公司供应链管理流程模型(英文版)

SECTION1

SCMTEMPLATEWORKFLOW

SCMTemplateWorkflow

Copyright2023i2Technologies,Inc.

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February,2023

DocumentID:

HiTech4.2SCMTemplateWorkflow

DocumentVersion:

V1.0

DocumentTitle:

HiTech4.2SCMTemplateWorkflow

DocumentRevision:

Draft1

RevisionDate:

3February,2023

DocumentReference:

.

PrimaryAuthor(s):

SCMTeam–KrishnanSubramanian,JatinBindal,AbhaySinghal

Comments:

Contents

TOC\o"1-3"

SCMProcessesOverview

SCMProcesses

DemandPlanning

DemandForecasting

Top-DownForecasting

Bottom-UpForecasting

LifeCyclePlanning–NewProductIntroductionsandPhase-In/Phase-Out

EventPlanning

ConsensusForecast

Attach-RateForecasting/DependentDemandForecastinginConfigure-to-Orderenvironments

DemandCollaboration

FlexLimitPlanning

ForecastNetting

ForecastExtraction

MasterPlanning

SupplyPlanning

EnterprisePlanning:InventoryPlanning

Enterpriseplanning:Longtermcapacityplanning

Enterpriseplanning:Longtermmaterialplanning

FacilityPlanning:Supplyplanforenterprisemanagedcomponents

CollaborationPlanningforEnterpriseandFactoryManagedComponents–ProcurementCollaboration

CollaborationPlanningwithTransportationProviders-TransportationCollaboration

AllocationPlanning

DemandFulfillment

OrderPromising

Promisingneworders

ConfiguretoOrder(CTO)Orders

BuildtoOrder(BTO)Orders

OrderPlanning

FactoryPlanning

TransportationPlanning

SCMProcessesOverview

ThefollowingfigurebrieflydescribesthesolutionarchitectureforthecoreprocessesthatconstitutetheSCMsolution.

SCMProcesses

TheSCMtemplateasawholeperformsthefollowingfunctions:

DemandPlanning:Forecastinganddemandcollaboration.Salesforecastsaregeneratedusingvariousstatisticalmodelsandcustomercollaboration.

MasterPlanning:Longtermandmediumtermmasterplanningformaterialaswellascapacity.Masterplanningcanbedoneatboththeenterpriselevel(forcriticalsharedcomponents)andthefactorylevel.Inaddition,decisionsrelatingtomaterialprocurementandcapacityoutsourcingofmaterialsfromsuppliers((orcapacityoutsourcingdecisions)canbemade.

AllocationPlanning:Reservingproductsupplyforchannelpartnersorcustomersbasedonpre-specifiedrules.Also,managingthesupplysothatordersthathavealreadybeenpromisedcanbefulfilledinthebestpossiblemanner(onthepromiseddatesandinthepromisedquantities).

OrderPromising:Promisingadateandquantitytocustomerorders.Thesepromisesaremadelookingattheprojectedsupply.Inaddition,sourcingdecisionsarealsomadehereafterconsideringsuchvariablesaslead-time,productcost,shippingcost,etc.

OrderPlanning:Detailedorderplanningencompassingmultiplefactories.Inadditiondetailedtransportationplanningisalsodonewhichcanhandlesuchcomplexrequirementsasmergingtwoshipmentsfromdifferentlocationsduringtransit.

Informationflowsseamlesslybetweenallthesefunctions.Theinputstothesystemarethestaticdata(supplychainstructure,supplierrelationships,sellerandproducthierarchies,supplierrelationships,etc),someforecastdataandactualorders.Theoutputisacomprehensiveandintelligentsupplychainplanwhichtakesallthesupplychaindeliveryprocessesintoconsiderationinordertomaximizecustomersatisfaction,atthesametimereducingorderfulfillmentleadtimesandcosts.

Thescopeofthisdocumentistodescribethescenariosmodeledasapartofthecurrentreleaseofthetemplate(Hitech2).Foranyplanningsystem,theplacetobeginplanningisdemandforecasting.Welookatthisinmoredetailinthenextsection.

