محمدعلی کرامتی

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.

One of the most important applications of data envelopment analysis tech-nique is measuring the efficiency of bank branches. Performance measure-ment in the banking industry is important for several groups, including bank managers, customers, investors, and shareholders. The purpose of this study is to examine and design a mixed structure to measure the efficiency of branches of Iranian banks according to their policies. In order to obtain the efficiency of the structure divisions proposed in this study, a slack-based NDEA model was selected to solve its mathematical model. The study sam-ple consists of 31 branches of a large commercial bank in Iran. The ad-vantage of this research to previous studies is that the result will be more realistic considering the inputs and outputs consistent with Iran's banking conditions.

In the current situation, one of the concerns in the fight against smuggling of goods and currency is the improvement of the inter-organizational network. The purpose of this research is to design and implement organizational architecture to improve the management of organizational networks with SWOT approach, in this area using the artificial neural network toolbox and fuzzy logic in Matlab. This research is applied-modeling in terms of purpose. The statistical population includes expert professors and experts of organizations in charge of combating smuggling of goods and currency. After distributing 100 questionnaires, the sample size of this study is equal to 96 experts who were selected by a combination of two methods of non-probabilistic purposive sampling and snowball sampling. The results show that using the intelligent system, the status of "success of the organization's network management" can be examined numerically and more accurately: In terms of ideal importance, if; The "Network Management Based on EA Application Layer" status is good, ie exactly 0.813, and "Network management based on EA data source layer" is good, ie exactly 0.824, and "Network Management Based on EA Central Component Layer" is good, ie exactly 0.819, and "Network Management Based on EA Data Preparation Layer" is good, ie exactly 0.812, and "Network management based on EA service quality layer" in good condition, ie exactly 0.815;Then; The status of "successful implementation of the organization's network management" is at the top level (fifth level), ie exactly 0.952.According to the membership functions of language variables by experts, the value of 4.76 within the 5-value range in the range defined for the "excellent" language variable, ie the success status of the organization's network management, has been calculated exactly 0.952.