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A new methodology to measure efficiencies of inputs (outputs) of decision making units in Data Envelopment Analysis with application to agriculture

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  • Mosbah, Ezzeddine
  • Zaibet, Lokman
  • Dharmapala, P. Sunil

Abstract

This paper aims at developing a new methodology to measure and decompose global DMU efficiency into efficiency of inputs (or outputs). The basic idea rests on the fact that global DMU's efficiency score might be misleading when managers proceed to reallocate their inputs or redefine their outputs. Literature provides a basic measure for global DMU's efficiency score. A revised model was developed for measuring efficiencies of global DMUs and their inputs (or outputs) efficiency components, based on a hypothesis of virtual DMUs. The present paper suggests a method for measuring global DMU efficiency simultaneously with its efficiencies of inputs components, that we call Input decomposition DEA model (ID-DEA), and its efficiencies of outputs components, that we call output decomposition DEA model (OD-DEA). These twin models differ from Supper efficiency model (SE-DEA) and Common Set Weights model (CSW-DEA). The twin models (ID-DEA, OD-DEA) were applied to agricultural farms, and the results gave different efficiency scores of inputs (or outputs), and at the same time, global DMU's efficiency score was given by the Charnes, Cooper and Rhodes (Charnes et al., 1978) [1], CCR78 model. The rationale of our new hypothesis and model is the fact that managers don't have the same information level about all inputs and outputs that constraint them to manage resources by the (global) efficiency scores. Then each input/output has a different reality depending on the manager's decision in relationship to information available at the time of decision. This paper decomposes global DMU's efficiency into input (or output) components' efficiencies. Each component will have its score instead of a global DMU score. These findings would improve management decision making about reallocating inputs and redefining outputs. Concerning policy implications of the DEA twin models, they help policy makers to assess, ameliorate and reorient their strategies and execute programs towards enhancing the best practices and minimising losses.

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  • Mosbah, Ezzeddine & Zaibet, Lokman & Dharmapala, P. Sunil, 2020. "A new methodology to measure efficiencies of inputs (outputs) of decision making units in Data Envelopment Analysis with application to agriculture," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    • Handle: RePEc:eee:soceps:v:72:y:2020:i:c:s0038012119303891
    DOI: 10.1016/j.seps.2020.100857
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    2. Tao, Yongming & Muneeb, Farhan Muhammad & Wanke, Peter Fernandes & Tan, Yong & Yazdi, Amir Karbassi, 2024. "Revisiting the critical success factors of entrepreneurship to promote Chinese agriculture systems: A multi-criteria decision-making approach," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    3. Zhiwei Pan & Decai Tang & Haojia Kong & Junxia He, 2022. "An Analysis of Agricultural Production Efficiency of Yangtze River Economic Belt Based on a Three-Stage DEA Malmquist Model," IJERPH, MDPI, vol. 19(2), pages 1-15, January.
    4. Leonidas Sotirios Kyrgiakos & Georgios Kleftodimos & George Vlontzos & Panos M. Pardalos, 2023. "A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability," Operational Research, Springer, vol. 23(1), pages 1-38, March.
    5. Hepei Zhang & Zhangbao Zhong, 2022. "How Does Environmental Regulation Affect the Green Growth of China’s Citrus Industry? The Mediating Role of Technological Innovation," IJERPH, MDPI, vol. 19(20), pages 1-19, October.
    6. Mostafa Mardani Najafabadi & Hanieh Kazmi & Somayeh Shirzadi Laskookalayeh & Abas Abdeshahi, 2023. "Investigating the ability of fuzzy and robust DEA models to apply uncertainty conditions: an application for date palm producers," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 776-801, June.

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