonda4weekhorizon.Moonetal (2000)presenteddemand forecasting fromLucent (Alcatel

Lucent),demonstrating improvement in forecastingaccuracy (60% to8085%).Relatedobservations (Datta

2008a)resultedininventorymarkdowns.

MITESDWorkingPaperSeries:ForecastingandRiskAnalysisinSupplyChainManagement

Page2of19Dattaetalshoumen@mit.eduMITForumforSupplyChainInnovation,ESDCEE,SchoolofEngineering

Availabilityofincreasingvolumesofdata(Reyesetal2007,ReyesandFrazier2008)demandstoolsthatcan

extractvaluefromdata.Recentresearchhasshownthatadvancedforecastingtoolsenableimprovementsin

supplychainperformance(Zhaoetal2001,Zhaoetal2002,Bayraktaretal2008,WrightandYuan2008),if

certainprerequisitesareoptimized(orderingpolicies, inventorycollaboration).Autoregressivemodelshave

been effective inmacroeconomic inventory forecasts (Albertson and Aylen 2003). Zhao et al (2002) and

Bayraktaretal(2008)emphasizethattheroleofforecastinginsupplychainistoindicatetherightdirection

fortheactorsratherthanbeingexactlyright,ateverymoment.Choosingthecorrectforecastingmethod is

oftenacomplexissue(ChatfieldandYar1988).

Thepurposeofthiswork istoexplorehowadvancedforecastingmethodscouldbeapplied inglobalsupply

chainmanagementandtheirrequirements.Ourworkusessimulationofa4stagesupplychainmodel,beer

game (Vensimsimulation).WehavealsousedSPSSstatisticalanalysissoftware toconstructautoregressive

forecastingmodels. The problemsmay be described as: (1) how to construct autoregressive forecasting

models for a supply chainenvironment and (2)what changes areneeded in supply chains to apply these

advancedforecastingmodels?

Inthenextsection,we introduceafewchallengesrelatedtosupplychainmanagement,especiallydemand

amplification.Insection3wediscusssomefeaturesofautoregressivemodelsandgeneralizedautoregressive

conditional heteroskedasticity (GARCH). Supply chain and demand amplification issues are presented in

section4.Concludingthoughtsandfurtherresearchissuesareproposedinsection5.

2. Logistics,SupplyChainManagementandDemandAmplification

DespiterapidadvancesinSCMandlogistics,inefficienciesstillpersistandarereflectedinrelatedcosts(Datta

etal2004).Indevelopingnationstheactualamountsarelower,butproportionalshareishigher(Barrosand

Hilmola2007).Oneofthelogisticallyunfriendlycountrygroupsareoilproducers(Arvisetal2007).



Thehighcost foroperationsofferprosperity for theserviceproviders. In2006,APMollerMaersk raked in

US$46.8billion inrevenues(HilmolaandSzekely2008).DeutschePostreportedrevenuesof63.5billion in

2007(AnnualReport2007).Profitabilityandgrowthoftheseservicesareincreasing,fueledbyglobalization.

Global transportationgrowthexceedsglobalGDPgrowth (UnitedNations2005&2007),since tradegrows

twiceasfastasGDP.Fordecadescompaniesemphasizedlowerinventoriesandstreamlinedsupplychainsbut

ithasresultedinasituation(Chenetal2005,Krosetal2006)wherematerialhandlingindistributioncenters

has increased (transportation growth combined with lower lot sizes).Management science and practice

continuestoexplorewaystodecreasetransactioncosts(Coase1937,Coase1960,Coase1972,Coase1992)

throughrealtimeinformationarbitrage.Cooper&Tracey(2005)reportedthatinthe1990stheretailerWal

Mart had an information exchange capacity of 24 terabytes. While massive investments in information

technology(IT)maybeprudent,thesheervolumeofdatabegstoaskthequestionwhetherwehavethetools

toseparatedatafromnoiseandifwehavesystemsthatcantransformdataintodecisionableinformation.

Insupplychainmanagement,theissueofdemandamplificationorBullwhipeffecthasbeenintheforefront

forsometime(Forrester1958,Leeetal1997)butittookdecadesbeforeitsimportancewasrecognizedwith

concomitantdevelopmentofsupplychainmanagement(Oliver&Webber1982,Houlihan1985)catalysedby

globalization, strategic importance of logistics and appearance of information technology. Small demand

changes in the consumerphase results ina situation,where factoriesandothervalue chainpartners face

suddenpeaksanddownturns indemand, inventoryholdingsandcorresponding impactonproductionand

delivery (deliverystructurephenomenondue toBullwhipeffect isreferred toasreverseamplificationby

Holweg&Bicheno(2000)andHinesetal(2000)referredtoitassplashback).Humaninterventiontotame

theBullwhipeffect,comparedtosimpleheuristics,leadstohigherdemandamplification(Sterman1989).

