Supply chain DEA: production possibility set and performance evaluation model

Performance evaluation is of great importance for effective supply chain management. The foundation of efficiency evaluation is to faithfully identify the corresponding production possibility set. Although a lot of researches have been done on supply chain DEA models, the exact definition for supply chain production possibility set is still in absence. This paper defines two types of supply chain production possibility sets, which are proved to be equivalent to each other. Based upon the production possibility set, a supply chain CRS DEA model is advanced to appraise the overall technical efficiency of supply chains. The major advantage of the model lies on the fact that it can help to find out the most efficient production abilities in supply chains, by replacing or improving inefficient subsystems (supply chain members). The proposed model also directly identifies the benchmarking units for inefficient supply chains to improve their performance. A real case validates the reasonableness and acceptability of this approach.

[1]  Chi Kin Chan,et al.  Successful Strategies in Supply Chain Management , 2005 .

[2]  Desheng Dash Wu,et al.  A note on DEA efficiency assessment using ideal point: An improvement of Wang and Luo's model , 2006, Appl. Math. Comput..

[3]  David J. Murphy,et al.  Purchasing performance evaluation: with data envelopment analysis , 2002 .

[4]  Desheng Dash Wu,et al.  Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank , 2006, Expert Syst. Appl..

[5]  Magnus Tambour,et al.  Productivity and customer satisfaction in Swedish pharmacies: A DEA network model , 1999, Eur. J. Oper. Res..

[6]  Vasilios T. Voudouris,et al.  Mathematical programming techniques to debottleneck the supply chain of fine chemical industries , 1996 .

[7]  David L. Olson,et al.  A comparison of stochastic dominance and stochastic DEA for vendor evaluation , 2008 .

[8]  Joe Zhu,et al.  DEA models for supply chain efficiency evaluation , 2006, Ann. Oper. Res..

[9]  K. Gourdin,et al.  MEASURING THE EFFICIENCY OF THE LOGISTICS PROCESS. , 1991 .

[10]  Thomas R. Sexton,et al.  Network DEA: efficiency analysis of organizations with complex internal structure , 2004, Comput. Oper. Res..

[11]  Walter Ukovich,et al.  DEA-like models for the efficiency evaluation of hierarchically structured units , 2004, Eur. J. Oper. Res..

[12]  Desheng Dash Wu,et al.  Performance evaluation: An integrated method using data envelopment analysis and fuzzy preference relations , 2009, Eur. J. Oper. Res..

[13]  R. Narasimhan,et al.  Exploring flexibility and execution competencies of manufacturing firms , 2004 .

[14]  R. C. Baker,et al.  A multi-phase mathematical programming approach for effective supply chain design , 2002, Eur. J. Oper. Res..

[15]  Liang Liang,et al.  A DEA game model approach to supply chain efficiency , 2006, Ann. Oper. Res..

[16]  Anthony D. Ross,et al.  An analysis of operations efficiency in large-scale distribution systems , 2004 .

[17]  Marvin D. Troutt,et al.  Optimal throughput for multistage input-output processes , 2001 .

[18]  Ury Passy,et al.  An efficiency measurement framework for multi-stage production systems , 2006, Ann. Oper. Res..

[19]  James R. Evans,et al.  Blending OR/MS, Judgment, and GIS: Restructuring P&G's Supply Chain , 1997 .

[20]  Hau L. Lee,et al.  Material Management in Decentralized Supply Chains , 1993, Oper. Res..

[21]  Anthony D. Ross,et al.  An integrated benchmarking approach to distribution center performance using DEA modeling , 2002 .

[22]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[23]  Kazuyoshi Ishii,et al.  Integrated production, inventory and distribution systems , 1988 .

[24]  Francis J. Vasko,et al.  Consolidating Product Sizes to Minimize Inventory Levels for a Multi-Stage Production and Distribution System , 1993 .

[25]  Tayfur Altiok,et al.  Multi-stage, pull-type production/inventory systems , 1995 .

[26]  Marvin D. Troutt,et al.  Multi-Stage Efficiency Tools for Goal Setting and Monitoring in Supply Chains , 2005 .

[27]  C. Weber,et al.  Determination of paths to vendor market efficiency using parallel coordinates representation: A negotiation tool for buyers , 1996 .