DemandPlanning

TheobjectiveoftheDemandPlanningprocessistodevelopanaccurate,reliableviewofmarketdemand,whichiscalledthedemandplan.TheDemandPlanningprocessunderstandshowproductsareorganizedandhowtheyaresold.Thesestructuresarethefoundationoftheprocessanddeterminehowforecastaggregationanddisaggregationisconducted.Abaselinestatisticalforecastisgeneratedasastartingpoint.Itisimprovedwithinformationdirectlyfromlargecustomersandchannelpartnersthroughcollaboration.Theforecastisrefinedwiththeplannedeventschedule,sothedemandplanissynchronizedwithinternalandexternalactivities.Eachproductisevaluatedbasedonitslifecycle,andcontinuallymonitoredtodetectdeviation.Newproductintroductionsarecoordinatedwitholderproducts,pipelineinventories,andcomponentsupplytomaximizetheireffectiveness.Attachratesareusedtodeterminecomponentforecastsgiventheproliferationofproducts.Theresultisademandplanthatsignificantlyreducesforecasterrorandcalculatesdemandvariability,bothofwhichareusedtodeterminethesizeoftheresponsebuffers.Thespecificresponsebuffersandtheirplacementaredifferentbasedonthemanufacturingmodelemployed,thereforetheDemandPlanningprocessmustrepresentthosedifferences.

OrderPlanning

DemandPlanning

OrderPromising

Allocation

Planning

DemandForecasting

Topdownforecasting

Bottomupforecasting

Lifecycleplanning

Optionforecast

Consensusforecasting

Forecastextraction

Demand

Collaboration

Demand

Planning

Customers

OrderCreation

&Capture

Forecast

Netting

MasterPlanning

Thefollowingfigureidentifiesthekeyprocessesthatconstitutedemandplanningandthescenariosthataremodeledinthetemplate.

DemandForecasting

Top-DownForecasting

Definition

Topdownforecastingistheprocessoftakinganaggregateenterpriserevenuetargetandconvertingthisrevenuetargetintoarevenueforecastbysalesunit/productline.Thisallocationprocessofrevenuetargetscanbedoneusinghistoricalperformancemeasuresorusingrulebasedallocationtechniques.TherevenuetargetscanfurtherbebrokendownintounitvolumeforecastsbyusingAverageSellingPriceinformationforproductlines.

Historicalinformationistypicallymoreaccurateataggregatelevelsofcustomer/producthierarchies.Therefore,statisticalforecastingtechniquesaretypicallyappliedattheseaggregatelevels.Atlevelswherehistoricalinformationmightnotbeveryrelevantorisnotperceivedtobeaccurate,thisallocationcanbedonewitharule-basedapproach.

Frequency:Thisprocessistypicallyperformedatamonthly/quarterlyfrequency,withtheforecastbeinggeneratedforthenextseveralmonths/quarters.

ScenarioDescription

Baseduponhistoricalbookingsatanaggregatelevelacrosstheentirecompany(forallproductsandgeography’s),thesystemwillautomaticallygeneratemultipleforecastsusingdifferentstatisticaltechniques.Thestatisticaltechniqueswillaccountforsuchthingsasseasonality,trends,andquarterlyspikes.Eachstatisticalforecastwillbecomparedwithactualstocalculateastandarderror.Thiswillautomaticallyoccurateverybranch(intersection)intheproductandgeographichierarchies.Theaggregatestatisticalforecastgeneratedfortheentirecompanywillbeautomaticallydisaggregatedateveryintersectionusingthestatisticaltechniquewiththesmalleststandarderror.Theoutcomeofthisprocesswillbea“Pickbest”statisticallygeneratedforecastateverylevelintheproductandgeographyhierarchies.Thisforecastisthenusedasabaselineorstartingpoint.

Inputs

HistoricalBookingsbyunits

HistoricalStatisticallybasedBookingsForecast

Outputs

MultipleStatisticalforecasts

Statistical“Pickbest”forecast

Forecastcommittedtotop-downforecastdatabaserow.

Benefits

Easydisaggregationofdatameansfaster,moreaccurateforecasting

Simplealignmentofrevenuetargets

Usestopdownstatisticaladvantagestoeasilytielowerlevelforecaststorevenuetargets

i2ProductsUsed

TRADEMATRIXDemandPlanner

Bottom-UpForecasting

Definition

Thisprocessenablesthedifferentsalesorganizations/salesreps/operationsplannerstoenterthebestestimateoftheforecastfordifferentproducts.Thisprocessconsolidatestheknowledgeofsalesrepresentatives,localmarkets,andoperationalconstraintsintotheforecastingprocess.Thisforecastcanbeaggregatedfrombottomupandcomparedtothetargetsestablishedbythetop-downforecastingprocessattheenterpriselevel.Thiswillenableeasycomparisonbetweensalesforecastsandfinancialtargets.