It follows that demand amplification may have serious consequences due to increased uncertainty and

increasesthesignificanceofriskmanagement.Duringadownturn,Towill(2005)showedthatamplification

causespossibleshortagesonproductvolume(productsarenotordered,evenifdemandhasnotdiminished)

andvarietyaswellasonidlecapacityinoperationsandmayinvolvepotentiallayoffcosts.Incaseofpositive

demand,Towill(2005)identifiesthatstockdeteriorationandsalescannibalizationproduceslostincome.



Consumerspurchaseproducts luredbydiscountsandthatdiminishessales inthefollowingtimeperiodsor

seasons (Warburton & Stratton 2002). During upswings, operations run into premium cost rates in

manufacturinganddistribution (orders increase rapidly),butalsodecreasesproductivitydevelopmentand

increaseswaste levels.Recentemphasisonoutsourcingand largescaleutilizationof lowcostsourcinghas

worseneddemandamplification(Lowson2001,WarburtonandStratton2002,StrattonandWarburton2003,

Hilletofth andHilmola 2008).Risks associatedwithproduction and transportationdelays are considerably

higher.Tomitigatesuch risks,somecorporationsareusing responsivenessasastrategicdifferentiatorand

have built their supply chains to react onmarket changes throughmore localized supply networks, for

example,Benetton (Dapiran1992), Zara (Fraiman and Singh2002),Griffin (Warburton and Stratton2002,

StrattonandWarburton2003),Obermeyer(Fisheretal1994)andNEXT(Towill2005).Thecarbonfootprintof

sourcing strategieswillbecome increasingly relevantand logisticsmayultimatelybenefit fromadisruptive

innovation(Datta2008b).

Inrecentdecades,evenmacroeconomistsare including inventoryasakey indicatorofeconomicdeclineof

nationaleconomies(Ramey1989,AlbertsonanAylen2003).Ramey(1989)arguedthatmanufacturing input

inventories,rawmaterialsandworkinprocess(WIP),fluctuatedmostinrecession,whileenditemorfinished

goods inventoriesfluctuate less(Table1).However,the labourmarketvolatility isalsoan issue inchanging

economicenvironments(Ramey1989).Although,inventorypositionsseemtofluctuate,AlbertsonandAylen

(2003)argue thatautoregressive forecastingmodelsareable to forecastnextperiod situationwitha50%

accuracy.Whileautoregressivetechniqueshavebeenwidelyusedinfinance(andeconomics)inthepastfew

decades,theymaynothavebeenappliedorexploredasdecisionsupporttoolsbysupplychainplannersor

analystsintheareaofsupplychainmanagement(Dattaetal2007).



Table1.InventorychangesinrecessioninUSAduring19601982(Ramey1989)

Recessions Retail Wholesale

Manufact.Finished Inventories

Manufact.Input Inventories

1960:1-1960:4 -6.3 -1.7 -3.1 -6.31969:3-1970:4 -8.2 1.2 -0.4 -5.21973:4-1975:1 -16.0 -5.8 2.4 -13.21980:1-1980:2 3.6 1.9 -0.3 -4.11981:3-1982:4 -7.6 -2.3 -7.8 -11.1

All numbers billions of US dollars (1972), annual rates of change.

2

0

2

4

6

8

10

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Computers Semiconductors

Figure1.CapacityadditionchangeinUScomputerandsemiconductor19862008(FederalReserve2008)

Logicofdemandamplification isevident ineconomiccycles(Forrester1976,Sterman1985).Orderbacklog,

existinginventoryholdings,amountofproduction,amountofemploymentandcapacityadditionswereused

insimulationtrialstoforecastdifferentlevelofeconomiccycles.InlongtermchangesbothForrester(1976)

andSterman (1985)haveemphasized the importanceofcapacityadditions.Similarmethodologyhasbeen

usedinmaritimeeconomicstoestimatepricelevelchanges(Dikosetal2006)andinvestmentcyclelengthsin

capitalintensiveindustries(Berends&Romme2001).Hilmola(2007)hasshownthatcapacityadditionsinUS

insemiconductorsandcomputersindustrymayexplainthebehaviourofstockmarketindices.



