Performance evaluation of production companies using data envelopment analysis and Monte Carlo simulation: a case study

Data envelopment analysis based on linear programming model, is a scientific approach, which evaluates the efficiency of organisations and units which use multiple inputs in order to produce multiple outputs. Data envelopment analysis models presented by Charnes, Cooper and Rhodes have intentional deficiencies, one of which is relying on data from periods of time that decision making units have passed. Therefore the results are based on past data. This is especially significant when the goal is to evaluate the current efficiency of units and forecast their future performance. On the other hand, classic DEA modes, such as BCC and CCR are based on the assumption that the precise and definite numerical value of all inputs and outputs are in hand, while in real world this is not always the case; specifically when the decision maker intends to evaluate the performance in long period of time in which values are definitively imprecise and inputs and outputs come as time series. The purpose of this article is to present an approach composing of DEA and Monte Carlo simulation which enables the decision maker to find the most efficient organisation in a given period of time, considering imprecise time series of data and also helps with forecasting and estimating the efficiency of companies in the future for a safe investment.