公司供应链管理流程参考模型模板

公司供应链管理流程参考模型资料内容仅供参考，如有不当或者侵权，请联系本人改正或者删除。SECTION1SCMTEMPLATEWORKFLOW

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withanyproductorvendormentionedinthispublicationunlessotherwisenoted.Thefollowingtrademarksandservicemarksarethepropertyofi2Technologies,Inc.:EDGEOFINSTABILITY;i2TECHNOLOGIES;ORBNETWORK;PLANET;andRESULTSDRIVENMETHODOLOGY.Thefollowingregisteredtrademarksarethepropertyofi2Technologies,Inc.:GLOBALSUPPLYCHAINMANAGEMENT;i2;i2TECHNOLOGIESanddesign;TRADEMATRIX;TRADEMATRIXanddesign;andRhythmLink.February,DocumentID:HiTech4.2SCMTemplateWorkflowDocumentVersion:V1.0DocumentTitle:HiTech4.2SCMTemplateWorkflowDocumentRevision:Draft1RevisionDate:3February,DocumentReference:.PrimaryAuthor(s):SCMTeam–KrishnanSubramanian,JatinBindal,AbhaySinghalComments:ContentsTOC\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 SCMProcessesOverviewThefollowingfigurebrieflydescribesthesolutionarchitectureforthecoreprocessesthatconstitutetheSCMsolution.SCMProcessesTheSCMtemplateasawholeperformsthefollowingfunctions: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.DemandPlanningTheobjectiveoftheDemandPlanningprocessistodevelopanaccurate,reliableviewofmarketdemand,whichiscalledthedemandplan.TheDemandPlanningprocessunderstandshowproductsareorganizedandhowtheyaresold.Thesestructuresarethefoundationoftheprocessanddeterminehowforecastaggregationanddisaggregationisconducted.Abaselinestatisticalforecastisgeneratedasastartingpoint.Itisimprovedwithinformationdirectlyfromlargecustomersandchannelpartnersthroughcollaboration.Theforecastisrefinedwiththeplannedeventschedule,sothedemandplanissynchronizedwithinternalandexternalactivities.Eachproductisevaluatedbasedonitslifecycle,andcontinuallymonitoredtodetectdeviation.Newproductintroductionsarecoordinatedwitholderproducts,pipelineinventories,andcomponentsupplytomaximizetheireffectiveness.Attachratesareusedtodeterminecomponentforecastsgiventheproliferationofproducts.Theresultisademandplanthatsignificantlyreducesforecasterrorandcalculatesdemandvariability,bothofwhichareusedtodeterminethesizeoftheresponsebuffers.Thespecificresponsebuffersandtheirplacementaredifferentbasedonthemanufacturingmodelemployed,thereforetheDemandPlanningprocessmustrepresentthosedifferences.OrderPlanningDemandPlanningOrderPlanningDemandPlanningOrderPromisingAllocationPlanningDemandForecastingTopdownforecastingBottomupforecastingLifecycleplanningOptionforecastConsensusforecastingForecastextractionDemandCollaborationDemandPlanningCustomersOrderCreation&CaptureForecastNettingMasterPlanningDemandForecastingTop-DownForecastingDefinitionTopdownforecastingistheprocessoftakinganaggregateenterpriserevenuetargetandconvertingthisrevenuetargetintoarevenueforecastbysalesunit/productline.Thisallocationprocessofrevenuetargetscanbedoneusinghistoricalperformancemeasuresorusingrulebasedallocationtechniques.TherevenuetargetscanfurtherbebrokendownintounitvolumeforecastsbyusingAverageSellingPriceinformationforproductlines.Historicalinformationistypicallymoreaccurateataggregatelevelsofcustomer/producthierarchies.Therefore,statisticalforecastingtechniquesaretypicallyappliedattheseaggregatelevels.Atlevelswherehistoricalinformationmightnotbeveryrelevantorisnotperceivedtobeaccurate,thisallocationcanbedonewitharule-basedapproach.Frequency:Thisprocessistypicallyperformedatamonthly/quarterlyfrequency,withtheforecastbeinggeneratedforthenextseveralmonths/quarters. ScenarioDescriptionBaseduponhistoricalbookingsatanaggregatelevelacrosstheentirecompany(forallproductsandgeography’s),thesystemwillautomaticallygeneratemultipleforecastsusingdifferentstatisticaltechniques.Thestatisticaltechniqueswillaccountforsuchthingsasseasonality,trends,andquarterlyspikes.Eachstatisticalforecastwillbecomparedwithactualstocalculateastandarderror.Thiswillautomaticallyoccurateverybranch(intersection)intheproductandgeographichierarchies.Theaggregatestatisticalforecastgeneratedfortheentirecompanywillbeautomaticallydisaggregatedateveryintersectionusingthestatisticaltechniquewiththesmalleststandarderror.Theoutcomeofthisprocesswillbea”Pickbest”statisticallygeneratedforecastateverylevelintheproductandgeographyhierarchies.Thisforecastisthenusedasabaselineorstartingpoint.Inputs HistoricalBookingsbyunitsHistoricalStatisticallybasedBookingsForecastOutputsMultipleStatisticalforecastsStatistical”Pickbest”forecastForecastcommittedtotop-downforecastdatabaserow.BenefitsEasydisaggregationofdatameansfaster,moreaccurateforecastingSimplealignmentofrevenuetargetsUsestopdownstatisticaladvantagestoeasilytielowerlevelforecaststorevenuetargetsi2ProductsUsedTRADEMATRIXDemandPlanner