3.AdvancedStatisticalModels

Forecastingdemand isakeytool inmanaginguncertainty.Forecastaccuracydependsontheunderstanding

andcoverageofparametersaswellastheaccuracyofhistoricdataavailableforeachvariablethatmayhave

animpactontheforecastorpredictiveanalytics.

OneoftheassumptionsintheClassicalLinearRegressionModels(CLRM)relatestohomoskedasticity(homo

equal,skedasticityvarianceormeansquareddeviation(2),ameasureofvolatility)orconstantvariance

fordifferentobservationsof theerror term.Forecasterrorsareheteroskedastic (unequalornonconstant

variance). For example, inmultistage supply chains, the error associatedwithmanufacturers forecastof

salesoffinishedgoodsmayhaveamuchlargervariancethantheerrorassociatedwithretailersprojections

(theassumptionbeingthattheproximityoftheretailertotheendconsumermakestheretailerofferabetter

ormore informed forecastof futuresales through improvedunderstandingofendconsumerpreferences).

TheupstreamvariabilityreflectedintheBullwhipEffectviolatesthebasicpremiseofCLRM,theassumption

ofhomoskedasticity.CLRMignorestherealworldheteroskedasticbehavioroftheerrortermtandgenerates

forecasts,whichmayprovideafalsesenseofprecisionbyunderestimatingthevolatilityoftheerrorterms.

Homoskedastic

Heteroskedastic Bullwhip Effect

Figure2.Illustrationspfhomoskedasticity,heteroskedasticityandtheBullwhipEffect.

Inahomoskedasticdistribution,observationsoftheerrortermcanbethoughtofasbeingdrawnfromthe

samedistributionwithmean=0andvariance=2foralltimeperiods(t).



Adistribution isdescribedasheteroskedasticwhenobservationsof theerror termoriginate fromdifferent

distributionswithdifferingwidths(measureofvariance). Insupplychains,thevarianceoforders isusually

largerthanthatofsalesandthedistortionincreasesasonemovesupstreamfromretailertomanufacturerto

supplier.Therefore, theassumptionofheteroskedasticity,over time, seemsappropriateasacharacteristic

thatmaybeassociatedwithdemandamplificationortheBullwhipeffect.

Whilevarianceoferrortermmaychangeacrosscrosssectionalunitsatanypointintime,itmayalsochange

overtime.Thisnotionoftimevaryingvolatilityisfrequentlyobservedinfinancialmarketsandhasbeenthe

drivingforcebehindrecentadvancementsintimeseriestechniques.Theseadvancesfromeconometricsmay

bedevelopedintotoolsforforecastingandrisksanalyticswithabroadspectrumofapplicationsinbusiness,

industry, security and healthcare (Datta 2008d), as well as decision support systems and operations

management,forexample,supplychainmanagement(Dattaetal2007).

ImpactofRealTimeHighVolumeDatafromAutomaticIdentificationTechnologies(AIT)

TrackingtechnologiesevolvedfromthediscoveryoftheRADARatMITinthe1940s.AITisslowlybeginning

to impact information flow in themodern value chainnetwork. Trackingproducts frommanufacturers to

retailersthatmayhavestarted inthe1970swiththe introductionofthebarcodeto identifystockkeeping

units(SKU), isgraduallyembracingAITorauto id,forexample,useofradiofrequency identification(RFID).

AITmakes itpossibletoelectronically logproductmovement inthedigitalsupplychain.This information

maybeavailableinrealtime.However,thestandards,suchastheelectronicproductcode(EPC),tocapture

uniqueidentificationofphysicalobjectsandprocesses(Datta2007a)callsforaparadigmshift(Datta2008c).

SinceRFIDupdatesreportseverytimean individual itemorSKUmovesfromonestagetoanotherstage in

thesupplychain,orwhen the item issold, it ispossible todetermine thedemand foran item inrealtime

ratherthanwaitforbatchupdatesorweeklyormonthlybucketstogenerateaforecast.Thegranularityof

thedatafromauto idsystemsmayresult inveryhighvolumedatawhichmayrevealpeaksandtroughsof

demandfortheproduct,hourlyordaily.Thisvolatilityislostwhendataisaggregatedinbucketsorbatch.



