A Network-DEA Model with Internal Dynamic Effects

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


Modern production networks are comprised of a large collection of interrelated value-adding processes. Adding to this, the flow time of such a complex network can easily go from weeks to months, making time an influential factor in constructing efficient frontier for the network technology. Both the network structure and dynamics in the production environment stand in sharp contrast with the standard DEA model, which assumes that the internal process is a “black box” and that inputs and outputs are independent across time periods. In light of the above limitations, this chapter introduces an approach to computing the technical efficiency scores for a dynamic production network and its sub-processes.


Network Productive efficiency Dynamic effects 


  1. Banker, R., & Thrall, R. (1992). Estimation of returns to scale using data envelopment analysis. European Journal of Operational Research, 62, 74–84.CrossRefGoogle Scholar
  2. Battese, G., & Coelli, T. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325–332.CrossRefGoogle Scholar
  3. Castelli, L., Pesenti, R., & Ukovich, W. (2001). DEA-like models for efficiency evaluations of specialized and interdependent units. European Journal of Operational Research, 132, 274–286.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.CrossRefGoogle Scholar
  6. Chen, C. (2009). A network-dea model with new efficiency measures to incorporate the dynamic effect in production networks. European Journal of Operational Research, 194(3), 687–699.CrossRefGoogle Scholar
  7. Chen, C., & Van Dalen, J. (2010). Measuring dynamic efficiency: Theories and an integrated methodology. European Journal of Operational Research, 203(3), 749–760.CrossRefGoogle Scholar
  8. Clarke, D. (1976). Econometric measurement of the duration of advertising effect on sales. Journal of Marketing Research, 13, 345–357.CrossRefGoogle Scholar
  9. Cooper, W., Deng, H., Seiford, L., & Zhu, J. (2004). Congestion: Its identification and management with DEA. In W. Cooper, L. Seiford, & J. Zhu (Eds.), Handbook on data envelopment analysis (pp. 177–202). New York: Kluwer Academic.Google Scholar
  10. Cooper, W., Tone, K., & Seiford, L. (2006). Introduction to data envelopment analysis and its uses: With DEA-solver software and references. New York: Springer.Google Scholar
  11. De Meyer, A., & Ferdows, K. (1990). Influence of manufacturing improvement programmes on performance. International Journal of Operations & Production Management, 10, 120–131.CrossRefGoogle Scholar
  12. Färe, R., & Grosskopf, S. (1996). Intertemporal production frontiers: With dynamic DEA. Boston: Kluwer Academic.CrossRefGoogle Scholar
  13. Huselid, M., & Becker, B. (1996). Methodological issues in cross-sectional and panel estimates of the human resource-firm performance link. Industrial Relations, 35, 400–422.Google Scholar
  14. Lewis, H., & Sexton, T. (2004). Network DEA: Efficiency analysis of organizations with complex internal structure. Computers & Operations Research, 31, 1365–1410.CrossRefGoogle Scholar
  15. Nemoto, J., & Goto, M. (2003). Measurement of dynamic efficiency in production: An application of data envelopment analysis to Japanese electric utilities. Journal of Productivity Analysis, 19, 191–210.CrossRefGoogle Scholar
  16. Ouellette, P., & Vierstraete, V. (2004). Technological change and efficiency in the presence of quasi-fixed inputs: A DEA application to the hospital sector. European Journal of Operational Research, 154, 755–763.CrossRefGoogle Scholar
  17. Yang, Y., Ma, B., & Koike, M. (2000). Efficiency-measuring DEA model for production system with k independent subsystems. Journal of the Operations Research Society of Japan, 43, 343–354.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Nanyang Business SchoolNanyang Technological UniversitySingaporeSingapore

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