Chapter 2 An Introduction to Data Envelopment Analysis 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 meth- odology 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 technol- ogies and programs. 2.1 Symbols and Notations In the DEA literature, the terms production units, firms, and DMUs are all used interchangeably. A short definition of a production unit or firm or DMU is that of an entity responsible for consuming inputs to generate outputs. Suppose there are j DMUs to be evaluated. An important problem before choosing a DEA model is the choice of inputs and outputs. If DEA is used as a benchmarking tool, then the inputs are “the best—the better” types of measures and the outputs are “the more— the better” types of measures. However, if undesirable factors are present in the production process, then this does not hold, and researchers must be careful in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 A. Amirteimoori et al., Stochastic Benchmarking, International Series in Operations Research & Management Science 317, https://doi.org/10.1007/978-3-030-89869-4_2 13