基于快速响应供应链的提前期研究

上海交通大学硕士学位论文基于快速响应供应链的提前期研究姓名：许志焱申请学位级别：硕士专业：企业管理指导教师：季建华20050101RESEARCHOFLEADTIMEINQUICKLYRESPONDINGSUPPLYCHAINABSTRACTWiththedevelopmentoftechnologyandeconomy,customers’consumptionlevelisincreasingcontinuously.Customers’needisdiversifiedandindividual.Thecompetitionamongsupplychainsfocusesonsatisfyingcustomers’need.Oneofthemostimportantwaystoincreasecustomers’satisfactionistorespondtothecustomers’needinarapidspeed.Thisthesisexplainstheproblemofquickresponseinsupplychainfromthepointofleadtimemanagement.Itanalyzeshowtodecideleadtimeaccuratelyinquicklyrespondingsupplychainfromacademicmethod,demonstration,marketresearchandsimulation.Thenitbringssomemethodstodecreaseleadtimeeffectivelyinordertoadapttherequirementofquickresponseinsupplychain.Firstly,thethesisstatesthebackgroundandresearchvalue,introducessomerelatedresearchresultsintheworldandbringstheideaofestablishingquicklyrespondingsupplychaintoadapttotherapidchangesinmarketsandagainstthesupplychainrisks.Secondly,thethesisexplainsthedefinitionandobjectiveofquicklyrespondingsupplychain.Itbringsthedefinitionandcomposingofleadtimeinsupplychain.Itindicatesthatleadtimeistheimportantbasiswhenexecutingandrunningquicklyrespondingsupplychain.Italsoanalyzestherelationsbetweenquickresponseandleadtimemanagement.ThenthisthesisprovesthatdecreasingleadtimeistheeffectivewaytorealizequickresponseinsupplychainthroughintroducingandanalyzingthecaseifYD.Thirdly,thispaperintroducessomemethodstodecideleadtimedynamicallyincludingthetraditionalwayusedinERP/MRP-,fillermodelandOPTtheory.ItresearcheshowtouseNeuralNetworkTheorytosetupleadtimedecisionmodelandbringsmathematicsmethodanddemonstration.ThenitusesmethodofcomputersimulationtoestablishaNeuralNetworkmodelonthebasisofleadtimemanagementinoneautomaticpartscompany.Itanalyzeshowtomanageleadtimedynamicallyinordertoincreasetheresponsespeedinsupplychain.Fourthly,thisthesispointsoutthatthereiscontradictionbetweenthebenefitsandcostsofdecreasingleadtimeinsupplychain.Thenitusesthemethodofmarginalanalysisandbringstheconditionofdecreasingtheleadtimeaccordingtocostandbenefit.Finally,thisthesisanalyzestwenty-fivefactorsthoseaffectedtheleadtimeinsupplychainthroughpracticalsurveyandstatisticalmethod.Thenitclassifiesandanalyzesthesefactors.Italsobringssomegeneralformsofshorteningleadtimefromthepointsofsourcing,manufacturingandlogistics.Theinnovationsofthisthesiscanbesummarizedasfollows:Firstly,itbringsthewayofdecidingtheleadtimeinsupplychainusingthemodelofNeuralNetworkandprovesitsserviceabilitythroughcaseanalysis.Secondly,thethesisestablishestheconditionofdecreasingleadtimethroughanalyzingcostsandbenefits.Thirdly,itexplainsthemethodsofdecreasingleadtimeinsupplychainsystematicallyfromtheaspectsofsourcing,manufacturingandlogistics.KEYWORDSquickresponse,supplychain,leadtime,responsetime-3-1.1-4-1.1.1-5-1.1.2-6-1.1.31.2-7-1.31.3.1-8-1.3.21.4-9-Figure1-1ResearchFrameofThesisERP/MRP-OPT-10-2.12.1.12.1.2-11-2.1.3-12-Figure2-1QuicklyRespondingSupplyChain2.2-13-2.2.12.2.2-14-Figure2-2BasicStructureofLeadTimeinSupplyChain2.32.42.4.1-15-2.4.22.4.3-16-3.1ERP/MRP-6N2LT+×=N-17-3.2OPT3.2.1[23]3-1Figure3-1FillerModel-18-3.2.2OPTOPTEliGoldratt2070OPTOPTOPT9OPTOPT3.33.3.1-19-3.3.2-20-Figure3-23-levelNeuralNetworkModelixktijwjkvjqkr-21-)(xfjzky)(1jniiijjxwfzq-=∑=)(1kmjjjkkrzvfy-=∑=kdjd)1()(kkkkkyyyt--=d∑=-=qkjkkjjjzzd1)1(ndijijijxdwwa+=jkjkjkzadnn+=jjjdbqq+=kkkrrbd+=()pytEpqpqpq2/2∑∑-=3.4-22-3.4.1-23-3-1103-13.4.2()()x-+=exp11)x(f-24-Figure3-3ImageofActivateFunctionfx3.4.310.5-25-Figure3-4CalculatingProcessYYNNPxitkzjykdjwijvjks+(tk-yk)2/2psPe+IPese-26--27-3.4.4-28-4.14.1.1Figure4-1AgeStructureofInvestigationObjects-29-2Figure4-2EducationDegreeStructureofInvestigationObjects3Figure4-3PositionStructureofInvestigationObjects-30-4Figure4-4IndustryStructureofInvestigationObjects4.1.2-31-4.24.2.1ŸŸŸŸŸŸŸFigure4-5Multi-levelLeadTimeinSupplyChainABC-32-)(iTT),(jiT),,(kjiT∑∑∑===++=213121),3,(),2,(),1,()i(kkkkiTkiTkiTTT4.2.2ŸŸŸFigure4-6CostStructureofResponseinSupplyChain)(iTC),(jiC),,(kjiC∑∑∑===++=213121),3,(),2,(),1,()(kkkkiCkiCkiCiTC4.2.3)(iTTΔqkjiT),,(hkjiT),,(q)i(TTh)i(TTABC-33-∑∑∑===-+-+-=Δ213121]),3,(),3,([]),2,(),2,([]),1,(),1,([))((kqhkqhkqhkiTkiTkiTkiTkiTkiTiTT∑∑∑===++=21h31h21hh),3,(),2,(),1,()i(kkkkiTkiTkiTTT∑∑∑===++=21q31q21qq),3,(),2,(),1,()i(kkkkiTkiTkiTTT))((iTCΔqkjiC),,(hkjiC),,(q)(iTCh)(iTC∑∑∑===-+-+-=Δ213121]),3,(C),3,(C[]),2,(C),2,(C[]),1,(C),1,(C[))(C(kqhkqhkqhkikikikikikiiT∑∑∑===++=21h31h21hh),3,(),2,(),1,()(kkkkiCkiCkiCiTC∑∑∑===++=21q31q21qq),3,(),2,(),1,()(kkkkiCkiCkiCiTC)(iTTΔ)(iE)i(1)(TTiE=)(iEqhiE)()(iR))i(I(Δ)(R)i(1)i(1))i(I(qhiTTTT×⎟⎟⎠⎞⎜⎜⎝⎛-=Δ))i(C(Δhqqhh)i(1)i()()i()())i(C(TTTTiTCTTiTC×⎟⎟⎠⎞⎜⎜⎝⎛-=Δn4-34-0))i((ΔTT0))i((ΔTT0))i(TC(Δ0))i((ΔTT))n(I(Δ∑∑∑===⎥⎥⎦⎤⎢⎢⎣⎡×⎟⎟⎠⎞⎜⎜⎝⎛-=×Δ=Δ=Δn1in1iqhn1i)i(R)i(1)i(1)]i(R))i(E([))i(I())n(I(TTTT))n(C(Δ∑∑==⎥⎥⎦⎤⎢⎢⎣⎡×⎟⎟⎠⎞⎜⎜⎝⎛-=Δ=Δn1ihqqhhn1i)i(1)i()()i()())i(C())n(C(TTTTiTCTTiTC))n(I(Δ))n(C(Δ()()()()nCnIΔΔ()()()()nCnIΔΔ()()()()nCnIΔ=Δ-35-5.15.1.12512345678910111213ERP141516-36-171819/20212223242525543215.1.25-132