Extractingthevaluefromthishighvolumenearrealtimedataanddecipheringthemeaningofthe implicit

volatilitymaybeaboontobusinessintelligenceandpredictiveanalytics,includingforecasting.

Indeedmanywarehouses adopt an inventory policy of ordering productswhen stock levels fall below a

certainminimumamountsandorderuptoamaximumamountS[the(s,S)policy].Withautoiditispossible

toascertain thisat the instant the threshold isattained, thereby,eliminating the likelihoodofoutofstock

(OOS).Hence,itfollowsthatcapitalizingontheincreasedvolumeofnearrealtimedemanddatafromautoid

mayhaveprofoundimpactonsupplychainforecasting.

However,currentsoftwarewithitsCLRMenginesandclusteringapproach,doesnotimproveforecastseven

with high volume data. The assumptions implicit in CLRM limits the gains in forecast accuracy from high

volumedataandfailstoshowreturnoninvestment(ROI)fromadoptionof(new)autoidtools.

It isourobjective to justifywhydeploymentofnew tools, forexample,auto id,calls foradoptionofnew

techniques fordata analytics, for example, advanced techniques from financial econometrics. It is safe to

state that new streamsofdata emerging from amultitudeof sources, for example, auto id and sensors,

cannot yield value or ROI if used in conjunctionwith archaic software systems running ancient forms of

analyticalenginesthataretypicallyCLRMbased,atleast,intheforecastingdomain.

To extractdecisionable information fromhigh volumenear realtime auto id and sensordata, theuseof

techniques likeGARCHdeserves intenseexploration.The factthatGARCHmaybeaclue togeneratingROI

fromautoidandRFIDdataisnoaccidentbecauseGARCHrequireshighvolumedatatobeeffectivelyutilized

andgenerate resultswithhigheraccuracy levels.Hence, this convergenceofauto iddatawith tools from

econometricsmaybean innovativeconfluencethatmaybeuseful inanyvertical inanyoperation including

securityandhealthcare,aswellasobviousandimmediateuseinsupplychainmanagement.Dattaetal2007

andthischapter,hasattemptedtohighlighthowtoextracttheadvancesineconometricsfromtheworldof

financeandgeneralizetheirvaluableuseindecisionsystems,ingeneral.



Evolutionofsuchatoolmayhelpanalystsandplanningmanagerssinceakeyconcernofanymanageristhe

accuracyofthepredictionsonwhichtheirbudgetisbased.Theproperallocationofresourcesforacquisition

ofpersonnelandequipmenthaslongbeenplaguedbyerrorsintraditionalforecasting.Apartoftheanswer

tothisproblemmaybelatentinthepotentialforthecombineduseofVAR(vectorautoregression)withother

forecastingtechniques.TheprimaryVARmodelbestsuitedfortheplanningfunctioninresourceallocationis

the standardGARCHmodel. It iswell suited forpragmatic studies involving supply chain, armypersonnel

requirementsanddefenseequipmentrequirements.Anysystemthatmaybemodeledusingtimeseriesdata,

mayexplorehow to includeGARCHbasedon theerrorcorrection innovation thatmay improve forecasts.

ForecastaccuracyofGARCHmodelmaybequantifiedinanumberofdifferentways.Traditionalmethodsare:

1. MeanSquareError

2. MeanAveragePercentageError

3. AikikiInformation

Oncetheforecastisdeveloped,theaccuracycanbemeasuredbycomparingtheactualobservedvalueswith

thepredictedvalues.Ifthecollecteddatafallswithintheconfidenceintervaloftheforecastmodel,thenthe

modelprovidesagood fit forthesystem.AlthoughGARCH isuseful in forecasting it is importanttorealize

thatitwasdesignedtomodelvolatilityandcanbeappliedtopositiveseriesbutnottoeconomicseries.

GARCHProofofConcept

AlthoughGARCHmodelshavebeenalmostexclusivelyusedforfinancialforecastinginthepast,wepropose

(Dattaetal2007b) thatwithappropriatemodifications, itmaybeapplicable tootherareas.Anoperation

exhibiting volatility may benefit from VARGARCH in addition to other techniques, in isolation or in

combination.It isknownthat intimesofconflictmilitarysupplychainsexperiencespikes indemand.These

spikesandtroughsoccuroverashortperiodoftimeandresultinlossesduetoOOSorsurplus.GARCHmay

minimizeforecasterrorsandfiscallossesinsomeofthefollowingdomains:

1.Costofpersonnel,supplies,support

2.Planning,programmingandbudgeting

3.Defenseprogramandfiscalguidancedevelopment

4.Forceplanningandfinancialprogramdevelopment



Inonepreliminarystudy1thesupplychainforsparepartsofamilitarybasewasexaminedforinefficiencies.

