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Performance measurement and classification data in DEA: Input-oriented model

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  • Cook, Wade D.
  • Bala, Kamel

Abstract

This paper presents an improved efficiency measurement tool by modifying the existing data envelopment analysis methodology to permit the incorporation of expert knowledge. A previous paper examined the inclusion of such knowledge within the additive model. This information appeared in the form of a binary classification of a subset of the decision making units under study (e.g. good versus poor performers). In the current paper, we extend this logic to the input-oriented radial projection model. We demonstrate that the inclusion of this and other forms of expert judgment can improve the performance of the DEA tool in the sense that the efficiency scores are more in line with expert/management beliefs.

Suggested Citation

  • Cook, Wade D. & Bala, Kamel, 2007. "Performance measurement and classification data in DEA: Input-oriented model," Omega, Elsevier, vol. 35(1), pages 39-52, February.
    • Handle: RePEc:eee:jomega:v:35:y:2007:i:1:p:39-52
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Bala, Kamel & Cook, Wade D., 2003. "Performance measurement with classification information: an enhanced additive DEA model," Omega, Elsevier, vol. 31(6), pages 439-450, December.
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    Cited by:

    1. Kayakutlu, Gulgun & Buyukozkan, Gulcin, 2011. "Assessing performance factors for a 3PL in a value chain," International Journal of Production Economics, Elsevier, vol. 131(2), pages 441-452, June.
    2. Stewart, Theodor J., 2010. "Goal directed benchmarking for organizational efficiency," Omega, Elsevier, vol. 38(6), pages 534-539, December.
    3. Edirisinghe, N.C.P. & Zhang, X., 2010. "Input/output selection in DEA under expert information, with application to financial markets," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1669-1678, December.
    4. Ehrgott, Matthias & Tind, Jørgen, 2009. "Column generation with free replicability in DEA," Omega, Elsevier, vol. 37(5), pages 943-950, October.
    5. Po, Rung-Wei & Guh, Yuh-Yuan & Yang, Miin-Shen, 2009. "A new clustering approach using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 199(1), pages 276-284, November.
    6. Samoilenko, Sergey & Osei-Bryson, Kweku-Muata, 2013. "Using Data Envelopment Analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system," Omega, Elsevier, vol. 41(1), pages 131-142.
    7. Yu, Ming-Miin & Lin, Erwin T.J., 2008. "Efficiency and effectiveness in railway performance using a multi-activity network DEA model," Omega, Elsevier, vol. 36(6), pages 1005-1017, December.
    8. Nattanin Ueasin & Anupong Wongchai & Sakkarin Nonthapot, 2015. "Performance Assessment and Optimization of Biomass Steam Turbine Power Plants by Data Envelopment Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 5(3), pages 668-672.
    9. Demirbag, Mehmet & Tatoglu, Ekrem & Glaister, Keith W. & Zaim, Selim, 2010. "Measuring strategic decision making efficiency in different country contexts: A comparison of British and Turkish firms," Omega, Elsevier, vol. 38(1-2), pages 95-104, February.

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