The Design of Dynamic Model to Predict Money Demand in Tehran City in Automated Teller Machines (ATMs) (Case Study: Shahr Bank)

Document Type: Research Paper

Authors

1 Faculty of Engineering, Azad University of Science and Research Campus, Tehran, Iran

2 Department of Industrial Management, Allameh Tabatabaei University, Tehran, Iran

Abstract

ATMs are one of the most important cash distribution channels for banks. In this paper, asset and liability management model is investigated according to the ATMs failure times and ATMs failure to provide services by s, S policy. This paper seeks to provide a continuous review dynamic model for predicting demand with demands of money withdraw in a discrete way, in order to minimize the total costs of dormant money and lost opportunity for Shahr bank ATMs. The number of ATMs surveyed is 272 in Tehran city, and the second 6-month period in 1394 is intended to assess the machines behavior. Arena software is used to simulate the ATMs behavior. Results showed that this model is capable to provide money re-order point and money demand point up to desired size for bank ATMs. Accordingly, the optimal time and amount of money placement in ATMs and the minimum cost of the whole money putting process including optimization costs of the dormant money and lost opportunity are presented. With implementation of this model, in total, money placement costs of Shahr Bank ATMs in Tehran city has decreased by about 10 times.

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  1. Altunoglu, Y. (2010). “Cash inventory management at automated teller machines under incomplete information”, MSc Thesis of Turkey BILKENT University, PP. 25–40.
  2. Naghshineh, N., Hanifi, F., and Kordloei, H. (2013). “Management of bank assets and liabilities with the help of linear multi-objective programming by econometric simulation, Case study: Bank X”, Journal of Financial Engineering and management of securities (Portfolio Management): Vol. 4, No. 14, PP. 61–81.
  3. Kavand, M. (2010). “Design of assets liabilities optimal management mathematical model in non-usury banking - MCDM approach, case study: Iran Tose’e saderat bank”, MSc Thesis submitted by help doctor Adel Azar, PP. 35–45.
  4. Eghtesad novin bank risk study group (2008). “Assets liabilities management and liquidity risk in financial institutions”, Farasokhan, Tehran.
  5. Moshiri, E., and Karimi, M. (2006). “Optimal assets liabilities management in the banks using goal programming model (GP) and AHP method (Case study: Karafarin Bank)”, Vol. 8, No. 22, PP. 89–114.
  6. Taleeizadeh, A., and Salehi, A. (2015). “Stochastic inventory control model under the policy credit purchases”, Journal of Industrial Engineering, Vol. 49, No.1, PP. 69–78.
  7. Axsäter, S. (2015). Inventory control”,Vol. 225, PP. 42,43. Springer.
  8. Chiang, C. (2003). “Optimal replenishment for a periodic review inventory system with two supply modes”, European Journal of Operational Research, Vol. 149, No. 1, PP. 229–244.
  9. Çetinkaya, S., and Parlar, M. (1998) “Optimal myopic policy for a stochastic inventory problem with fixed and proportional back order costs”, European Journal of Operational Research, Vol. 110, No. 1, PP. 20–41.
  10. Smitus, R., Dilijonas, D., Bastian, L., Friman, J., and Drobinov, P. (2007). “Optimization of cash management for ATM network”, Information Technology And Control, Vol. 36, No. 1, PP. 117–121.
  11. Wagner, M. (2007) “The optimal cash deployment strategy-Modeling a network of Automated teller machines”, MSc Thesis, Hanken Swedish School of Economics and Business Administration, PP. 70–80
  12. Salimifard, Kh., and Farajzadeh, S. (2012), “Using monte carlo simulation to determine the amount of money in the ATM and the improvement of customer satisfaction”, Proceeding of the 3rd Annual European Decision Science Institute Conference, 24–27 June, Istanbul, Turkey.
  13. Supatchaya, Ch., Peerayuth, Ch., Juta, P., and John, K. (2013). “An optimization-based heuristic for a capacitated lot-sizing model in an automated teller machines network”, Journal of Mathematics and Statistics, Kasetsart University, Chatuchak, Bangkok, Thailand, Vol. 9, No. 4, PP. 283–288.
  14. Baker, T., Vaidyanathan, J., and Ashley, N. (2012). “A data-driven inventory control policy for cash logistics operations: An exploratory case study application at a financial institution”, Decision Sciences, Vol. 44, No. 1, PP. 205–226.
  15. Ekinci, Y., Lu, J. Ch., and Duman, E. (2014). “Optimization of ATM cash replenishment with group-demand forecasts”, Expert Systems with Applications, doi: http://dx.doi.org, Vol. 42, No. 7, PP. 3480–3490.
  16. Kolos, C., Ágoston, S., Benedek, G., and Gilányi, Z. (2016). “Pareto improvement and joint cash management optimization for banks and cash-in-transit firms”, European Journal of Operational Research, PP. 1–9.
  17. Sajjadi, Kh., and Azimi, P. (2014). “Optimizing the number of bank branches equipments by simulation and annealing algorithm”, Journal of Management Researches in Iran, Vol. 18, No. 4, PP. 65–86.