Data Envelopment Analysis in the Presence of Partial Input-to-Output Impacts

Abstract

Data envelopment analysis (DEA) is a methodology used to evaluate the relative efficiencies of peer decision-making units (DMUs) in multiple input, multiple output situations. In the original formulation, and in the vast literature that followed, the assumption was that all members of the input bundle affected the output bundle. However, many potential applications of efficiency measurement exist wherein some inputs do not influence certain outputs. For example, in a manufacturing setting from which multiple products (outputs) emerge, resources (e.g., packaging labor) will not affect products that do not pass through that department. For this paper, extension of the conventional DEA methodology allows for the measurement of technical efficiency in situations where only partial input-to-output impacts are evident. Evaluating the efficiencies of a set of steel fabrication plants using the methodology was the focus of the research.