Random Inputs and Outputs in DEA Models

Data Envelopment Analysis (DEA) is a method for evaluation of relative efficiency of production units described by multiple inputs and outputs. Standard DEA models are based on deterministic inputs and outputs. The paper formulates basic DEA models supposing that inputs and outputs are continuous random variables. Under this assumption the efficiency scores of production units are random variables as well. Several approaches for description of random efficiency scores were developed by various researches. They are mostly based on a formulation of LP optimisation problems. The paper summarises some of these approaches and discusses their advantages and disadvantages. Except this the aim of the paper is to describe the properties of random efficiency scores by means of Monte Carlo experiments. The experiments are realised within the MS Excel environment together with LINGO optimisation solver and Crystal Ball add-in application for Monte Carlo analysis in spreadsheets. The computations were realised with a real set of bank branches of one of the Czech commercial banks.