A cross-efficiency profiling for increasing discrimination in Data Envelopment Analysis

Abstract

Data Envelopment Analysis (DEA) cannot provide adequate discrimination among efficient decision making units (DMUs). To discriminate these efficient DMUs is an interesting research subject. The purpose of this paper is to present a Cross-Efficiency Profiling (CEP) model which can be used to improve discrimination power of DEA and conduct a methodological comparison of CEP and the other developed methods without a priori information. CEP retains the original spirit of DEA in trying to extract as much information as possible from the data without requiring pre-selected weights on inputs and outputs. We propose that inputs which are not substitutes for each other be assessed separately and only with respect to outputs which consume them or to which they are otherwise related. In this way input-specific ratings based on the concept of cross-efficiency measure are derived giving a profile for each DMU. We will demonstrate that CEP is more discriminating through an example taken from Baker and Talluri [Computer and Industrial Engineering, 32(1), 101-108 (1997)].