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Performance measurement for network DEA with undesirable factors

Author

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  • Zhongsheng Hua
  • Yiwen Bian

Abstract

Traditional Data Envelopment Analysis (DEA) models take Decision-Making Unit (DMU) as a 'black box' without considering the inputs/outputs of its intermediate production processes. To provide efficiency enhancing information regarding the sources of DMUs' inefficiencies, researchers investigate network DEA models. This paper proposes a network DEA model in the presence of undesirable factors. In the network DEA model, a DMU is composed of a set of interdependent sub-DMUs, that is, input of a sub-DMU may be an undesirable output of another sub-DMU. We develop a method of estimating efficiency of such DMU, and analyses efficiency relationship between a DMU and its sub-DMUs. Our model provides a way of improving performance of a DMU through identifying its inefficient sub-DMUs. Numerical examples are used to show our results.

Suggested Citation

  • Zhongsheng Hua & Yiwen Bian, 2008. "Performance measurement for network DEA with undesirable factors," International Journal of Management and Decision Making, Inderscience Enterprises Ltd, vol. 9(2), pages 141-153.
    • Handle: RePEc:ids:ijmdma:v:9:y:2008:i:2:p:141-153
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    Citations

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    Cited by:

    1. Wei-Hsin Kong & Tsu-Tan Fu & Ming-Miin Yu, 2017. "Evaluating Taiwanese Bank Efficiency Using the Two-Stage Range DEA Model," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1043-1068, July.
    2. Matthews, Kent, 2013. "Risk management and managerial efficiency in Chinese banks: A network DEA framework," Omega, Elsevier, vol. 41(2), pages 207-215.
    3. Tsung-Sheng Chang & Kaoru Tone & Quanling Wei, 2014. "Ownership-specified network DEA models," Annals of Operations Research, Springer, vol. 214(1), pages 73-98, March.
    4. Hampf, Benjamin, 2011. "Separating environmental efficiency into production and abatement efficiency: A nonparametric model with application to U.S. power plants," Darmstadt Discussion Papers in Economics 204, Darmstadt University of Technology, Department of Law and Economics.
    5. Halkos, George & Petrou, Kleoniki Natalia, 2019. "Treating undesirable outputs in DEA: A critical review," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 97-104.
    6. Lorenzo Castelli & Raffaele Pesenti & Walter Ukovich, 2010. "A classification of DEA models when the internal structure of the Decision Making Units is considered," Annals of Operations Research, Springer, vol. 173(1), pages 207-235, January.
    7. Avkiran, Necmi K., 2009. "Opening the black box of efficiency analysis: An illustration with UAE banks," Omega, Elsevier, vol. 37(4), pages 930-941, August.
    8. Sun, Jiasen & Li, Guo & Wang, Zhaohua, 2018. "Optimizing China’s energy consumption structure under energy and carbon constraints," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 57-72.
    9. Zohreh Sadeghi & Reza Farzipoor Saen & Mahdi Moradzadehfard, 2022. "RETRACTED ARTICLE: Developing a network data envelopment analysis model for appraising sustainable supply chains: a sustainability accounting approach," Operations Management Research, Springer, vol. 15(3), pages 809-824, December.
    10. Zhao, Y. & Triantis, K. & Murray-Tuite, P. & Edara, P., 2011. "Performance measurement of a transportation network with a downtown space reservation system: A network-DEA approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1140-1159.
    11. Liu, Wenbin & Zhou, Zhongbao & Ma, Chaoqun & Liu, Debin & Shen, Wanfang, 2015. "Two-stage DEA models with undesirable input-intermediate-outputs," Omega, Elsevier, vol. 56(C), pages 74-87.
    12. Q L Wei & T-S Chang, 2011. "Optimal system design series-network DEA models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1109-1119, June.
    13. Dorota Kuchta, 2023. "Project implementation scenario selection for sustainable project and product lifecycle management. Application of network data envelopment analysis," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(4), pages 133-154.
    14. Tavassoli, Mohammad & Faramarzi, Gholam Reza & Farzipoor Saen, Reza, 2014. "Efficiency and effectiveness in airline performance using a SBM-NDEA model in the presence of shared input," Journal of Air Transport Management, Elsevier, vol. 34(C), pages 146-153.
    15. Hampf, Benjamin, 2011. "Separating Environmental Efficiency into Production and Abatement Efficiency – A Nonparametric Model with Application to U.S. Power Plants," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 53901, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    16. Benjamin Hampf, 2014. "Separating environmental efficiency into production and abatement efficiency: a nonparametric model with application to US power plants," Journal of Productivity Analysis, Springer, vol. 41(3), pages 457-473, June.
    17. Lozano, Sebastián, 2016. "Slacks-based inefficiency approach for general networks with bad outputs: An application to the banking sector," Omega, Elsevier, vol. 60(C), pages 73-84.
    18. Halkos, George & Petrou, Kleoniki Natalia, 2018. "A critical review of the main methods to treat undesirable outputs in DEA," MPRA Paper 90374, University Library of Munich, Germany.
    19. Mallikarjun, Sreekanth & Lewis, Herbert F. & Sexton, Thomas R., 2014. "Operational performance of U.S. public rail transit and implications for public policy," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 74-88.

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