供应链外包的DEA和风险价值模型2

SupplyChainOutsourcingin

EnterpriseRiskManagement:ADEAVaRModel

DeshengDashWuUniversityofTorontoReykjavikUniversityRiskLabdash@risklab.caExtractedfrom

OlsonD.L.andWuD.EnterpriseRiskManagement.WorldScientificPublisher.2007WuD.andOlsonD.L.

AComparisonofStochasticDominanceandStochasticDEAforVendorEvaluation.IntJofProductionResearch.2007(1).

Nov,2008CallforpaperOutlineIntroductionEnterpriseRiskManagement((ERM全面风险险管理)SupplyChainOutsourcing,,VendorEvaluationContribution::ERMstepsinSupplyChainOutsourcingRiskDataenvelopmentanalysis(DEA)++ValueatRisk(VaR):IntuitionConclusionsandFutureResearch(银行链,金融危机机)ReviewofRiskManagementTools风险管理理工具介介绍RiskManagementtoolsmean-varianceframeworkofportfoliotheoryi..e.,,selectionanddiversification(Markowitz1952)CapitalAssetPricingModel((Sharpe1964;Lintner1965;;Mossin1966)ArbitragePricingTheory((Ross,1976)Optionpricingtheory((Black1972;;Black1973))ValueatRisk(VaR)),RiskMetrics((Jorion1997))Prob{1dayLoss≤≤VaR}==1-αMin{VaR︱P(VaR))≥α}EnterpriseRiskManagementProfessionalorganization,Consultant,Ratingagency,Academics31%adoptedERMinCanadianrisk&&insurance[[Kleffner2003]WhyERM?ToyotaReviewofRiskManagementTools(cont.))VariousRisks:$$MeasurementDefinitionofERMSystematic,integratedapproachManageallrisksfacingorganizationExternalEconomic((market-price,,demandchange))Financial((insurance,currencyexchange))Political//LegalTechnologicalInternalHumanerrorFraudSystemsfailureDisruptedproductionStochasticORModelsforRiskManagement(Beneda[[2005]],Dash&Kajiji[2005])))MultiplecriteriaanalysisSubjectiveSimulationProbabilistic;Canbesubjective((systemdynamics)Dataenvelopmentanalysis(DEA))OptimizationObjective,,subjective,,probabilisticERMResearchandStepsStep1:Determinethecorporation’sobjectivesStep2:Identifytheriskfactors,exposuresStep3:Quantifythefactors,exposuresAssesstheimpactStep4:ExaminealternativeriskmanagementtoolsStep5:SelectappropriateriskmanagementapproachStep6:ImplementandmonitorprogramMorethan80frameworks:problem-oriented,descriptive,,frameworksSpecificERM::SupplyChainOutsourcingRiskSupplierManufacturerRetailerEndcustomerWarehouseASupplyChainModelSupplyChainVendorSelectionSupplyChainVendorSelectiongoodsinputbads(risk,uncertainty??)(risk,uncertainty??)Efficiency=output//inputSupplierPerformanceDataEnvelopmentAnalysis(DEA))--Deterministic{Charnes,Cooper,Rhodes}}nVendors((DMUs)tobeevaluated.mdifferentinputsXij,sdifferentoutputsYrj.ThedeterministicDEAmodel

DEAefficiencyforDMUj:DeterministicDEA(cont.))

