Efficiency Evaluation of Banks with Many Branches using a Heuristic Framework and Dynamic Data Envelopment Optimization Approach: A Real Case Study
This work addresses efficiency evaluation for bank managers, but it is incremental as it extends existing DEA methods to a dynamic context with a specific case study.
This paper tackled the problem of evaluating bank branch efficiency by applying a dynamic data envelopment analysis (DDEA) method to a private Iranian bank's branches over three years, comparing results with static DEA and clustering branches using K-means, with sensitivity analysis to guide managerial decisions for improvement.
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.