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Click to edit Master title style,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,*,*,Click to edit Master title style,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,*,Supply Chain Outsourcing in,Enterprise Risk Management:,A,DEA VaR Model,Desheng Dash Wu,University of Toronto,Reykjavik University,RiskLab,dashrisklab.ca,Extracted from,Olson D.L.and Wu D.,Enterprise Risk Management,.World Scientific Publisher.2007 Wu D.and Olson D.L.,A Comparison of Stochastic Dominance and Stochastic DEA for,Vendor Evaluation,.Int J of Production Research.2007(1).,Nov,2008,Call for paper,Outline,Introduction,Enterprise Risk Management(ERM,全面风险管理,),Supply Chain Outsourcing,Vendor Evaluation,Contribution:,ERM steps in Supply Chain Outsourcing Risk,Data envelopment analysis,(DEA)+,Value at Risk,(VaR):Intuition,Conclusions and,Future Research,(,银行链,金融危机,),Review of Risk Management Tools,风险管理工具介绍,Risk Management tools,mean-variance framework of portfolio theory i.e.,selection and diversification(Markowitz 1952),Capital Asset Pricing Model(Sharpe 1964;Lintner 1965;Mossin 1966),Arbitrage Pricing Theory(Ross,1976),Option pricing theory(Black 1972;Black 1973),Value at Risk(VaR),RiskMetrics(Jorion 1997),Prob 1 day Loss VaR=1-,Min,VaR,P(VaR),Enterprise Risk Management,Professional organization,Consultant,Rating agency,Academics,31%adopted ERM in Canadian risk&insurance Kleffner 2003,Why ERM?Toyota,Review of Risk Management Tools,(cont.),Various Risks:$Measurement,Definition of ERM,Systematic,integrated approach,Manage all risks facing organization,External,Economic(market-price,demand change),Financial(insurance,currency exchange),Political/Legal,Technological,Internal,Human error,Fraud,Systems failure,Disrupted production,Stochastic OR Models for Risk Management(Beneda 2005,Dash&Kajiji 2005),Multiple criteria analysis,Subjective,Simulation,Probabilistic;Can be subjective(system dynamics),Data envelopment analysis(DEA),Optimization,Objective,subjective,probabilistic,ERM Research and Steps,Step 1,:,Determine the corporations,objectives,Step 2,:,Identify,the risk factors,exposures,Step 3,:,Quantify,the factors,exposures,Assess the,impact,Step 4,:,Examine alternative risk management,tools,Step 5,:,Select,appropriate risk management approach,Step 6,:,Implement,and,monitor,program,More than 80 frameworks:problem-oriented,descriptive,frameworks,Specific ERM:Supply Chain Outsourcing Risk,Supplier,Manufacturer,Retailer,End customer,Warehouse,A Supply Chain,Model,Supply Chain Vendor Selection,Supply Chain Vendor Selection,go,o,ds,input,bads,(risk,uncertainty?),(risk,uncertainty?),Efficiency,=output/input,Supplier,P,erformance,Data Envelopment Analysis(DEA)-Deterministic,Charnes,Cooper,Rhodes,n Vendors(DMUs)to be evaluated.,m different inputs Xij,s different outputs Yrj.,The,deterministic DEA,model,DEA efficiency for,DMUj:,Deterministic,DEA,(cont.),CCR Multiplier form,DEA VaR,-Stochastic,model,j,:,aspiration level,;,j,:,risk criterion;,0,j,j,1,Intuition:,1)At what confidence level,it is efficient to select the?th Vendor?,2)At what confidence level,it is enough to reduce the?th cost in order to make the?th Vendor efficient?,(1),Stochastic DEA,Assuming multivariate normal distribution:,(2),Equivalent linear programming,:,(3),Metrics in Vendor Selection,Olson&Wu,Criteria,Number of studies using,Price/cost,12,Acceptance/quality,12,On-time response/logistics,12,R&D in technology/innovation/design,7,Production facilities/assets,6,Flexibility/agility,6,Service,4,Management&organization,2,Data Set,Moskowitz,Tang&Lam,2000,Decision Sciences,31,327-360,9 vendors,V,j,Mean,Standard deviation,Normally distributed,12 Criteria each with weight,W,i,Quality personnel,Quality procedure,Concern for quality,Company history,Price-quality,Actual price,Financial ability,Technical performance,Delivery history,Technical assistance,Production capability,Manufacturing equipment,Sample data demonstration,Criteria,V1,V2,V3,V4,V5,V6,V7,V8,V9,1 Quality personnel,85,(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),2 Quality procedure,80,(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),3 Delivery history,80,(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),4 Company history,90,(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),Simulated weights and Parameter Sensitivity,Equal weights,Useful to identify dominated solutions,V,2,0.03,V,4,0.08,V,6,0.36,V,8,0.53,Ordinal weights,Reflect decision maker preference,More useful to make decision:select nondominated solutions,Used centroid weights Olson&Dorai,V,2,0.71,V,4,0.22,V,6,0.07,V,8,0,Adjusted risk criterion 0,j,1,Adjusted RHSs with,j,DEA efficiency scores:equal weight,%,V1,V2,V3,V4,V5,V6,V7,V8,V9,Average,V1,95.40,94.33,93.58,94.62,75.11,95.33,95.16,94.32,89.72,91.95,V2,93.56,95.60,94.63,95.02,79.37,93.93,94.53,92.15,90.02,92.09,V3,94.98,85.17,95.37,9
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