CCRMultiplierformDEAVaR-Stochasticmodelβj:aspirationlevel;αj:riskcriterion;0≤αj,βj≤1Intuition::1)Atwhatconfidencelevel,,itisefficienttoselectthe?thVendor??2)Atwhatconfidencelevel,,itisenoughtoreducethe?thcostinordertomakethe?thVendorefficient?(1)StochasticDEAAssumingmultivariatenormaldistribution:(2)Equivalentlinearprogramming:(3)MetricsinVendorSelection{Olson&&Wu}CriteriaNumberofstudiesusingPrice/cost12Acceptance/quality12On-timeresponse/logistics12R&Dintechnology/innovation/design7Productionfacilities/assets6Flexibility/agility6Service4Management&organization2DataSetMoskowitz,,Tang&&Lam,,2000,,DecisionSciences31,327-3609vendorsVjMeanStandarddeviationNormallydistributed12CriteriaeachwithweightWiQualitypersonnelQualityprocedureConcernforqualityCompanyhistoryPrice-qualityActualpriceFinancialabilityTechnicalperformanceDeliveryhistoryTechnicalassistanceProductioncapabilityManufacturingequipmentSampledatademonstrationCriteriaV1V2V3V4V5V6V7V8V91Qualitypersonnel85(5.2)82(4.2)90(3.1)78(12.8)95(1.5)75(2.9)90(1.7)70(12.2)75(2.8)2Qualityprocedure80(3.3)88(4.2)85(5.1)90(4.2)75(5.6)82(2.2)82(4.2)90(33)78(3.8)3Deliveryhistory80(4.7)83(5.5)70(5.5)75(14.3)85(5.8)85(1.9)75(5.9)90(2.4)90(1.1)4Companyhistory90(5.5)88(4.5)75(7.0)85(5.6)70(5.6)80(4.1)80(4.6)85(4.5)82(3.7)SimulatedweightsandParameterSensitivityEqualweightsUsefultoidentifydominatedsolutions{V20.03,V40.08,V60.36,V80.53}OrdinalweightsReflectdecisionmakerpreferenceMoreusefultomakedecision:selectnondominatedsolutionsUsedcentroidweights{{Olson&Dorai}}{V20.71,V40.22,V60.07,V80}Adjustedriskcriterion0≤αj≤1AdjustedRHSswithβjDEAefficiencyscores::equalweight%V1V2V3V4V5V6V7V8V9AverageV195.4094.3393.5894.6275.1195.3395.1694.3289.7291.95V293.5695.6094.6395.0279.3793.9394.5392.1590.0292.09V394.9885.1795.3792.2794.8388.5592.9694.7192.2592.34V489.6190.2895.9398.1189.2393.8894.3297.4594.8893.74V585.8683.0191.0495.6398.1083.6488.8091.0886.1689.26V692.6992.8792.8492.1192.3293.4793.2988.4692.8692.32V790.6993.2193.8793.3792.9791.3793.9693.1791.2492.65V894.9394.4096.3094.6686.3794.2994.1596.6495.9794.19V993.0692.8893.5592.9492.8293.7693.6593.2293.7893.30ExplanationofResultsV195.4094.3393.5894.6275.1195.3395.1694.3289.7291.95Self-RatedScore:95.40=V*×Mean((V1))Cross-RatedScore:94.33=V*×Mean((V2))95%chance,V1selectV6,V7ExplanationofResults95%chance,V3nondominatedHow??Slacks4%Quality+5.5%%DeliveryRawdata:Quality85(5.1)Delivery70(5.5)V3V495.3792.2795.9398.11Rankings:StochasticDEAStochasticefficiencywithoutweightrestrictionDiagonalV4>V5>V8>V2>V1>V3>V7>V9>V6UsingaveragesV8>V4>V9>V7>V3>V6>V2>V1>V5StochasticefficiencywithweightrestrictionDiagonalV8>V5>V4>V2>V3>V7>V1>V6>V9UsingaveragesV2>V8>V3>V7>V5>V4>V6>V1>V9ClassicalDeterministicDEAResultsDeterministicCCRWithoutweightrestrictionAll==1.000Withweightrestriction{V2V3V4V5V7V8}>V6>V9>V1SuperCCRWithoutweightrestrictionV5>V6>V3>V4>V8>V7>V2>V1>V9WithweightrestrictionV2>V3>V8>V4>V7>V5>V6>V1>V9Benchmarking::{V2V4V6V8}ImplicationsBenchmarking::{V2V4V6V8}DifferentMethods,DifferentResultsClassicalDEA,SuperCCRfailNon--dominated:V2-SuperCCRwithweight&&StochasticefficiencyV8-Stochasticefficiencywithout&&withweightDominated::V1,V6,V9–weightrestriction;ConclusionsandFutureResearchRiskmanagementofgrowingimportanceORModelsusefulinRMDEAmethodsc