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Measurement and decomposition of the Malmquist productivity index for parallel production systems

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  • Kao, Chiang

Abstract

The conventional Malmquist productivity index (MPI), which ignores the internal structure of a production system when measuring changes in performance between two periods, may produce misleading results. This paper thus takes the operations of the component processes into account in investigating the MPI of parallel production systems. A relational data envelopment analysis (DEA) model is developed to measure the biennial MPIs of the system and internal processes at the same time, and it is shown that the former is a linear combination of the latter. This decomposition helps identify the processes that cause the decline in performance of the system. An example of 39 branches of a commercial bank, with deposits, sales, and services as the three major functions operating in parallel, is used to illustrate this approach.

Suggested Citation

  • Kao, Chiang, 2017. "Measurement and decomposition of the Malmquist productivity index for parallel production systems," Omega, Elsevier, vol. 67(C), pages 54-59.
  • Handle: RePEc:eee:jomega:v:67:y:2017:i:c:p:54-59
    DOI: 10.1016/j.omega.2016.04.001
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    6. Güner, Samet & Cebeci, Halil İbrahim & Antunes, Jorge Junio Moreira & Wanke, Peter F., 2021. "Sustainable efficiency drivers in Eurasian airports: Fuzzy NDEA approach based on Shannon's entropy," Journal of Air Transport Management, Elsevier, vol. 92(C).
    7. Madjid Tavana & Kaveh Khalili-Damghani & Francisco J. Santos Arteaga & Arousha Hashemi, 2020. "A Malmquist productivity index for network production systems in the energy sector," Annals of Operations Research, Springer, vol. 284(1), pages 415-445, January.
    8. Chia-Chin Chang & Chia-Syuan Chang, 2023. "Influences of Talent Cultivation and Utilization on the National Human Resource Development System Performance: An International Study Using a Two-Stage Data Envelopment Analysis Model," Mathematics, MDPI, vol. 11(13), pages 1-18, June.
    9. Adel Hatami-Marbini & Aliasghar Arabmaldar & John Otu Asu, 2022. "Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1213-1254, December.
    10. Wang, H. & Zhou, P. & Xie, Bai-Chen & Zhang, N., 2019. "Assessing drivers of CO2 emissions in China's electricity sector: A metafrontier production-theoretical decomposition analysis," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1096-1107.
    11. Khoshroo, Alireza & Izadikhah, Mohammad & Emrouznejad, Ali, 2022. "Total factor energy productivity considering undesirable pollutant outputs: A new double frontier based malmquist productivity index," Energy, Elsevier, vol. 258(C).
    12. Fenfen Li & Bo Dai & Qifan Wu, 2021. "Dynamic Green Growth Assessment of China’s Industrial System with an Improved SBM Model and Global Malmquist Index," Mathematics, MDPI, vol. 9(20), pages 1-26, October.

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