供應(yīng)鏈外包的DEA與風(fēng)險(xiǎn)價(jià)值模型概述

1、Supply Chain Outsourcing in Enterprise Risk Management: A DEA VaR Model Desheng Dash Wu University of Toronto Reykjavik University RiskLab dashrisklab.caExtracted from Olson D. L. and Wu D. Enterprise Risk Management. World Scientific Publisher. 2007 Wu D. and Olson D. L. A Comparison of Stochastic

2、Dominance and Stochastic DEA for Vendor Evaluation. Int J of Production Research. 2007 (1). Nov, 2008Call for paperOutline Introduction Enterprise Risk Management (ERM 全面風(fēng)險(xiǎn)管理) Supply Chain Outsourcing, Vendor Evaluation Contribution: ERM steps in Supply Chain Outsourcing Risk Data envelopment analys

3、is (DEA) + Value at Risk (VaR): Intuition Conclusions and Future Research (銀行鏈, 金融危機(jī)) Review of Risk Management Tools風(fēng)險(xiǎn)管理工具介紹Risk Management tools mean-variance framework of portfolio theory i.e., selection and diversification (Markowitz 1952) Capital Asset Pricing Model (Sharpe 1964; Lintner 1965;

4、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 ManagementProfessional organization, Consultant, Rating agency, Academics31% adopted ERM in Canadian

5、risk & insurance Kleffner 2003Why ERM? Toyota Review of Risk Management Tools (cont.)Various Risks: $ MeasurementDefinition of ERM Systematic, integrated approachManage all risks facing organization External Economic (market - price, demand change) Financial (insurance, currency exchange) Politi

6、cal/Legal Technological Internal Human error Fraud Systems failure Disrupted productionStochastic 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) Op

7、timization Objective, subjective, probabilisticERM 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

8、 management approach Step 6: Implement and monitor programMore than 80 frameworks: problem-oriented, descriptive, frameworksSpecific ERM: Supply Chain Outsourcing RiskSupplierManufacturerRetailerEnd customerWarehouseA Supply Chain ModelSupply Chain Vendor Selection Supply Chain Vendor Selection good

9、s input bads (risk, uncertainty?) (risk, uncertainty?) Efficiency= output / input SupplierPerformanceData Envelopment Analysis (DEA) -Deterministic Charnes, Cooper, Rhodes n Vendors(DMUs) to be evaluated. m different inputs Xij , s different outputs Yrj.kTkTXVYUMax1. .jTjTXVYUtsnj, 2 , 10, 0TTVUThe

10、deterministic DEA model jTjTmiijisrrjrjXVYUxvyuE11nj, 2 , 1DEA efficiency for DMUj :Deterministic DEA (cont.) ,TT max. . 1 0 ,0 TkkTkTTwv Ys tXv YXv CCR Multiplier formDEA VaR -Stochastic model max E() s.t. Pr() 1, =1,2N 0TkvTjjjTv yv yjv j :aspiration level ; j : risk criterion; 0 j , j 1Intuition:

11、 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)1,kNStochastic DEAAssuming multivariate normal distribution:1max E() s.t. (1) 0TkvTjjjjTv yv yVarv(2)Equivalent linea

12、r programming:1max s.t. (1),1, , ,0TkvTjjjjTv yv bysjNv s(3)Metrics in Vendor Selection Olson & WuCriteriaNumber of studies usingPrice/cost12Acceptance/quality12On-time response/logistics12R&D in technology/innovation/design7Production facilities/assets6Flexibility/agility6Service4Management

13、 & organization2Data SetMoskowitz, Tang & Lam, 2000, Decision Sciences 31, 327-3609 vendors VjMeanStandard deviationNormally distributed12 Criteria each with weight WiQuality personnelQuality procedureConcern for qualityCompany historyPrice-qualityActual priceFinancial abilityTechnical perfo

14、rmanceDelivery historyTechnical assistanceProduction capabilityManufacturing equipmentSample data demonstration CriteriaV1V2V3V4V5V6V7V8V91 Quality personnel85(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 procedure80(3.3)88(4.2)85(5.1)90(4.2)75(5.6)82(2.2)82(4.2)90(33)78(3

15、.8)3 Delivery history80(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 history90(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 V2 0.03, V4 0.08, V6 0.36, V8 0.53 Ord

16、inal weights Reflect decision maker preference More useful to make decision: select nondominated solutions Used centroid weights Olson & Dorai V2 0.71, V4 0.22, V6 0.07, V8 0 Adjusted risk criterion 0 j 1 Adjusted RHSs with jDEA efficiency scores: equal weight%V1V2V3V4V5V6V7V8V9AverageV195.4094.

17、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.

18、32V790.6993.2193.8793.3792.9791.3793.9693.1791.2492.65V894.9394.4096.3094.6686.3794.2994.1596.6495.9794.19V993.0692.8893.5592.9492.8293.7693.6593.2293.78Explanation of ResultsV195.4094.3393.5894.6275.1195.3395.1694.3289.7291.95Self-Rated Score: 95.40=V* Mean(V1)Cross-Rated Score: 94.33=V* Mean(V2)95

19、% chance, V1 select V6 , V7 Explanation of Results95% chance, V3 nondominatedHow? Slacks 4% Quality+ 5.5% DeliveryRaw data:Quality 85 (5.1)Delivery 70 (5.5)V3V495.3792.2795.9398.11Rankings: Stochastic DEA Stochastic efficiency without weight restriction Diagonal V4 V5 V8 V2 V1 V3 V7 V9 V6 Using aver

20、ages V8 V4 V9 V7 V3 V6 V2 V1 V5 Stochastic efficiency with weight restriction Diagonal V8 V5 V4 V2 V3 V7 V1 V6 V9 Using averages V2 V8 V3 V7 V5 V4 V6 V1 V9Classical DeterministicDEA Results Deterministic CCR Without weight restriction All = 1.000 With weight restriction V2 V3 V4 V5 V7 V8 V6 V9 V1 Su

21、per CCR Without weight restriction V5 V6 V3 V4 V8 V7 V2 V1 V9 With weight restriction V2 V3 V8 V4 V7 V5 V6 V1 V9 Benchmarking: V2 V4 V6 V8 Implications Benchmarking: V2 V4 V6 V8 Different Methods, Different Results Classical DEA, Super CCR fail Non-dominated: V2 - Super CCR with weight & Stochas

22、tic efficiency V8 - Stochastic efficiency without & with weight Dominated: V1 , V6 , V9 weight restriction; Conclusions and Future Research Risk management of growing importance OR Models useful in RM DEA methods can deal with high levels of complexity DEA VaR Model Time horizonTwo variations Cost-ori