Imprecise Data Envelopment Analysis: Concepts, Methods, and Interpretations

Abstract

DEA has proven to be a useful tool for assessing efficiency or productivity of organizations. While DEA assumes exact input and output data, the development of imprecise DEA (IDEA) broadens the scope of applications to efficiency evaluations involving imprecise information which implies various forms of ordinal and bounded data often occurring in practice. The primary purpose of this article is to review what has been developed so far, including the body of concepts and methods that go by the name of IDEA. This review comprises (a) why we look at imprecise data and how to elicit imprecise information, (b) how to calculate the efficiency measures, and (c) how we can interpret the resulting efficiency. Special emphasis will be placed on how to deal with strict inequality types of imprecise data, such as strict orders and strict bounds, rather than weak inequalities. A general approach to these strict imprecise data is presented, in order to arrive at efficiency scores. This approach first constructs a nonlinear program, transform it into a linear programming equivalent, and finally solve it via a two-stage method.