Developing a stochastic DEA model for considering non-discretionary inputs

Abstract

Formal statistical inference on efficiency measures is not possible. Stochastic DEA can deal effectively with noise in the non-parametric measurement of efficiency. In any realistic situation, however, there may be exogenously fixed or non-discretionary inputs or outputs that are beyond the control of a DMU's management. The objective of this paper is to present a methodology for treating non-discretionary variables in stochastic formulation. Based on the proposed method, an effective performance measurement tool is developed to provide a basis for understanding the efficiency in stochastic situations. A numerical example is presented. In short, the main contributions of this work are as follows: an stochastic DEA model is extended to encompass non-discretionary variables and stochastic data, thus a typical model for efficiency analysis is developed as an effective performance measurement tool that is the contribution of the paper.