Frequency:Thisisaweeklyprocess.However,thereiscontinuousrefinementoftheforecastatanintervaldeterminedbytheforecastingcycletimeand/ornatureofthechangerequired.

ScenarioDescription

Inparallelwiththetop-downforecast,thesalesforce/operationalplannerswillenterforecastsforindependentdemandforaparticularSKUorproductseriesbycustomerorregionasispertinenttoaparticularProduct/Geographycombination.Thisdatawillautomaticallybeaggregatedandcomparedtothetargetsestablishedbythetop-downforecastingprocess.UsingtheAverageSellingPriceforaunit,theunitbasedforecastscanbeconvertedtorevenuedollarsandautomaticallyaggregated.

Thebottom-upforecastcanalsobegeneratedusingcollaborativedemandplanningwithacustomer.Inthiscase,theconsensusforecastforaproduct/productseriesforacustomerisaggregatedandcomparedtothetop-downtarget.

Input

Salesforceinput

OperationsPlanningInput

AverageSellingPrice(ASP)

Customerforecast(fromtheDemandCollaborationprocess)

Outputs

AggregatedSalesforecastbyunit

AggregatedSalesForecastbyDollars

AggregatedOperationsPlanbyunit

Benefits

Automaticaggregationofdatameansfaster,moreaccurateforecasting

SimplealignmentoflowerlevelSalesplanstohigherlevelrevenuetargets

i2ProductsUsed

TRADEMATRIXDemandPlanner,TRADEMATRIXCollaborationPlanner

LifeCyclePlanning–NewProductIntroductionsandPhase-In/Phase-Out

Definition

Forecastingproducttransitionsplaysacriticalroleinthesuccessfulphasingoutandlaunchofnewproducts.NewProductIntroduction(NPI)andphaseIn/phaseoutforecastingallowstheenterprisetoforecastrampdownsandrampupsmoreaccurately.Rampingcanbedefinedintermsofeitherapercentageorasunits.Typicallynewproductsaredifficulttoforecastbecausenohistoricalinformationforthatproductexists.NPIplanningmustallowfornewproducttoinherithistoricalinformationfromotherproductwhenitisexpectedthatanewproductwillbehaveliketheolderproduct.Insituationswhereanewproductwillnotbehavelikeanyotherolderproduct,NPIplanningallowsausertopredictalifecyclecurveforaproduct,andthenoverlaylifetimevolumeforecastsacrossthatcurve.

ScenarioDescription

Givenaforecastfortwocomplimentaryproducts,theusercanchangetherampingpercentageofbothtoreflecttherampingupofoneproductandtherampingdownofanother.GivenaNewProductIntroductionthatispredictedtobehavelikeanolderproduct,theusercanutilizehistoricaldatafromtheolderproducttobeusedinpredictingtheforecastforthenewproduct.ThescenariosforthisprocessareexecutedinTradeMatrixDemandPlanner.FuturereleasesofthetemplatewilluseTradeMatrixTransitionalPlannertodoproductlifecycleplanning.

Inputs

Historicalbookings

Newproductandassociationwiththeolderpart

Productrampinginformationforanewproduct

Outputs

AdjustedForecastrampingbrokenoutby%

Newproductforecastbasedonasimilarproductshistory

Newproductforecastbasedonlifecycleinput

Benefits

Theabilitytoforecastanewproductusinghistoryfromananotherproduct

Theabilitytoforecastusingproductlifecyclecurves

Cleanerproducttransitionsallowingfordecreasedinventoryobsolescence

i2ProductsUsed

TRADEMATRIXDemandPlanner,TRADEMATRIXTransitionPlanner

EventPlanning

Definition

Thisprocessdeterminestheeffectoffutureplannedeventsontheforecast.Themarketingforecastisadjustedbasedoneventsrelatedfactors.Apromotionalcampaignorpricechangebythecompanyorthecompetitionisanexampleofaneventrelatedfactorthatmayinfluencedemand.Themarketingforecastisadjustedupordownbyacertainfactor.Thefactorcanbeincreasedordecreasedacrossperiodstosimulatearamp-uporaramp-downinsalesdependinguponthenatureoftheevent.

