A multi-criteria ratio-based approach for two-stage data envelopment analysis

Abstract Data Envelopment Analysis (DEA) is a well-known technique for assessing efficiency levels of decision-making units (DMUs). Very often, available data may be expressed as ratios and, in such cases, traditional DEA models cannot be applied as long as biased efficiency results are produced, yielding the issues of efficiency underestimation and pseudo-inefficiency. In this paper, a novel two-stage MCDEA-R model to handle ratio data is developed observing three distinct assumptions – black-box, free-link, and fixed-link – offering a multi-criteria decision making (MCDM) perspective to the efficiency assessment problem in productive networks. While the proposed models are tested by evaluating the efficiency levels of a set of 30 bank branches in Iran, their distinctive features are highlighted in terms of previous literature to model ratio data under network structures. Precisely, there were not only gains in terms of mitigating pseudo-inefficiency and lack of discrimination power of weights issues, but there were also actual gains in terms of efficiency reliability as measured by information entropy.