Non-discretionary Factors in Data Envelopment Analysis: Review and Extension

Data Envelopment Analysis (DEA) technique uses linear programming to evaluate the relative efficiency of a homogeneous set of Decision Making Units (DMUs) in their use of multiple inputs to produce multiple outputs. The standard DEA models do not take into account nondiscretionary inputs and outputs and ignore the possibility that efficiency may be correlated with the nondiscretionary factors. However, one key issue in performance measurement problems is how to treat nondiscretionary factors, which influence the performance of DMUs and are, at the same time, out of the control of the management. In this paper, a new model for measuring efficiency is defined such that nondiscretionary factors are taken into account by the decision maker. The main contributions of this paper are fourfold: (1) we review the existing approaches for measuring efficiency scores to control nondiscretionary factors in production; (2) we provide a discussion of strengths and weaknesses and highlighting potential limitations of the existing nondiscretionary DEA models; (3) we propose a new approach based on relative importance of nondiscretionary inputs that overcomes existing weaknesses; (4) we use a numerical example to demonstrate the feasibility and richness of the obtained solutions.