Bottom-UpForecastingDefinitionThisprocessenablesthedifferentsalesorganizations/salesreps/operationsplannerstoenterthebestestimateoftheforecastfordifferentproducts.Thisprocessconsolidatestheknowledgeofsalesrepresentatives,localmarkets,andoperationalconstraintsintotheforecastingprocess.Thisforecastcanbeaggregatedfrombottomupandcomparedtothetargetsestablishedbythetop-downforecastingprocessattheenterpriselevel.Thiswillenableeasycomparisonbetweensalesforecastsandfinancialtargets.Frequency:Thisisaweeklyprocess.However,thereiscontinuousrefinementoftheforecastatanintervaldeterminedbytheforecastingcycletimeand/ornatureofthechangerequired.ScenarioDescriptionInparallelwiththetop-downforecast,thesalesforce/operationalplannerswillenterforecastsforindependentdemandforaparticularSKUorproductseriesbycustomerorregionasispertinenttoaparticularProduct/Geographycombination.Thisdatawillautomaticallybeaggregatedandcomparedtothetargetsestablishedbythetop-downforecastingprocess.UsingtheAverageSellingPriceforaunit,theunitbasedforecastscanbeconvertedtorevenuedollarsandautomaticallyaggregated.Thebottom-upforecastcanalsobegeneratedusingcollaborativedemandplanningwithacustomer.Inthiscase,theconsensusforecastforaproduct/productseriesforacustomerisaggregatedandcomparedtothetop-downtarget.InputSalesforceinputOperationsPlanningInputAverageSellingPrice(ASP)Customerforecast(fromtheDemandCollaborationprocess)OutputsAggregatedSalesforecastbyunitAggregatedSalesForecastbyDollarsAggregatedOperationsPlanbyunitBenefitsAutomaticaggregationofdatameansfaster,moreaccurateforecastingSimplealignmentoflowerlevelSalesplanstohigherlevelrevenuetargetsi2ProductsUsedTRADEMATRIXDemandPlanner,TRADEMATRIXCollaborationPlanner

LifeCyclePlanning–NewProductIntroductionsandPhase-In/Phase-OutDefinitionForecastingproducttransitionsplaysacriticalroleinthesuccessfulphasingoutandlaunchofnewproducts.NewProductIntroduction(NPI)andphaseIn/phaseoutforecastingallowstheenterprisetoforecastrampdownsandrampupsmoreaccurately.Rampingcanbedefinedintermsofeitherapercentageorasunits.Typicallynewproductsaredifficulttoforecastbecausenohistoricalinformationforthatproductexists.NPIplanningmustallowfornewproducttoinherithistoricalinformationfromotherproductwhenitisexpectedthatanewproductwillbehaveliketheolderproduct.Insituationswhereanewproductwillnotbehavelikeanyotherolderproduct,NPIplanningallowsausertopredictalifecyclecurveforaproduct,andthenoverlaylifetimevolumeforecastsacrossthatcurve.ScenarioDescriptionGivenaforecastfortwocomplimentaryproducts,theusercanchangetherampingpercentageofbothtoreflecttherampingupofoneproductandtherampingdownofanother.GivenaNewProductIntroductionthatispredictedtobehavelikeanolderproduct,theusercanutilizehistoricaldatafromtheolderproducttobeusedinpredictingtheforecastforthenewproduct.ThescenariosforthisprocessareexecutedinTradeMatrixDemandPlanner.FuturereleasesofthetemplatewilluseTradeMatrixTransitionalPlannertodoproductlifecycleplanning.InputsHistoricalbookingsNewproductandassociationwiththeolderpartProductrampinginformationforanewproductOutputsAdjustedForecastrampingbrokenoutby%NewproductforecastbasedonasimilarproductshistoryNewproductforecastbasedonlifecycleinputBenefitsTheabilitytoforecastanewproductusinghistoryfromananotherproductTheabilitytoforecastusingproductlifecyclecurvesCleanerproducttransitionsallowingfordecreasedinventoryobsolescencei2ProductsUsedTRADEMATRIXDemandPlanner,TRADEMATRIXTransitionPlanner

