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 ...

In this paper, we present a methodology for evaluating competing organizations in order to identify best practices among those organizations. We focus attention specifically on competitiveness in the context of a set of business schools for the purpose of identifying those that appear to be most efficient relative to their peers. One of the most widely recognized efficiency measurement methodologies is data envelopment analysis (DEA). DEA literature has witnessed the expansion of the original concept to encompass a wide range of theoretical and applied research areas, with one such area being network DEA, with two-stage DEA in particular. This latter concept and its extensions to multi-stage situations have been particularly influential in such settings as supply chain management. In the conventional two-stage serial model, it is assumed that in each stage efficiency will be defined by t...