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Identifying the efficiency status in network DEA

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  • Fukuyama, Hirofumi
  • Mirdehghan, S.M.

Abstract

Data envelopment analysis (DEA) is commonly employed to evaluate the efficiency performance of a decision making unit (DMU) that transforms exogenous inputs into final outputs. In such a black-box DEA approach, details of an internal production process of the DMU are typically ignored and hence the locations of inefficiency are not adequately provided. In view of this, DEA researchers have recently developed various network approaches by looking into the black box, where the inputs that enter the box and the outputs that come out of it are only considered. However, most of these network approaches evaluate divisional efficiency by using an optimal solution of their respective optimization problem. If such an optimal solution is used in the case when there are multiple optima, then managerial guidance based on this solution alone may be inappropriate because more appropriate targets from the viewpoint of management may be ignored. Taking this fact into account, therefore, we propose a network approach for identifying the efficiency status of each DMU and its divisions. This approach provides a practical computational procedure.

Suggested Citation

  • Fukuyama, Hirofumi & Mirdehghan, S.M., 2012. "Identifying the efficiency status in network DEA," European Journal of Operational Research, Elsevier, vol. 220(1), pages 85-92.
  • Handle: RePEc:eee:ejores:v:220:y:2012:i:1:p:85-92
    DOI: 10.1016/j.ejor.2012.01.024
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    References listed on IDEAS

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    Cited by:

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    3. Tatiana Bencova & Andrea Bohacikova, 2022. "DEA in Performance Measurement of Two-Stage Processes: Comparative Overview of the Literature," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 111-129.
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    6. Azadi, Majid & Shabani, Amir & Khodakarami, Mohsen & Farzipoor Saen, Reza, 2015. "Reprint of “Planning in feasible region by two-stage target-setting DEA methods: An application in green supply chain management of public transportation service providers”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 74(C), pages 22-36.
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    10. Antonio Peyrache & Maria C. A. Silva, 2022. "Efficiency and Productivity Analysis from a System Perspective: Historical Overview," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 173-230, Springer.
    11. Iftikhar, Yaser & Wang, Zhaohua & Zhang, Bin & Wang, Bo, 2018. "Energy and CO2 emissions efficiency of major economies: A network DEA approach," Energy, Elsevier, vol. 147(C), pages 197-207.
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    15. Victor John M. Cantor & Kim Leng Poh, 2020. "Efficiency measurement for general network systems: a slacks-based measure model," Journal of Productivity Analysis, Springer, vol. 54(1), pages 43-57, August.
    16. Gulati, Rachita & Charles, Vincent & Hassan, M. Kabir & Kumar, Sunil, 2023. "COVID-19 crisis and the efficiency of Indian banks: Have they weathered the storm?," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    17. Huaqing Wu & Jingyu Yang & Wensheng Wu & Ya Chen, 2023. "Interest rate liberalization and bank efficiency: A DEA analysis of Chinese commercial banks," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(2), pages 467-498, June.
    18. Antonio Peyrache & Maria C. A. Silva, 2019. "The Inefficiency of Production Systems and its decomposition," CEPA Working Papers Series WP052019, School of Economics, University of Queensland, Australia.
    19. Hang Yin & Qing He & Tao Guo & Jianxin Zhu & Botao Yu, 2014. "Measurement Method and Empirical Research on the Sustainable Development Capability of a Regional Industrial System Based on Ecological Niche Theory in China," Sustainability, MDPI, vol. 6(12), pages 1-25, November.
    20. 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.
    21. Kao, Han-Ying & Wu, Dong-Jyun & Huang, Chia-Hui, 2017. "Evaluation of cloud service industry with dynamic and network DEA models," Applied Mathematics and Computation, Elsevier, vol. 315(C), pages 188-202.
    22. Hirofumi Fukuyama & Atsuo Hashimoto & Kaoru Tone & William L. Weber, 2018. "Does human capital or physical capital constrain output in Japanese prefectures?," Empirical Economics, Springer, vol. 54(2), pages 379-393, March.
    23. Khoveyni, Mohammad & Fukuyama, Hirofumi & Eslami, Robabeh & Yang, Guo-liang, 2019. "Variations effect of intermediate products on the second stage in two-stage processes," Omega, Elsevier, vol. 85(C), pages 35-48.
    24. Azadi, Majid & Shabani, Amir & Khodakarami, Mohsen & Farzipoor Saen, Reza, 2014. "Planning in feasible region by two-stage target-setting DEA methods: An application in green supply chain management of public transportation service providers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 324-338.
    25. Villa, G. & Lozano, S., 2016. "Assessing the scoring efficiency of a football match," European Journal of Operational Research, Elsevier, vol. 255(2), pages 559-569.

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