EventPlanningDefinitionThisprocessdeterminestheeffectoffutureplannedeventsontheforecast.Themarketingforecastisadjustedbasedoneventsrelatedfactors.Apromotionalcampaignorpricechangebythecompanyorthecompetitionisanexampleofaneventrelatedfactorthatmayinfluencedemand.Themarketingforecastisadjustedupordownbyacertainfactor.Thefactorcanbeincreasedordecreasedacrossperiodstosimulatearamp-uporaramp-downinsalesdependinguponthenatureoftheevent.Frequency:EventBasedScenarioDescriptionAneventrowwillmodeltheinfluenceoftheeventthatwillchangethemarketingforecast.Apromotionalcampaignorpricechangebythecompanyorthecompetitionisanexampleofafactorthatmayinfluencedemand.TheuserwillpopulatetheEventrowwithscalarvalueswhichwhenmultipliedbytheMarketingstatisticalforecastwilladjusttheMarketingforecastupordownbyafactor(0.90fora10%declineor1.05fora5%increaseetc.).Eventrowcanbeincreasedordecreasedacrossperiodstosimulatearamp-uporaramp-downinsalesdependinguponthenatureoftheevent.InputsEvent–constantfactortypicallyHistoricalBookingsMarketingforecastOutputsAdjustedMarketingForecastBenefitsTheabilitytoalloweventstodynamicallyinfluenceforecastI2ProductsUsedTRADEMATRIXDemandPlanner

ConsensusForecastDefinitionTheconsensusprocessisoneinwhichthemultipleforecastingprocessesthusfarusedarebroughttogethertoarriveatonesingleforecast.Allinformationcriticaltoreachingconsensusontheforecastwillbebroughttogetherforanalysisandfacilitationoftheconsensusprocess.Thelevelatwhichtheconsensusprocessisperformedistypicallyatanintermediatelevel,wheretheforecastismostmeaningfulforthedifferentstakeholderorganizations.Thus,top-downforecast,bottom-upforecast,marketingforecastandcollaborativeforecastwillbeusedtoarriveataconsensusforecast.ScenarioDescriptionThedifferentforecastsincludingthetop-down,bottom-up,marketing,operationsandsalesarecomparedandcontrastedbythevariousforecastownersandbasedonconsiderationssuchasrevenuetargets,life-cycleconsiderationsandcapacityaconsensusforecastisdetermined.Thisisthefinalforecastthatisusedbythesupplyplanningprocess.InputsTopdownforecasts,bottomupforecasts,etc.ataspecificnode(intersectionofproductandgeography)inthehierarchy.OutputsConsensusforecastBenefitsCommunicationbetweendifferentorganizationsisachievedMultipledatapointscanbedisplayed,allowingforanalysis,comparisonsandmetricsEmphasizesdataanalysisandreduceddatagatheringI2ProductsUsedTRADEMATRIXDemandPlanner

