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Supply chain DEA: production possibility set and performance evaluation model

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Abstract

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.

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References

  • Altiok, T., & Ranjan, R. (1995). Multi-stage, pull-type production/inventory systems. IIE Transactions, 27, 190–200.

    Article  Google Scholar 

  • China Construction Bank (Anhui Branch) (2004). Annual report 2004 of China Construction Bank in Anhui Province (in Chinese).

  • Camm, J. D., Chorman, T. E., Dull, F. A., Evans, J. R., Sweeney, D. J., & Wegryn, G. W. (1997). Blending OR/MS, judgment, and GIS: restructuring P&G’s supply chain. Interfaces, 27(1), 128–142.

    Article  Google Scholar 

  • Castelli, L., Pesenti, R., & Ukovich, W. (2004). DEA-like models for the efficiency evaluation of hierarchically structured units. European Journal of Operational Research, 154, 465–476.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    Article  Google Scholar 

  • Chen, Y., Liang, L., & Yang, F. (2006). A DEA game model approach to supply chain efficiency. Annals of Operations Research, 1451, 5–13.

    Article  Google Scholar 

  • Clarke, R. L., & Gourdin, K. N. (1991). Measuring the efficiency of the logistics process. Journal of Business Logistics, 122, 17–33.

    Google Scholar 

  • Cohen, M. A., & Lee, H. L. (1989). Resource deployment analysis of global manufacturing and distribution networks. Journal of Manufacturing and Operations Management, 2, 81–104.

    Google Scholar 

  • Easton, L., Murphy, D. J., & Pearson, J. N. (2002). Purchasing performance evaluation:with data envelopment analysis. European Journal of Purchasing & Supply Management, 8, 123–134.

    Article  Google Scholar 

  • Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 341, 35–49.

    Article  Google Scholar 

  • Golany, B., Hackman, S. T., & Passy, U. (2003). An efficiency measurement framework for multi-stage production systems. Annals of Operations Research, 1451, 51–68.

    Google Scholar 

  • Ishii, K., Takahashi, K., & Muramatsu, R. (1988). Integrated production, inventory and distribution systems. International Journal of Production Research, 263, 473–482.

    Google Scholar 

  • Lee, H. L., & Billington, C. (1993). Material management in decentralized supply chains. Operations Research, 41, 835–847.

    Article  Google Scholar 

  • Lewis, H. F., & Sexton, T. R. (2004). Network DEA: efficiency analysis of organizations with complex internal structure. Computers and Operations Research, 319, 1365–1410.

    Article  Google Scholar 

  • Liang, L., Yang, F., Cook, W. D., & Zhu, J. (2006). DEA models for supply chain efficiency evaluation. Annals of operation research, 1451, 35–49.

    Article  Google Scholar 

  • Lothgren, M., & Tambour, M. (1999). Productivity and customer satisfaction in Swedish pharmacies:A DEA network model. European Journal of Operational Research, 115, 449–458.

    Article  Google Scholar 

  • Narasimhan, R., Talluri, S., & Das, A. (2004). Exploring flexibility and execution competencies of manufacturing firms. Journal of Operations Management, 22, 91–106.

    Article  Google Scholar 

  • Newhart, D. D., Stott, K. L., & Vasko, F. J. (1993). Consolidating production sizes to minimize inventory levels for a multi-stage production and distribution systems. Journal of the Operational Research Society, 447, 637–644.

    Google Scholar 

  • Ross, A., & Droge, C. (2002). An integrated benchmarking approach to distribution center performance using DEA modeling. Journal of Operations Management, 20, 19–32.

    Article  Google Scholar 

  • Ross, A., & Droge, C. (2004). An analysis of operations efficiency in large-scale distribution systems. Journal of Operations Management, 21, 673–688.

    Article  Google Scholar 

  • Talluri, S., & Baker, R. C. (2002). A multi-phase mathematical programming approach for effective supply chain design. European Journal of Operational Research 141(3), 546–560. This paper is ranked 21 in highly requested papers in EJOR from April 2002–April 2004 with 1,114 requests.

    Article  Google Scholar 

  • Troutt, M. D., Ambrose, P., & Chan, C. K. (2001). Optimal throughput for multistage input-output processes. International Journal of Operations and Production Management, 21(1), 148–158.

    Article  Google Scholar 

  • Troutt, M. D., Ambrose, P., & Chan, C. K. (2004). Multi-stage efficiency tools for goal setting and monitoring in supply chains. In C. K. Chan & H. W. J. Lee (Eds.), Successful strategies in supply chain management Hershey: Idea Group Publishing Co.

    Google Scholar 

  • Voudouris, V. T. (1996). Mathematical programming techniques to debottleneck the supply chain of fine chemical industries. Computers and Chemical Engineering, 20, S1269–S1274.

    Article  Google Scholar 

  • Weber, C. A., & Desai, A. (1996). Determinants of paths to vendor market efficiency using parallel coordinates representation:a negotiation tool for buyers. European Journal of Operational Research, 90, 142–155.

    Article  Google Scholar 

  • Wu, D. (2008). A note on dea efficiency assessment using ideal point: an improvement of Wang and Luo’s model. Applied Mathematics and Computation, 183(2), 819–830.

    Article  Google Scholar 

  • Wu, D. (2009). Performance evaluation: an integrated method using data envelopment analysis and fuzzy preference relations. European Journal of Operational Research, 194(1), 227–235.

    Article  Google Scholar 

  • Wu, D., & Olson, D. L. (2008). A comparison of stochastic dominance and stochastic DEA for vendor evaluation. International Journal of Production Research, 46, 2313–2327.

    Article  Google Scholar 

  • Wu, D., Yang, Z., & Liang, L. (2006). Using DEA-neural network approach to evaluate branch efficiency of a large Canadian Bank. Expert Systems with Applications 31(1), 108–115.

    Article  Google Scholar 

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Correspondence to Desheng Dash Wu.

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Yang, F., Wu, D., Liang, L. et al. Supply chain DEA: production possibility set and performance evaluation model. Ann Oper Res 185, 195–211 (2011). https://doi.org/10.1007/s10479-008-0511-2

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