An Introduction to Data Envelopment Analysis

Alireza Amirteimoori, Biresh Sahoo (), Vincent Charles and Saber Mehdizadeh ()
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Alireza Amirteimoori: Rasht Branch, Islamic Azad University
Vincent Charles: Pontifical Catholic University of Peru
Saber Mehdizadeh: Rasht Branch, Islamic Azad University

Chapter Chapter 2 in Stochastic Benchmarking, 2022, pp 13-29 from Springer

Abstract: Abstract Following the seminal work of Farrell (1957), Charnes et al. (1978) introduced DEA as a deterministic and nonparametric efficiency evaluation tool. DEA is a linear programming-based technique that has been widely accepted as a competing methodology to evaluate the relative efficiency of entities or decision-making units, DMUs (Charles et al., 2016, 2018; Tsolas et al., 2020). DEA is a data-oriented technique (Zhu, 2020) that is used to construct an empirical production frontier to measure efficiency. Note that the original DEA program of Charnes et al. (1978) is based on the CRS specification of technology and is used to measure the technical and scale efficiency of DMUs. However, Banker et al. (1984) extended this program to the case of VRS to estimate purely technical efficiency. Over the past three decades, DEA has been widely used to evaluate the relative efficiency of production firms, the nature of the returns-to-scale, and the productivity changes. The DEA literature has seen a wide variety of applications across a plethora of domains, having become a powerful management science tool (Charles et al., 2018). In this chapter, we briefly review the fundamental concepts in DEA, along with the basic technologies and programs.

Date: 2022
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DOI: 10.1007/978-3-030-89869-4_2

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