Calculation of efficiency and efficiency of branches of National Bank of West Tehran

Subject Areas : Industrial Management

Niloofar Nikkhah ¹ , amirgholam abri ²

1 - Department of Management, Firoozkoh Branch, Islamic Azad Univeristy, Firoozkoh , Iran
2 - Associate Professor of Mathematics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

Received: 2019-12-25 Accepted : 2020-06-15 Published : 2020-07-18

Keywords: Ranking, Malmquist Productivity Factor, West Branch of National Bank of Iran, performance,

Abstract :

The bank is an economic entity that carries out the tasks of equipping and distributing credit, financial operations, currency trading, documentary and dividend claims, stock maintenance and securities. The National Bank of Iran has a staff of over 45,000 people. The National Bank has 5 branches operating in Iran, 2 operating branches and 2 subsidiaries abroad. Branches and Evaluate Branch Productivity during 96 and 97 Years Using Data Envelopment Analysis (DEA) Model. For this purpose, the effective factors are first evaluated and then the DEA model is used to evaluate the performance of the branches in order to measure their efficiency and inefficiency. We then rank them using the AP model and finally measure the productivity of a branch relative to 96 and 97 using the Malmquist Productivity Coefficient model. In this paper, using the data analysis approach, the performance of the West Bank Branches of Tehran National Bank in two years has been evaluated. The efficient and inefficient units of this branch are then identified by the CCR model and finally the efficient branches are ranked with the Anderson Peterson (AP) model and finally the Malmquist efficiency model is measured.

References:

1-        Anderson, P., & Petersen, N.C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10): 1261-12. 


2- Banker, R.D., & Charnes, A., & Cooper, W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9): 1078-1092. 


3-        Chang, Tzu-Pu.; Jin-Li Hu, Ray; Yeutien Chou & Lei Sun. (2012). The Sources of Bank Productivity Growth in China During  (2002-2009):  A Disaggregation View. Journal of Banking and finance, 36, 1997-2006.

4-        Chavoshi, Seyedeh Fatemeh, Mahmoudzad, Mahmoud, Gholam Abri, Amir. (2019). Evaluating the Efficiency of E-Commerce in Iran's Provinces with a Coverage of Data Approach. Quarterly Journal of Economic Research and Policy, 27(89), 233-286.

5- Cooper, W., & Seiford, L., &Tone, K. (2002). Data envelopment analysis a comprehensive text with models applications references, DEA solved software. Third Printing by Kluwer academic publishers. 


6- Gholam Abri, A., & Jahanshahloo, G.R., & Hosseinzadeh Lotfi, F., & Shoja. N., & Fallah Jelodar, M. (2013). A new method for ranking non-extreme efficient units in Data envelopment analysis. Optimization letters, 7(1), 309-324. 


7-        Gholam Abri, Amir. (2014). Efficiency Book of Social Organization Branches of Isfahan Province. Modeling Quarterly, 6(8), 83-99.

8-        Fu, X. & Sh. Heffernan. (2007). Cost X-Efficiency in China's Banking Sector.

China Economic Review, 18, 35-53.

9-        Martin D.H., G.Kocher and M. Sutter. (2000). Measuring Efficeincy of German Football Teams by DEA. University of Innsbruck, Australia, 4-5.

10-    Moazzami Gudarzi M, Jaberansari M, Moallem A, Shakiba M. (2014). Applying Data Envelopment Analysis (DEA) for Measuring Relative Efficiency and Ranking Branches of Refah Kargaran Bank in Lorestan Province. QJER, 14 (1),115-126

11-    Wang, Ke.; Huang, Wei; Wu, Jie & Ying-Nan Liu. (2014). Efficiency Measures of the Chinese Commercial Banking System using an Additive two-Stage DEA. Omega, 44, 5-20.