Selecting Inputs and Outputs in Data Envelopment Analysis by Designing Statistical Experiments

Data envelopment analysis (DEA) is a data oriented, non-parametric method to evaluate relative efficiency based on pre-selected inputs and outputs. In some cases, the performance model is not well defined, so it is critical to select the appropriate inputs and outputs by other means. When we have many potential variables for evaluation, it is difficult to select inputs and outputs from a large number of possible combinations. We propose an input output selection method that uses diagonal layout experiments, which is a statistical approach to find an optimal combination. We demonstrate the proposed method using financial statement data from NIKKEI 500 index.