Attach-RateForecasting/DependentDemandForecastinginConfigure-to-OrderenvironmentsDefinitionInaConfigureToOrder(CTO)manufacturingenvironment,aparticularproductmodelcanbesoldwithseveraloptions.Thecustomerchoosestheexactconfigurationatthetimeofplacinganorder.However,forthepurposeofprocuringtheseparts,theenterprisewillneedtoforecastthemixofoptionsthatwillpotentiallybesold.Theforecastpercentagemixofoptionsiscalled”attachrates”.Theconsensusprocessessentiallydeterminestheforecastattheproductmodellevel.Thisprocessperformstheoptionmixanalysistoforecastattachrates.The‘attachrates’canbevaryingbytimeand/orgeography.ProductorProduct-serieslevelforecastswillbebrokendownintothecomponentsoroptionsthatcomprisethembyusingattachrates.Attachratescanbemanuallyinputorforecastedbaseduponhistory.ScenarioDescriptionInputsModeltooptionsmappingRelationshiptodeterminedependentforecastOutputsAttachRatesDependentForecastBenefitsEasywaytodeterminedependentforecastsinaCTOenvironmentAttachRatescanbeforecastacrosstimeandgeographyI2ProductsUsedTRADEMATRIXDemandPlanner,RHYTHMPRO

DemandCollaborationDefinitionInsituationswherethecustomersoftheenterprisehavetheirownforecastingprocesses,demandcollaborationwillenablemoreaccurateforecastingbyensuringrapidtransmissionofanydownstreamdemandpatternchangestotheenterprise.Furthermore,intheabsenceofsuchaworkflow,everynodeinthesupplychaininvariablytendstoputin”sandbagging”inventorytocompensateforthelackoffastinformationflow.ScenarioDescriptionTheInternetenablestherapidcollaborativedemandforecastingprocess.Aworkflowcanoriginateateithertheenterpriseorthecustomer,i.e.,theenterprisecouldinitiateabaselineforecasttosubmittothecustomersforfeedback,orabaselineforecastcouldbeinitiatedbyacustomerandsubmittedtotheenterpriseforreviewandcollaboration.Theworkflowusedcandifferdependingoneitherthecustomerorproduct.ThecollaborativecommunicationwillbeovertheWorldWideWeb.Customerswillonlybeabletosee”their”forecasts,notthoseofothercustomers.Inadditiontoforecast,informationregardingsellthroughrates,inventorylevelsetc.canalsobecommunicatedbetweenenterpriseandcustomers.InputsEnterpriseinitiatedbaselineforecastorcustomerinitiatedbaselineforecastRevisionstotheforecastbycustomerandenterpriseOutputsAconsensusforecastagreeduponbetweencustomerandenterprisefordifferentproductlines.BenefitsCollaborativeforecastingovertheInternetreducescycletimebetweenforecastinformationpropagation.Henceenterprisegetsmorerealtimeupdatesofchangesindownstreamdemandpatterns.Collaborativeforecastingprocesseswillenableimprovinghonestinformationexchangebetweenenterpriseandcustomerstherebyreducingthe”sandbagging”inventoryinthesupplychain.I2ProductsUsedTRADEMATRIXCollaborationPlanner

FlexLimitPlanningDefinitionContractsbetweentheenterpriseandtheircustomersplacerestrictionsonhowmuchflexibilityisprovidedtothecustomersintermsofvaryingforecastnumbersfromonetimeperiodtoanother.Basedonthecollaborationprocesswithchannelpartners/customers,flexlimitsontheforecastvaluesareestablished.Theseflexlimitswillthendrivetheamountofinventorythattheenterpriseneedstopositiontocoverfortheanticipatedvariationindemand.ScenarioDescriptionThisprocessiscurrentlynotapartofthetemplate.Futurereleaseswillincorporatethisprocessasastandardworkflowinthetemplate.InputsOutputsBenefitsI2ProductsUsedTRADEMATRIXCollaborationPlanner

