Annals of Operations Research

, Volume 145, Issue 1, pp 5–13 | Cite as

A DEA game model approach to supply chain efficiency

  • Yao Chen
  • Liang Liang
  • Feng Yang


Data envelopment analysis (DEA) is a useful method to evaluate the relative efficiency of peer decision making units (DMUs). Based upon the definitions of supply chain efficiency, we investigate the efficiency game between two supply chain members. It is shown that there exist numerous Nash equilibriums efficiency plans for the supplier and the manufacturer with respect to their efficiency functions. A bargaining model is then proposed to analyze the supplier and manufacturer's decision process and to determine the best efficiency plan strategy. DEA efficiency for supply chain operations is studied for the central control and the decentralized control cases. The current study is illustrated with a numerical example.


Supply chain Data envelopment analysis (DEA) Efficiency Nash equilibrium Bargaining model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Castelli, L., R. Pesenti, and W. Ukovich. (2004). “DEA-Like Models for the Efficiency Evaluation of Hierarchically Structured Units.” European Journal of Operational Research 154, 465–476.CrossRefGoogle Scholar
  2. Charnes, A., W.W. Cooper, and E. Rhodes. (1978). “Measuring the Efficiency of Decision Making Units.” European Journal of Operational Research 2(6), 429–444.CrossRefGoogle Scholar
  3. Easton, L., D.J. Murphy, and J.N. Pearson. (2002). “Purchasing Performance Evaluation: With Data Envelopment Analysis.” European Journal of Purchasing & Supply Management 8, 123–134.CrossRefGoogle Scholar
  4. Lothgren, M. and M. Tambour. (1999). “Productivity and Customer Satisfaction in Swedish Pharmacies: A DEA Network Model.” European Journal of Operational Research 115, 449–458.CrossRefGoogle Scholar
  5. Shaked, A. and J. Sutton. (1984). “Unvoluntary Unemployment as a Perfect Equilibrium in a Bargaining Model.” Econometrica 52, 1351–1364.CrossRefGoogle Scholar
  6. Troutt, M.D., P. Ambrose, and C.K. Chan. (2001). “Optimal Throughput for Multistage Input-Output Processes.” International Journal of Operations and Production Management 21(1), 148–158.CrossRefGoogle Scholar
  7. Weber, C.A. and A. Desai. (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.CrossRefGoogle Scholar
  8. Zhu, J. (2002). Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets. Boston: Kluwer Academic Publishers.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.College of ManagementUniversity of MassachusettsLowellUSA
  2. 2.School of BusinessUniversity of Science and Technology of ChinaHe FeiP.R. China

Personalised recommendations