Abstract:
Network data envelopment analysis models assess efficiency of decision-making unit and its sections using historical data but fail to measure efficiency of its units and their internal stages in the future. In this paper we aim to measure efficiency of stages of bank branches and obtain efficiency trend of stages during the time, then to estimate their efficiency in the future therefore we can be aware of stages inefficiency before occurrence and prevent them. First, a two-stage structure including deposit collection and loan giving was designed for bank branches using literature review and comments of experts. Human forces and fixed assets were considered as input variables of the first stage, deposit as mediator variable, delayed claims as interim variable, and loan amount as output variable of the second stage. Then, a dynamic network data envelopment analysis model was formulated and stages efficiency were obtained for 16 consecutive periods. Therefore, efficiency trend of stages was obtained during the time. In the following, efficiency of various stages of branches were estimated using artificial neural network and some recommendations are provided according to obtained amounts in order to prevent inefficiency before occurrence.
Machine summary:
Estimating Efficiency of Bank Branches by Dynamic Network Data Envelopment Analysis and Artificial Neural Network Javad Niknafs, Mohammad Ali Keramati*, Jalal Haghighat Monfared Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran ARTICLE INFO Article history: Received 24 February 2019 Accepted 23 April 2019 Keywords: Network data envelopment analysisDynamic network data envelopment analysis Artificial neural network Efficiency estimating Bank ABSTRACT Network data envelopment analysis models assess efficiency of decision-making unit and its sections using historical data but fail to measure efficiency of its units and their internal stages in the future.
Then, a dynamic network data envelopment analysis model was formulated and stages efficiency were obtained for 16 consecutive periods.
In the following, efficiency of various stages of branches were estimated using artificial neural network and some recommendations are provided according to obtained amounts in order to prevent inefficiency before occurrence.
On the other hand, network DEA models de- pend on historical data and are not able to estimate the efficiency trend of decision-making units and their internal structure.
In this paper, DNDEA has been used together with artificial neural network to estimate the efficiency of internal structure of decision-making units (bank branches) in future.
Dynamic Network Data Envelopment Analysis Dynamic network models consider both structure and time in assessment of stages efficiency of a unit.
Avkiran [11] designed a dynamic network model for commercial banks and showed that a more com- prehensive analysis on efficiency estimation can be provided considering dimensions of network and time.