Research Article
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Year 2020, Volume: 10 Issue: 1, 64 - 74, 01.06.2020
https://doi.org/10.36222/ejt.683040

Abstract

References

  • [1] Abensur, E.O. (2011). Banking operations using queuing theory and genetic algorithms," Produto & Produção, 12(2), 69 - 86.
  • [2] Kelton W., Sadowski, R., Sturrock, D.T. Simulation with Arena, 4th Edition, McGraw-Hill, New York, USA, 2007.
  • [3] Cardoen, B., Demeulemeester, E., Beliën, J. (2010). Operating room planning and scheduling: A literature review, European journal of operational research, 201(3), 921-932.
  • [4] VanBerkel, P.T., Blake, J.T. (2007). A comprehensive simulation for wait time reduction and capacity planning applied in general surgery, Health care management Science, 10(4), 373-385.
  • [5] Favaretto, F. (2018). Management of Queues Assisted by Two Servers With Different Assistance Rates, Sistemas & Gestao, 13(1), 2-9.
  • [6] Paradi, J.C., Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis, Omega, 41(1), 61-79.
  • [7] Banks, J., Carson II, J., Nelson, B., Nicol, D. Discrete-event system simulation, 5th Edition, Prentice Hall, USA, 2009.
  • [8] Banks, J. Handbook of simulation: principles, methodology, advances, applications, and practice. John Wiley & Sons, 1998.
  • [9] Sargent, R.G. (2010). Verification and validation of simulation models," In Proceedings of the 2010 IEEE winter simulation conference, Baltimore, MD, USA, 166-183.
  • [10] Sargent, R.G. (2013). Verification and validation of simulation models, Journal of simulation, 7(1), 12-24.
  • [11] Montevechi, J., Leal, F., de Pinho, A., da Silva Costa, R., de Oliveira, M., da Silva, A. (2010). Conceptual modeling in simulation projects by mean adapted IDEF: an application in a Brazilian tech company," In Proceedings of the 2010 IEEE winter simulation conference, Baltimore, MD, USA, 1624-1635.
  • [12] Gibbons, J.D., Olkin, I., Sobel, M. (1999). Selecting and Ordering Populations: A New Statistical Methodology, 2nd Edition, SIAM, USA, 1999.

A SIMULATION MODEL FOR CUSTOMER FLOW ANALYSIS IN A COMMERCIAL BANK IN NIGERIA

Year 2020, Volume: 10 Issue: 1, 64 - 74, 01.06.2020
https://doi.org/10.36222/ejt.683040

Abstract

In view of competitiveness and increasing search for improved services in Nigerian commercial banks, there are some organizational changes necessary to facilitate this fate. Recently, the possibility of applying operational research techniques, more specifically computer simulation, was raised to address the problem of customer queues at bank branches. No bank has been punished with a fine if there are customers waiting for more than a certain period of time in the queues to be served. The solution to the problem sought to offer customer-care to the crowd in all bank branches in Nigeria. In view of this, the present work proposes a computer simulation model to study the flow of customers in a branch of the bank. This model simulates service capacity and time in the face of various hypothetical scenarios to which a bank branch may be subject using the Rockwell Automation Arena® v15 software. The results make it possible to evaluate new policies for increasing the quality of service and compliance with easy customer-care operational service. The generated scenarios were evidence alternatives that would reduce waiting times with only a few minor alterations. Thereby allowing the service with maximum waiting time the standards required.

References

  • [1] Abensur, E.O. (2011). Banking operations using queuing theory and genetic algorithms," Produto & Produção, 12(2), 69 - 86.
  • [2] Kelton W., Sadowski, R., Sturrock, D.T. Simulation with Arena, 4th Edition, McGraw-Hill, New York, USA, 2007.
  • [3] Cardoen, B., Demeulemeester, E., Beliën, J. (2010). Operating room planning and scheduling: A literature review, European journal of operational research, 201(3), 921-932.
  • [4] VanBerkel, P.T., Blake, J.T. (2007). A comprehensive simulation for wait time reduction and capacity planning applied in general surgery, Health care management Science, 10(4), 373-385.
  • [5] Favaretto, F. (2018). Management of Queues Assisted by Two Servers With Different Assistance Rates, Sistemas & Gestao, 13(1), 2-9.
  • [6] Paradi, J.C., Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis, Omega, 41(1), 61-79.
  • [7] Banks, J., Carson II, J., Nelson, B., Nicol, D. Discrete-event system simulation, 5th Edition, Prentice Hall, USA, 2009.
  • [8] Banks, J. Handbook of simulation: principles, methodology, advances, applications, and practice. John Wiley & Sons, 1998.
  • [9] Sargent, R.G. (2010). Verification and validation of simulation models," In Proceedings of the 2010 IEEE winter simulation conference, Baltimore, MD, USA, 166-183.
  • [10] Sargent, R.G. (2013). Verification and validation of simulation models, Journal of simulation, 7(1), 12-24.
  • [11] Montevechi, J., Leal, F., de Pinho, A., da Silva Costa, R., de Oliveira, M., da Silva, A. (2010). Conceptual modeling in simulation projects by mean adapted IDEF: an application in a Brazilian tech company," In Proceedings of the 2010 IEEE winter simulation conference, Baltimore, MD, USA, 1624-1635.
  • [12] Gibbons, J.D., Olkin, I., Sobel, M. (1999). Selecting and Ordering Populations: A New Statistical Methodology, 2nd Edition, SIAM, USA, 1999.
There are 12 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Article
Authors

Mayowa Rafiat Ajiboye

Publication Date June 1, 2020
Published in Issue Year 2020 Volume: 10 Issue: 1

Cite

APA Ajiboye, M. R. (2020). A SIMULATION MODEL FOR CUSTOMER FLOW ANALYSIS IN A COMMERCIAL BANK IN NIGERIA. European Journal of Technique (EJT), 10(1), 64-74. https://doi.org/10.36222/ejt.683040

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