Two alternative approaches for selecting performance measures in data envelopment analysis

• We deal with the rule of thumb in DEA.
• Two selecting models are proposed to select performance measures.
• The multiplier model leads to the maximum efficiency scores.
• The envelopment model implies the maximum discrimination between efficient units.
• The applicability of the new approach is shown with a real data set.

Data envelopment analysis seeks a frontier to envelop all data with data acting in a critical role in the process and in such a way measures the relative efficiency of each decision making unit in comparison with other units. There is a statistical and empirical rule that if the number of performance measures is high in comparison with the number of units, then a large percentage of the units will be determined as efficient, which is obviously a questionable result. It also implies that the selection of performance measures is very crucial for successful applications. In this paper, we extend both multiplier and envelopment forms of data envelopment analysis models and propose two alternative approaches for selecting performance measures under variable returns to scale. The multiplier form of selecting model leads to the maximum efficiency scores and the maximum discrimination between efficient units is achieved by applying the envelopment form. Also individual unit and aggregate models are formulated separately to develop the idea of selective measures. Finally, in order to illustrate the potential of the proposed approaches a case study using a data from a banking industry in the Czech Republic is utilized.