ForecastNettingForecastnettingasaprocesscanbedoneoutsideofDemandPlanningorwithindemandplanning.Thedecisionastowheretoperformthisprocesswouldvarybyindustry.Thetemplatesupportsbothtypesofworkflow.DefinitionTheconsensusforecastisusedasinputforsupplyplanningfortheenterprise.Ascustomerorders/confirmedorders(orderbacklog)arerealizedinashortterm(fewweekstofewmonths),theordersarenettedagainsttheforecastforthesupplyplanningpurpose.Thesupplyplanningprocess,thus,plansforthenettedforecastandtheorderbacklog.Itisimportanttodistinguishbetweenforecastandordersinsupplyplanningbecauseordersarefirmdemandthattheenterprisehascommittedtothecustomers.Therefore,ittranslatesdirectlyintorevenuefortheenterprise.Byprovidingtheordersandnettedforecastasinputstothesupplyplanningprocess,wecanallocateconstrainedmaterialandsupplyfirsttotheactualordersandthentotheforecast,therebyensuringthattheordersareplannedfirst.Nettingisimportantbecauseduringsupplyplanning,ordersmusthaveahigherprioritythanforecastsastheordersMUSTbeplannedforandfulfilled.Further,wewouldwantthatthecapacityandthematerialsconsumedbyordersshouldnotbeavailablewhileplanningfortheremainingforecasts.Sofirstweplanfororders(whichconsumesomematerialandcapacity)andnextweplanforthenettedforecasts(whichistheremainingforecastfortheperiod).ScenarioDescriptionForecastNettingforBTSandBTOproductsForecastnettingforaBTSproductissimpledoneatasellerproductlevel.Consideraparticularseller-productcombination.Weknowtheforecastforthebucket.Fromtheactualorders,wecandeterminetheactualordersfortheseller-productcombinationthatfallineachbucket.Theseorderscanthenbenettedagainsttheforecastusingpre-specifiedbusinessrules.ForecastNettingforCTOproductsForecastforCTOproductsisdoneatamodellevel.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.InputsConsensusForecastOrderbacklogOutputsNettedforecast(BTS/BTO)AdjustedForecastforCTOBenefitsSupplyreservationforactualorderscantakeplaceduringsupplyplanning.Thisisessentialasactualorders(whichhavealreadybeenpromised)MUSTbemet.Explainusingthedefinitionparagraphi2ProductsUsedTRADEMATRIXDemandPlanner,TradeMatrixDemandFulfillment.

ForecastExtractionOnceforecastnettinghasbeendone,weneedtoextracttheforecastsoitcanbesenttoMasterPlanningforsupplyplanningandtoAllocationPlanningtoaidinallocations.MasterPlanningrequiresnettedoradjustedforecastwhileAllocationPlanningrequiresgrossforecast.ForecastExtractionforSupplyPlanningDefinitionThisistheprocessofextractingforecastattheappropriateProduct/Geographyintersectionsandcommunicatingittothesupplyplanningprocesssothatasupplyplancanbedetermined.ScenariosDependingonwhetherthemanufacturingenvironmentisBuild-to-Stock(BTS),Build-to-Order(BTO)orConfigure-to-Order(CTO),theforecastextractionforsupplyplanningisdoneatdifferentlevelsintheproducthierarchy.Atthispoint,wemustrecallthatthererearetwodimensionstoaforecastinagivenbucket—sellerandproduct.Wejustmentionedthatthelevelintheproducthierarchyatwhichtheforecasthastobeextractedwillvarydependingonproducttype.Potentially,thesellerhierarchylevelatwhichforecastisextractedcanalsovarydependingonproducttypethoughwedonotdemonstratethisinthetemplatedataset.Formoredetails,pleaseseethescenariosbelow.BTSproductsInaBTSenvironment,theforecastisextractedforthesupplyplanningprocesstypicallyatamodellevel(intheproducthierarchy).Also,dependingonthelevelinthesellerhierarchyatwhichtheforecastistobeplanned,theforecastisextractedandnettingisperformedattheappropriatelevelinthesellerhierarchy.PleasenotethatsinceallocationplanningforBTSisdoneentirelyintheallocationplanningengine,sothereisnoadditionalinformationthatwillbegeneratedbyextractingtheforecastforBTSatcustomer-dclevel.AllweneedtoknowistheaggregatedforecastforaBTSproductintheregionswhichareservedfromonesupplypoint.BTOproductsInaBTOenvironment,forecastistypicallyextractedattheProductFamilyoratthemodellevel(forassemblycoordination).TheSupplyplanningprocessusesabillofmaterialtodeterminethecomponentrequirementsbasedontheseforecaststogenerateaSupplyPlan.DependingonthelevelintheSellerhierarchyatwhichthissupplyistobeplanned,forecastisextractedattheappropriatelevelinthesellerhierarchy.3.CTOproductsInaCTOenvironment,theforecastcanbeextractedintwoways.TheyareComponentlevel:Forecastisextractedatthecomponentlevelusingtheattachratesandbybreakingdownthemodellevelnettedforecastintocomponentforecasts.Modellevel:Theadjustedforecastisextractedatamodellevelandissenttosupplyplanningalongwiththeattachrates.IntheS