Shared Resources and Efficiency Decomposition in Two-Stage Networks

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 208)


In many real world scenarios, decision making units (DMUs) may have a two-stage structure with input resources shared by both stages of operations. The distinguishing characteristic is that some of the inputs to the first stage are also consumed by the second stage, and some of the shared inputs cannot be conveniently split up and allocated to operations of the two stages. Recognizing this distinction is critical for these types of DEA applications because measuring the efficiency of the production for first-stage outputs can be misleading and understate the efficiency if DEA fails to consider that some of the inputs generate other second-stage outputs. This chapter presents DEA models for measuring the performance of two-stage network processes with non-splittable shared inputs.


Efficiency Intermediate measures Shared resources Two-stage network 


  1. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.CrossRefGoogle Scholar
  2. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.CrossRefGoogle Scholar
  3. Chen, Y., & Zhu, J. (2004). Measuring information technology’s indirect impact on firm performance. Information Technology & Management Journal, 5(1–2), 9–22.CrossRefGoogle Scholar
  4. Chen, Y., Du, J., Sherman, H. D., & Zhu, J. (2010). DEA model with shared resources and efficiency decomposition. European Journal of Operational Research, 207, 339–349.CrossRefGoogle Scholar
  5. Cook, W. D., & Hababou, M. (2001). Sales performance measurement in bank branches. OMEGA, 29, 299–307.CrossRefGoogle Scholar
  6. Cook, W. D., Hababou, M., & Tuenter, H. (2000). Multicomponent efficiency measurement and shared inputs in data envelopment analysis: An application to sales and service performance in bank branches. Journal of Productivity Analysis, 14(3), 209–224.CrossRefGoogle Scholar
  7. Huang, Z. M., & Li, S. X. (2001). Co-op advertising models in a manufacturing-retailing supply chain: A game theory approach. European Journal of Operational Research, 135, 527–544.CrossRefGoogle Scholar
  8. Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418–429.CrossRefGoogle Scholar
  9. Liang, L., Cook, W. D., & Zhu, J. (2008). DEA models for two-stage processes: Game approach and efficiency decomposition. Naval Research Logistics, 55, 643–653.CrossRefGoogle Scholar
  10. Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 US commercial banks. Management Science, 45(9), 1270–1288.CrossRefGoogle Scholar
  11. Sexton, T. R., & Lewis, H. F. (2003). Two-stage DEA: An application to major league baseball. Journal of Productivity Analysis, 19(2–3), 227–249.CrossRefGoogle Scholar
  12. Wang, C. H., Gopal, R., & Zionts, S. (1997). Use of data envelopment analysis in assessing information technology impact on firm performance. Annals of Operations Research, 73, 191–213.CrossRefGoogle Scholar
  13. Zhu, J. (2000). Multi-factor performance measure model with an application to Fortune 500 companies. European Journal of Operational Research, 123(1), 105–124.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Manning School of BusinessUniversity of Massachusetts at LowellLowellUSA
  2. 2.School of Economics and ManagementTongji UniversityShanghaiPeople’s Republic of China
  3. 3.D’Amore-McKim School of BusinessNortheastern UniversityBostonUSA
  4. 4.School of BusinessWorcester Polytechnic InstituteWorcesterUSA

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