An exploration of the concept of constrained improvement in data envelopment analysis

Abstract

Constrained improvement refers to regulating rivalry between companies in a particular industry by defining a framework or an evaluation mechanism. Such a mechanism results in a more equitable and healthy competitive environment. The primary motivation is that the best-performing players in a particular industry improve their performance such that the rest of the contenders remain competitive. This study investigates the concept of constrained improvement from a frontier analysis perspective, develops a systematic implementation framework, and explores a novel application of sensitivity analysis in Data Envelopment Analysis (DEA). Original programming approaches are developed to discover the stability region considering a variable returns to scale. The objective is to determine the extent to which the input and output of a decision-making unit (DMU) can be improved or worsened before the configuration of the efficient frontier changes. Furthermore, the permissible change radius for the decision-making unit is identified, considering all possible change directions. The applicability of the approach is demonstrated using numerical examples.