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Evaluating productive performance: A new approach based on the product-mix problem consistent with Data Envelopment Analysis

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  • Aparicio, Juan
  • Pastor, Jesús T.
  • Vidal, Fernando
  • Zofío, José L.

Abstract

We propose a new approach to estimate technical coefficients from a set of Decision Making Units (DMUs) under the assumption that their production plans are set by process engineers through Linear Programming (LP) techniques. The idea behind this approach is that most manufacturing and agricultural firms routinely resort to LP-based modeling in their decision making processes in order to plan output production and, therefore, this particularity should be taken into account when estimating their technical efficiency. A usual model of LP for these sectors is the so-called product-mix problem, which we relate to a standard Data Envelopment Analysis (DEA) model in terms of the Directional Distance Function. In this paper, we finally show how to estimate the technical coefficients of a sample of Andalusian farms in Spain and how this information can be seen as a complement to the usual by-products associated with estimating technical efficiency by DEA.

Suggested Citation

  • Aparicio, Juan & Pastor, Jesús T. & Vidal, Fernando & Zofío, José L., 2017. "Evaluating productive performance: A new approach based on the product-mix problem consistent with Data Envelopment Analysis," Omega, Elsevier, vol. 67(C), pages 134-144.
    • Handle: RePEc:eee:jomega:v:67:y:2017:i:c:p:134-144
    DOI: 10.1016/j.omega.2016.04.007
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    Cited by:

    1. Aparicio, Juan & Zofío, José L., 2023. "Decomposing profit change: Konüs, Bennet and Luenberger indicators," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    2. Fei-Fei Ye & Ying-Ming Wang, 2019. "The effects of two types of environmental regulations on economic efficiency: An analysis of Chinese industries," Energy & Environment, , vol. 30(5), pages 898-929, August.
    3. Joe Zhu, 2022. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 309(2), pages 761-783, February.
    4. Valero-Carreras, Daniel & Aparicio, Juan & Guerrero, Nadia M., 2021. "Support vector frontiers: A new approach for estimating production functions through support vector machines," Omega, Elsevier, vol. 104(C).

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