备件需求产生、传播及解析算法研究-豆柴文库

您所在位置：网站首页 / 备件需求产生、传播及解析算法研究.docx / 文档详情

在线预览结束，喜欢就下载吧，查找使用更方便

5 金币

下载文档

/ 2

下载提示

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

文本预览

备件需求产生、传播及解析算法研究
Introduction:
备件需求产生、传播及解析算法研究(Researchonalgorithmsforgenerating,disseminating,andanalyzingsparepartsdemand)isacriticalareaofresearchinsupplychainmanagement.Theprocessofgeneratinganddisseminatingdemandsignalsplaysapivotalroleinensuringseamlessandoptimizedsupplychainoperations.Atthesametime,theanalyticalaspectofsparepartsdemandhelpstoidentifydemandpatterns,optimizeinventorymanagement,andenhancecustomersatisfaction.Inthispaper,wewillexploretheimportanceofsparepartsmanagement,discussthealgorithmsusedtogenerateanddisseminatedemandsignals,andhighlighttheanalyticaltechniquesusedtomanagesparepartsdemandeffectively.
Background:
Sparepartsmanagementisimportantinthemanufacturingindustry,anditcontributessignificantlytotheoverallsupplychainprocess.Theprimaryobjectiveofsparepartsmanagementistoensurethattherightsparepartsareavailableattherighttimeintherightquantity.Whenamachinebreaksdown,itcancauseastoppageintheproductionline,resultinginsignificantcostsforthemanufacturingcompany.Therefore,manufacturersneedtomanagetheirsparepartseffectivelytominimizedowntimeandproductionlosses.
Algorithmsforgeneratinganddisseminatingdemandsignals
Oneofthecriticalcomponentsofsparepartsmanagementiscreatingaccurateandtimelydemandsignals.Demandsignalsaregeneratedthroughvarioustypesofalgorithms,includingforecastingalgorithms,timeseriesanalysis,machinelearningalgorithms,andpredictivemodels.
Forecastingalgorithmsareusedtopredictfuturesparepartsdemandbyanalyzinghistoricaldataandtrends.Theyhelpmanufacturersbetteranticipatesparepartsdemandandaligntheirinventoryaccordingly.Timeseriesanalysisisoftenusedinconjunctionwithforecastingalgorithmstoidentifypatternsandperiodicvariationsthatmayaffectdemand.
Machinelearningalgorithmsareincreasinglybeingusedtogeneratedemandsignalsbyanalyzingdatafrommultiplesources,suchasmaintenancelogsandsensordata.Thesealgorithmscanidentifyandpredictpotentialequipmentfailuresbeforetheyoccur,helpingmanufacturerstominimizedowntimeandoptimizeinventory.
Predictivemodelsare