Thedatafora9monthperiodwascollectedforasparepartforamilitaryvehicle(HumV).BecausetheUS

DepartmentofDefenseaffixesRFIDtagsonsparepartsfromsomeofitssuppliers,thehourlyautoiddemand

datawasavailableforanalysis.ThehistoricaldatawasusedtodevelopCLRMandGARCH(1,1)model.Inthis

case,thelinearregressionmodelwasfoundtohaveMeanSquareError(MSE)of0.20(20%).Bycomparison,

theGARCH(1,1)modelproducedaMSEof0.06(6.7%).TheseresultsareencouragingandtheUSDoDcase

lendscredibilityforfurtherexplorationofGARCHinforecastinganalytics.Althoughthispreliminaryfindingis

promising,itneedstoberepeatedwithdifferentcasedataandsubjectedtorigorousmathematicalanalysis.

4.BeerGameandRoleofAdvancedForecastingMethods

Forrester(1958) introducedaclassical4stagebeergamesimulationandrevealedthatdemand information

amplifieswithinthesupplychainaswemovefurtherupstream.Figure3shows,customerdemandisflatat8

unitspertimeperiod(it increasedfrom4to8duringtimeperiodof100),butoverunderreactionappears

whensupplychainismovedfurtherwithrespecttotime.Productionordersspiketoover40unitsperperiod,

whilewaiting collapses to0unitsonly15 timeunits later.Thisoccursmostlydue to timedelayed supply

processineachstage,whichisfollowingmaketostock(MTS)inventoryprinciple(eachphasehastargetfor

enditeminventorylevels,whichtheytrytoreachwithorderalgorithms).

1StudysupportedbytheInstituteforDefenseAnalysis,WashingtonDCandalsomentionedinDattaetal2007



chain60

54

48

42

36

30

24

18

12

6

0100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 190 195 200

Time (Month)

Customer demand : original bottles per hourRetailordes : original bottles per hourDistributionorders : original bottles per hourWholesalerorders : original bottles per hourProduction orders : original bottles per hour

Figure3.Forrestereffect(delay=4,stepwisedemandchangefrom4to8unitsduringtimeunit100).

Itmaybeobserved fromprevious research thatconventional forecastingmethodsdonot reducenegative

impactfromdemandamplification.AsshowninFigure4,classicalforecastingtechniquessuchasexponential

smoothedmoving average (EWMA)onlyheightensdemand amplification (highest value reaches above50

unitsper timeperiod)due to the assumption that allprevious values shouldbeused topredictdemand.

Although,useofEWMAmaybe justifiedundercertaincircumstances,thenondiscriminatoryormandatory

useofpastdatatopredictfuturedemandmayoftengenerateundesirableoverreaction.

Figure5reveals implementationproblems forsharingofdemand information.Oftendifferentsupplychain

phasesusedifferentcompetingsupplierstogaincostefficiencyandtruedemand isoftenconfidential.Lam

andPostle(2006)describe60%ofrespondentsinasurvey(inChina)indicatingthattheircustomersarenot

willing toexchange information.Sharingdataabouthighdemandperiodscould result in inflatedpurchase

priceifsuppliersdecidetoformcartels.However,ithasbeenshowninasignalinggametheoreticapproach

thatsharingofinformationincreasestotalsupplychainprofit(Datta2004).



chain60

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48

42

36

30

24

18

12

6

0100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 190 195 200

Time (Month)

Customer demand : ewma bottles per hourRetailordes : ewma bottles per hourDistributionorders : ewma bottles per hourWholesalerorders : ewma bottles per hourProduction orders : ewma bottles per hour

Figure4. Forrestereffect ina supplychainas it tries touseEWMAat local level (0.5weight)within

originalsetting(delay=4,stepwisedemandchangefrom4to8unitsduringtimeunit100).2

chain40

36

32

28

24

20

16

12

8

4

0100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 190 195 200

Time (Month)

Customer demand : transparency bottles per hourRetailordes : transparency bottles per hourDistributionorders : transparency bottles per hourWholesalerorders : transparency bottles per hourProduction orders : transparency bottles per hour

Figure5. Forrestereffect in fourstagesupplychain,wherewehave transparency for thenextstage

(delay=4,stepwisedemandchangefrom4to8unitsduringtimeunit100).



