Ali Mohaghar; Sara Aryaee; Jalil Heidary; Ara Toomanian
Abstract
Nowadays banks, credit and financial institutions are trying to increase profits, reduce costs, compete with rivals, attract customers and increase productivity. One of the factors that contributes to the implementation of these strategies is the optimum locations of branches. Locating the new branches ...
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Nowadays banks, credit and financial institutions are trying to increase profits, reduce costs, compete with rivals, attract customers and increase productivity. One of the factors that contributes to the implementation of these strategies is the optimum locations of branches. Locating the new branches of Mehr Eghtesad bank in the region 1 in Tehran city is the aim of this research. Since the focus of service centers such as banks is on maximal or full service to customers, among the all the covering models, Maximal Covering Location model is chosen as the best option to locate the new bank branches. To this end, related literature about locating bank branches, Geographical Information system (GIS) and maximal covering location problem (MCLP) examined. Then, through library Studies and interviewing with managers and experts, the researcher chose effective criteria and sub criteria for locating bank branches. The weights of criteria and sub criteria were determined through filling the questionnaires by managers. GIS used to extract some input data for the model and weighted maximal covering model (MCLM) with partial covering used to choose the best locations. Mathematical programming model formulated with 363 binary variables, 122 constraints, 121 demand areas, 121 potential points, the 1000 m buffer, α = 0.75, b = 50%, θ = 2 and s= 8 & 30 branches (with two different scenarios) and solved with GAMS optimization software. It is clear that by solving the first scenario, eight suitable locations and second scenario thirty suitable locations to open new branches will be generated.
Kaveh Khalili Damghani; Mohammad TaghaviFard; Kiaras Karbaschi
Abstract
The main goal of this paper is to evaluate the relative efficiency of each level of customer services in MELLI bank branches. A three stage process is defined as consecutive results of service provision to the customers. This process consists of sub-process such as customer expectations, customer satisfaction, ...
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The main goal of this paper is to evaluate the relative efficiency of each level of customer services in MELLI bank branches. A three stage process is defined as consecutive results of service provision to the customers. This process consists of sub-process such as customer expectations, customer satisfaction, and customer loyalty. A hybrid method based on Multi-criteria Satisfaction Analysis (MUSA) and network Data Envelopment Analysis (DEA) is proposed to evaluate the relative efficiency of 30 branches. In this way, first the customer satisfaction was measured through a direct questionnaire based on customers perceptions analysis and quantified using MUSA method. Then, the customer satisfaction scores and the other important evaluating criteria such as number of employees, average evaluation scores of staff, operating costs, the amount of deposits, total credit facilities, the number of new checking accounts, expectations and customer loyalty were considered in DEA model as inputs and outputs. A three-stage DEA model was used to evaluate the efficiency of bank branches. The proposed DEA model was based on multipliers perspective, output-oriented with constant return to scale. The proposed three-stage DEA model quantified and assessed the efficiency of customer expectations, customer satisfactions, and customer loyalties in branches. The results showed that the mean relative efficiency of selected branches in three sub-processes namely customer satisfaction, operational results and customer loyalty were 83%, 94%, and 90%, respectively. The mean efficiency of the overall process is 89%.And four branches (about 13% of sample) were placed on efficient frontier for all sub-processes. Based on research findings, the branches which have been efficient in customer expectations were also efficient in other sub-processes and the main process.
