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Incorporating Multiprocess Performance Standards into the DEA Framework


  • Wade D. Cook

    () (Department of Management Science, Schulich School of Business, York University, Toronto, Ontario, Canada M3J 1P3)

  • Joe Zhu

    () (Department of Management, Worcester Polytechnic Institute, Worcester, Massachusetts 01609)


Data envelopment analysis (DEA) is a mathematical approach to measuring the relative efficiency of peer decision-making units (DMUs). It is particularly useful when no a priori information is available on the trade-offs or relationships among various performance measures. A shortcoming of the DEA model, however, is its inability to provide a measure of absolute performance for the DMUs under investigation. Traditionally, in the service sector, this has not been an issue that one could address, because performance standards in that sector have been difficult to establish. However, in those settings where it has become feasible to develop such standards, it is desirable to build these into DEA performance evaluation, thereby enhancing the capability of the tool. While there have been some attempts to incorporate standards into the DEA structure, these approaches have generally been indirect , in the sense that they have focused primarily on restricting the DEA dual multipliers. This paper introduces a new way of building performance standards into the model. Utilizing the conventional DEA framework and a set of activity matrices, a set of standard DMUs can be generated and incorporated directly into the analysis. We show that under normal circumstances, these generated DMUs are efficient relative to the normal ones, and therefore form a type of outer frontier against which regular units can be evaluated. The proposed approach is applied to a sample of 100 branches of a major Canadian bank, where time standards are used to generate a set of standard bank branches.

Suggested Citation

  • Wade D. Cook & Joe Zhu, 2006. "Incorporating Multiprocess Performance Standards into the DEA Framework," Operations Research, INFORMS, vol. 54(4), pages 656-665, August.
  • Handle: RePEc:inm:oropre:v:54:y:2006:i:4:p:656-665

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    References listed on IDEAS

    1. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
    2. Cook, Wade D. & Seiford, Lawrence M. & Zhu, Joe, 2004. "Models for performance benchmarking: measuring the effect of e-business activities on banking performance," Omega, Elsevier, vol. 32(4), pages 313-322, August.
    3. Charnes, A. & Cooper, W. W. & Huang, Z. M. & Sun, D. B., 1990. "Polyhedral Cone-Ratio DEA Models with an illustrative application to large commercial banks," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 73-91.
    4. Chen, Yao & Morita, Hiroshi & Zhu, Joe, 2003. "Multiplier bounds in DEA via strong complementary slackness condition solution," International Journal of Production Economics, Elsevier, vol. 86(1), pages 11-19, October.
    5. Berger, Allen N. & Humphrey, David B., 1997. "Efficiency of financial institutions: International survey and directions for future research," European Journal of Operational Research, Elsevier, vol. 98(2), pages 175-212, April.
    6. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    7. Sherman, H. David & Gold, Franklin, 1985. "Bank branch operating efficiency : Evaluation with Data Envelopment Analysis," Journal of Banking & Finance, Elsevier, vol. 9(2), pages 297-315, June.
    8. Cook, Wade D. & Hababou, Moez, 2001. "Sales performance measurement in bank branches," Omega, Elsevier, vol. 29(4), pages 299-307, August.
    9. Wade Cook & Moez Hababou & Hans Tuenter, 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, Springer, vol. 14(3), pages 209-224, November.
    10. Charnes, A. & Cooper, W. W. & Golany, B. & Seiford, L. & Stutz, J., 1985. "Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 91-107.
    11. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    12. Thompson, Russell G. & Langemeier, Larry N. & Lee, Chih-Tah & Lee, Euntaik & Thrall, Robert M., 1990. "The role of multiplier bounds in efficiency analysis with application to Kansas farming," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 93-108.
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    Cited by:

    1. Wade Cook & Joe Zhu, 2010. "Context-dependent performance standards in DEA," Annals of Operations Research, Springer, vol. 173(1), pages 163-175, January.
    2. Léopold Simar & Valentin Zelenyuk, 2016. "Asymptotic Theory for Aggregate Efficiency," CEPA Working Papers Series WP042016, School of Economics, University of Queensland, Australia.
    3. repec:eee:ejores:v:265:y:2018:i:2:p:748-764 is not listed on IDEAS
    4. repec:eee:energy:v:153:y:2018:i:c:p:338-348 is not listed on IDEAS
    5. repec:spr:annopr:v:250:y:2017:i:1:d:10.1007_s10479-015-2006-2 is not listed on IDEAS


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