Inpreviousandcurrentwork,wesuggest that improving forecastingaccuracycouldbeprofitablebyusing

advancedforecastingmethods,suchasautoregressivemovingaverage(ARMA)models.Figure6showsthat

demandforecastinginamplifiedenvironmentmaybecompletedbyassigningpositivevalueforlastobserved

demandandnegativecoefficientforolderobservations(forsimplicitywehaveusedlagofoneandtwo).

Figure5. Partial autocorrelation of four stage supply chain data from production phase (2401

observationsfrom300timeunits).

2JuhaSaranen,LappeenrantaUniversityofTechnology,Finland.



Table2. ARMAmodelsbuiltwith0.125 intervaldata fromoriginalbeergamesetting forproduction

phase(numberofobservationsinintegertimeunitsgiveninparenthesis).

Coefficient Goodnessoffit

Numberof

observations

t1 t2 R2 R2whole

sample

100(12.5) 1.936 0.938 99.8% 99.97%

200(25) 1.937 0.939 99.9% 99.97%

300(37.5) 1.937 0.939 99.9% 99.97%

2401(300.1) 1.918 0.919 100.0%

Table2showscoefficientoftwolaggingparametersarewithintheneighborhoodofARMAmodelsbuiltwith

largeramountofdata.However,thedifferencesamongdifferentmodelsareratherminimal.

P r o d o r d e r s T O T A L4 0

3 6

3 2

2 8

2 4

2 0

1 6

1 2

8

4

01 0 0 1 0 5 1 1 0 1 1 5 1 2 0 1 2 5 1 3 0 1 3 5 1 4 0 1 4 5 1 5 0 1 5 5 1 6 0 1 6 5 1 7 0 1 7 5 1 8 0 1 8 5 1 9 0 1 9 5 2 0 0

T im e (M o n th )

P ro d o rd e rs T O T A L : o r ig in a l

Figure7. ProductionordersasARMAmodelofwholesalerdemandisusedinproductionorderswith

modificationsonoperatingstructure.



ApplyingadvancedforecastingmodelstotametheBullwhipeffect ischallengingbecause itcallsforprocess

transformation(Zhaoetal2002,Bayraktaretal2008).InFigure7themanufacturingunitreservesforecasted

amount of inventory one period beforehand (in order to distribute knowledge from future demand into

operativedecisions).However,thisisnotenough.Wehaveusedanothermanufacturingunit,whichservesas

anemergencyinventory,dedicatedforsuddenupswingsofdemand.Thisemergencyinventoryisservedwith

localshortresponsemanufacturing,whichcontinuouslyreplenishesemergencyinventorywithlowlotsize(in

thiscase lotsize is7units, leadtimetoemergency inventory is1timeunit, insteadof4).Duringsimulation

trialsweexploredhowARMAmodelmaybebuiltwithindynamicenvironmentswithrespecttotime.

5.TemporaryConclusion

Inthiswork,weexploredthepossibilityofusingadvancedforecastingmethodsinacontextofsupplychains

andobservedthatbusinessprocesstransformationmaybenecessarytoobtainbetterresults.Theproposed

forecastingmodels,byitsveryconstruction,requireshighvolumedata.Availabilityofhighvolumedatamay

notbethe limitingfactor inviewoftherenewed interest inautomatic identificationtechnologies(AIT)that

mayfacilitateacquisitionofrealtimedatafromproductsorobjectswithRFIDtags.Althoughspeculative, it

stands to reason thatuseofadvanced forecastingmethodsmayenhanceprofitability from IT investments

requiredtoacquirerealtimedata.However,understandingthemeaningoftheinformationfromdataisan

areastillsteepedinquagmirebutmaysoonbegintoexperiencesomeclarityiftheoperationalprocessestake

advantageofthe increasingdiffusionofthesemanticwebandorganicgrowthofontologicalframeworksto

supportambientintelligenceindecisionsystemscoupledtointelligentagentnetworks(Datta2006).Tomove

ahead,weproposetobolstertheGARCHproofofconceptmodelthroughpilotimplementationsofanalytical

enginesusingadvancedforecastingmodelstobeintegratedwithrealworldbusinessprocessesandsystems.



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Forecasting and Risk Analysis in Supply Chain Management