Efficiency Evaluation of Banks with Many Branches using a Heuristic Framework and Dynamic Data Envelopment Optimization Approach: A Real Case Study

TL;DR

This paper evaluates bank branch efficiency over three years using a dynamic data envelopment analysis approach, compares it with static DEA, and introduces a sensitivity analysis to aid managerial decision-making.

Contribution

It applies a dynamic DEA method to bank branches over multiple years and develops a sensitivity analysis framework for strategic improvements.

Findings

Dynamic DEA provides more accurate efficiency assessments than static DEA.

Branches can be effectively clustered into efficiency groups using K-means.

Sensitivity analysis helps managers identify variables to improve branch performance.

Abstract

Evaluating the efficiency of organizations and branches within an organization is a challenging issue for managers. Evaluation criteria allow organizations to rank their internal units, identify their position concerning their competitors, and implement strategies for improvement and development purposes. Among the methods that have been applied in the evaluation of bank branches, non-parametric methods have captured the attention of researchers in recent years. One of the most widely used non-parametric methods is the data envelopment analysis (DEA) which leads to promising results. However, the static DEA approaches do not consider the time in the model. Therefore, this paper uses a dynamic DEA (DDEA) method to evaluate the branches of a private Iranian bank over three years (2017-2019). The results are then compared with static DEA. After ranking the branches, they are clustered using the K-means method. Finally, a comprehensive sensitivity analysis approach is introduced to help the managers to decide about changing variables to shift a branch from one cluster to a more efficient one.