Frequency:EventBased

ScenarioDescription

Aneventrowwillmodeltheinfluenceoftheeventthatwillchangethemarketingforecast.Apromotionalcampaignorpricechangebythecompanyorthecompetitionisanexampleofafactorthatmayinfluencedemand.TheuserwillpopulatetheEventrowwithscalarvalueswhichwhenmultipliedbytheMarketingstatisticalforecastwilladjusttheMarketingforecastupordownbyafactor(0.90fora10%declineor1.05fora5%increaseetc.).Eventrowcanbeincreasedordecreasedacrossperiodstosimulatearamp-uporaramp-downinsalesdependinguponthenatureoftheevent.

Inputs

Event–constantfactortypically

HistoricalBookings

Marketingforecast

Outputs

AdjustedMarketingForecast

Benefits

Theabilitytoalloweventstodynamicallyinfluenceforecast

I2ProductsUsed

TRADEMATRIXDemandPlanner

ConsensusForecast

Definition

Theconsensusprocessisoneinwhichthemultipleforecastingprocessesthusfarusedarebroughttogethertoarriveatonesingleforecast.Allinformationcriticaltoreachingconsensusontheforecastwillbebroughttogetherforanalysisandfacilitationoftheconsensusprocess.Thelevelatwhichtheconsensusprocessisperformedistypicallyatanintermediatelevel,wheretheforecastismostmeaningfulforthedifferentstakeholderorganizations.Thus,top-downforecast,bottom-upforecast,marketingforecastandcollaborativeforecastwillbeusedtoarriveataconsensusforecast.

ScenarioDescription

Thedifferentforecastsincludingthetop-down,bottom-up,marketing,operationsandsalesarecomparedandcontrastedbythevariousforecastownersandbasedonconsiderationssuchasrevenuetargets,life-cycleconsiderationsandcapacityaconsensusforecastisdetermined.Thisisthefinalforecastthatisusedbythesupplyplanningprocess.

Inputs

Topdownforecasts,bottomupforecasts,etc.ataspecificnode(intersectionofproductandgeography)inthehierarchy.

Outputs

Consensusforecast

Benefits

Communicationbetweendifferentorganizationsisachieved

Multipledatapointscanbedisplayed,allowingforanalysis,comparisonsandmetrics

Emphasizesdataanalysisandreduceddatagathering

I2ProductsUsed

TRADEMATRIXDemandPlanner

Attach-RateForecasting/DependentDemandForecastinginConfigure-to-Orderenvironments

Definition

InaConfigureToOrder(CTO)manufacturingenvironment,aparticularproductmodelcanbesoldwithseveraloptions.Thecustomerchoosestheexactconfigurationatthetimeofplacinganorder.However,forthepurposeofprocuringtheseparts,theenterprisewillneedtoforecastthemixofoptionsthatwillpotentiallybesold.Theforecastpercentagemixofoptionsiscalled“attachrates”.Theconsensusprocessessentiallydeterminestheforecastattheproductmodellevel.Thisprocessperformstheoptionmixanalysistoforecastattachrates.The‘attachrates’canbevaryingbytimeand/orgeography.ProductorProduct-serieslevelforecastswillbebrokendownintothecomponentsoroptionsthatcomprisethembyusingattachrates.Attachratescanbemanuallyinputorforecastedbaseduponhistory.

ScenarioDescription

Inputs

Modeltooptionsmapping

Relationshiptodeterminedependentforecast

Outputs

AttachRates

DependentForecast

Benefits

EasywaytodeterminedependentforecastsinaCTOenvironment

AttachRatescanbeforecastacrosstimeandgeography

I2ProductsUsed

TRADEMATRIXDemandPlanner,RHYTHMPRO

DemandCollaboration

Definition

Insituationswherethecustomersoftheenterprisehavetheirownforecastingprocesses,demandcollaborationwillenablemoreaccurateforecastingbyensuringrapidtransmissionofanydownstreamdemandpatternchangestotheenterprise.Furthermore,intheabsenceofsuchaworkflow,everynodeinthesupplychaininvariablytendstoputin“sandbagging”inventorytocompensateforthelackoffastinformationflow.

