A two-stage DEA approach to measure the performance of multi-activity bank branches

Data envelopment analysis (DEA) is a nonparametric method for measuring the efficiency of decision-making units (DMUs) with multiple inputs and outputs. This research used the original DEA model and extended it to solve the DEA efficiency measurement problem, specifically for unseparated shared inputs. The consideration of this context aims to establish a new DEA approach to explore bank branch performance in different activities based on the optimal usage of unseparated shared inputs. In this study, in the first stage, the efficiency score is calculated from several activities using graph efficiency, and then, a maximum efficiency score pertaining to each DMU is applied to propose a new model. In the second stage, the efficiency score, which is calculated by the new approach on unseparated shared inputs, is defined as a new constraint based on shared inputs on the CCR model. This approach is implemented on the real data of 25 branches of a private bank in Iran. In fact, the efficiency of each branch is calculated, and enhancement guidelines are presented considering the three activities of production, electronic banking, and intermediation. Presenting one real efficiency score for each DMU, instead of the traditional efficiency score, leads to more robust decisions based on a more transparent performance evaluation in bank branches.