Performance Benchmarking Using Interactive Data Envelopment Analysis

Traditional Data Envelope Analysis (DEA) neglects uncertainty for the input-output variables by treating the observations as if they were the true input-output variables to select reference units for efficiency estimation and performance benchmarking. In stochastic environments, the traditional framework may include stochastically dominated reference units and exclude stochastically undominated ones. To incorporate uncertainty for the input-output variables in DEA, we propose a mean-variance framework derived from the theory of stochastic dominance. From that framework an extension to the traditional model is derived that prevents the selection of stochastically dominated reference units.

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