ScenarioDescription

TheInternetenablestherapidcollaborativedemandforecastingprocess.Aworkflowcanoriginateateithertheenterpriseorthecustomer,i.e.,theenterprisecouldinitiateabaselineforecasttosubmittothecustomersforfeedback,orabaselineforecastcouldbeinitiatedbyacustomerandsubmittedtotheenterpriseforreviewandcollaboration.Theworkflowusedcandifferdependingoneitherthecustomerorproduct.ThecollaborativecommunicationwillbeovertheWorldWideWeb.Customerswillonlybeabletosee“their”forecasts,notthoseofothercustomers.Inadditiontoforecast,informationregardingsellthroughrates,inventorylevelsetc.canalsobecommunicatedbetweenenterpriseandcustomers.

Inputs

Enterpriseinitiatedbaselineforecastorcustomerinitiatedbaselineforecast

Revisionstotheforecastbycustomerandenterprise

Outputs

Aconsensusforecastagreeduponbetweencustomerandenterprisefordifferentproductlines.

Benefits

CollaborativeforecastingovertheInternetreducescycletimebetweenforecastinformationpropagation.Henceenterprisegetsmorerealtimeupdatesofchangesindownstreamdemandpatterns.

Collaborativeforecastingprocesseswillenableimprovinghonestinformationexchangebetweenenterpriseandcustomerstherebyreducingthe“sandbagging”inventoryinthesupplychain.

I2ProductsUsed

TRADEMATRIXCollaborationPlanner

FlexLimitPlanning

Definition

Contractsbetweentheenterpriseandtheircustomersplacerestrictionsonhowmuchflexibilityisprovidedtothecustomersintermsofvaryingforecastnumbersfromonetimeperiodtoanother.Basedonthecollaborationprocesswithchannelpartners/customers,flexlimitsontheforecastvaluesareestablished.Theseflexlimitswillthendrivetheamountofinventorythattheenterpriseneedstopositiontocoverfortheanticipatedvariationindemand.

ScenarioDescription

Thisprocessiscurrentlynotapartofthetemplate.Futurereleaseswillincorporatethisprocessasastandardworkflowinthetemplate.

Inputs

Outputs

Benefits

I2ProductsUsed

TRADEMATRIXCollaborationPlanner

ForecastNetting

ForecastnettingasaprocesscanbedoneoutsideofDemandPlanningorwithindemandplanning.Thedecisionastowheretoperformthisprocesswouldvarybyindustry.Thetemplatesupportsbothtypesofworkflow.

Definition

Theconsensusforecastisusedasinputforsupplyplanningfortheenterprise.Ascustomerorders/confirmedorders(orderbacklog)arerealizedinashortterm(fewweekstofewmonths),theordersarenettedagainsttheforecastforthesupplyplanningpurpose.Thesupplyplanningprocess,thus,plansforthenettedforecastandtheorderbacklog.Itisimportanttodistinguishbetweenforecastandordersinsupplyplanningbecauseordersarefirmdemandthattheenterprisehascommittedtothecustomers.Therefore,ittranslatesdirectlyintorevenuefortheenterprise.Byprovidingtheordersandnettedforecastasinputstothesupplyplanningprocess,wecanallocateconstrainedmaterialandsupplyfirsttotheactualordersandthentotheforecast,therebyensuringthattheordersareplannedfirst.Nettingisimportantbecauseduringsupplyplanning,ordersmusthaveahigherprioritythanforecastsastheordersMUSTbeplannedforandfulfilled.Further,wewouldwantthatthecapacityandthematerialsconsumedbyordersshouldnotbeavailablewhileplanningfortheremainingforecasts.Sofirstweplanfororders(whichconsumesomematerialandcapacity)andnextweplanforthenettedforecasts(whichistheremainingforecastfortheperiod).

ScenarioDescription

ForecastNettingforBTSandBTOproducts

ForecastnettingforaBTSproductissimpledoneatasellerproductlevel.Consideraparticularseller-productcombination.Weknowtheforecastforthebucket.Fromtheactualorders,wecandeterminetheactualordersfortheseller-productcombinationthatfallineachbucket.Theseorderscanthenbenettedagainsttheforecastusingpre-specifiedbusinessrules.

