A slacks-based measure model for calculating cross efficiency in data envelopment analysis

Chiang Kao, Shiang Tai Liu

Research output: Contribution to journalArticle

Abstract

Cross efficiency is a concept for solving the problem of incomparability among the efficiencies of a set of decision making units (DMUs) calculated from different weights in data envelopment analysis, and is helpful for ranking. Conventional cross efficiency is a radial measure, and the radial measure has some weaknesses. First, it is not able to provide appropriate efficiency scores for weakly efficient DMUs. Second, the efficiencies measured from the input and output sides under variable returns to scale are different which may cause difficulties in subsequent analyses. Third, negative values may appear when the efficiency is measured from the input side. To solve these problems, this paper proposes a slacks-based measure to calculate efficiency. The basic idea is the same as that of the radial measure using the frontier corresponding to a DMU to measure the efficiency of all DMUs, except that the efficiency measure is slacks-based instead of radial. Due to the nature of the slacks-based efficiency measure, the problems caused by weak efficiency and difference in input and output radial measures do not exist. More importantly, negative efficiencies are not produced. The proposed method is applied to a real case involving the selection of the most efficient robot to use for production. The results help identify the top-ranked robot.

Original languageEnglish
Article number102192
JournalOmega (United Kingdom)
DOIs
Publication statusAccepted/In press - 2020 Jan 1

Fingerprint

Slacks-based measure
Data envelopment analysis
Decision making units
Robot
Slacks
Efficiency measures
Variable returns to scale
Ranking

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Information Systems and Management

Cite this

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A slacks-based measure model for calculating cross efficiency in data envelopment analysis. / Kao, Chiang; Liu, Shiang Tai.

In: Omega (United Kingdom), 01.01.2020.

Research output: Contribution to journalArticle

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