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Decomposition of slacks-based efficiency measures in network data envelopment analysis

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  • Kao, Chiang

Abstract

One objective of the efficiency measurement of multi-division systems is to find the least efficient divisions such that making improvements to them will increase the efficiency of the system most effectively. To accomplish this task, it is desirable to find the relationship between the system and division efficiencies. This issue has been addressed for the radial efficiency measures. This paper explores the relationship for a non-radial efficiency measure known as the slacks-based measure. The idea is to transform the network system into an equivalent one that is composed of a series of subsystems with parallel structures. Based on the property that the system efficiency is the product of the division efficiencies adjusted by the linkage efficiencies for series systems and is a linear combination of the division efficiencies for parallel systems, the system efficiency can be decomposed into a function of the division and linkage efficiencies. The decomposition of the slacks-based efficiency measure works not only for the technology of constant returns to scale, but also for the case of variable returns to scale. In addition, the proposed model produces suitable efficiency scores for weakly efficient units and for those using weakly efficient units as the target to calculate efficiencies, which makes it possible to obtain reliable rankings for the assessed units.

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  • Kao, Chiang, 2020. "Decomposition of slacks-based efficiency measures in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 283(2), pages 588-600.
    • Handle: RePEc:eee:ejores:v:283:y:2020:i:2:p:588-600
    DOI: 10.1016/j.ejor.2019.11.021
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    Cited by:

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    2. Antonio Peyrache & Maria C. A. Silva, 2022. "A Comment on Decomposition of Efficiency in Network Production Models," CEPA Working Papers Series WP072022, School of Economics, University of Queensland, Australia.
    3. Alireza Moradi & Saber Saati & Mehrzad Navabakhsh, 2023. "Genetic algorithms for optimizing two-stage DEA by considering unequal intermediate weights," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1202-1217, September.
    4. Peyrache, Antonio & Silva, Maria C.A., 2024. "The decomposition of efficiency in parallel network production models," Omega, Elsevier, vol. 127(C).
    5. Alizadeh, Reza & Gharizadeh Beiragh, Ramin & Soltanisehat, Leili & Soltanzadeh, Elham & Lund, Peter D., 2020. "Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach," Energy Economics, Elsevier, vol. 91(C).
    6. 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.
    7. Lim, Dong-Joon & Kim, Moon-Su, 2022. "Measuring dynamic efficiency with variable time lag effects," Omega, Elsevier, vol. 108(C).
    8. Kai He & Nan Zhu & Wu Jiang & Chuanjin Zhu, 2022. "Efficiency Evaluation of Chinese Provincial Industrial System Based on Network DEA Method," Sustainability, MDPI, vol. 14(9), pages 1-24, April.

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