ForecastNettingforCTOproducts

ForecastforCTOproductsisdoneatamodellevel.However,unlikeforBTSandBTO,actualordersforCTOcomeinatcomponentlevel.Thecustomerwillspecifyasetbunchofcomponentsthathewouldwanttobeassembledintoamodel.Becauseofthisdiscrepancybetweenthelevelatwhichforecastingisdone(modellevel)andthelevelatwhichactualdemandcomesin(componentlevel),forecastnettingforCTOisnotsostraightforward.SoforCTO,wesend—notanettedforecastbut—anadjustedforecasttoMasterPlanning.Toarriveatanadjustedforecast,thegrossforecastcanbeadjustedattwolevels:a)Thetotalforecastforthebucketataseller-productcombinationnodecanbechanged,and/orb)Theforecastedattachrates(betweentheCTOmodelandthecomponents)canbechangedbylookingatthewaydemandactuallymaterialized.Forinstance,ifmostCTOorderscameinwiththerequirementfora6GBharddiskwhereasithadbeenforecastedthattheywouldusuallybefora8GBharddisk,thentheattachrateswouldnowhavetobechangedtoreflectthewayactualdemandmaterializedandthewayactualdemandisexpectedtomaterializeinfuture.

Asimplisticcase:DemandmaterializedexactlyinthesamewayashadbeenforecastedforaCTOproduct.Inthiscase,wewouldnotadjusttheCTOgrossforecastatall,andsendtheentireforecasttoMasterPlanning.

ItmaybenotedherethatMasterPlanningneverreadstheactualordersforCTOproducts(unlikeforBTOandCTO).ActualordersforCTOareonlyreadbyOrderPlanning.

Inputs

ConsensusForecast

Orderbacklog

Outputs

Nettedforecast(BTS/BTO)

AdjustedForecastforCTO

Benefits

Supplyreservationforactualorderscantakeplaceduringsupplyplanning.Thisisessentialasactualorders(whichhavealreadybeenpromised)MUSTbemet.Explainusingthedefinitionparagraph

i2ProductsUsed

TRADEMATRIXDemandPlanner,TradeMatrixDemandFulfillment.

ForecastExtraction

Onceforecastnettinghasbeendone,weneedtoextracttheforecastsoitcanbesenttoMasterPlanningforsupplyplanningandtoAllocationPlanningtoaidinallocations.MasterPlanningrequiresnettedoradjustedforecastwhileAllocationPlanningrequiresgrossforecast.

ForecastExtractionforSupplyPlanning

Definition

ThisistheprocessofextractingforecastattheappropriateProduct/Geographyintersectionsandcommunicatingittothesupplyplanningprocesssothatasupplyplancanbedetermined.

Scenarios

DependingonwhetherthemanufacturingenvironmentisBuild-to-Stock(BTS),Build-to-Order(BTO)orConfigure-to-Order(CTO),theforecastextractionforsupplyplanningisdoneatdifferentlevelsintheproducthierarchy.Atthispoint,wemustrecallthatthererearetwodimensionstoaforecastinagivenbucket—sellerandproduct.Wejustmentionedthatthelevelintheproducthierarchyatwhichtheforecasthastobeextractedwillvarydependingonproducttype.Potentially,thesellerhierarchylevelatwhichforecastisextractedcanalsovarydependingonproducttypethoughwedonotdemonstratethisinthetemplatedataset.Formoredetails,pleaseseethescenariosbelow.

BTSproducts

InaBTSenvironment,theforecastisextractedforthesupplyplanningprocesstypicallyatamodellevel(intheproducthierarchy).Also,dependingonthelevelinthesellerhierarchyatwhichtheforecastistobeplanned,theforecastisextractedandnettingisperformedattheappropriatelevelinthesellerhierarchy.PleasenotethatsinceallocationplanningforBTSisdoneentirelyintheallocationplanningengine,sothereisnoadditionalinformationthatwillbegeneratedbyextractingtheforecastforBTSatcustomer-dclevel.AllweneedtoknowistheaggregatedforecastforaBTSproductintheregionswhichareservedfromonesupplypoint.

BTOproducts

InaBTOenvironment,forecastistypicallyextractedattheProductFamilyoratthemodellevel(forassemblycoordination).TheSupplyplanningprocessusesabillofmaterialtodeterminethecomponentrequirementsbasedontheseforecaststogenerateaSupplyPlan.DependingonthelevelintheSellerhierarchyatwhichthissupplyistobeplanned,forecastisextractedattheappropriatelevelinthesellerhierarchy.

3.CTOproducts

InaCTOenvironment,theforecastcan