Simulation- based performance analysis of two commercial banks using multi-server queuing models

Abstract

Enhancing the effectiveness of service sectors with human employees performing random arrival and service times is a challenging decision-making environment. The banking sector is a good example of the current situation. In this study, Python-based simulation in multi-server queuing model is apply to compare the customer service performance of two commercial banks The overall operation of two private banks Bank A and Bank B are examined in this case study. However, in view of confidentiality of data concerns, the name of the bank is not disclosed in this study. The simulation results indicate which bank is more efficient in terms of customer handling capacity and service delivery speed. The paper evaluates key performance indicators like average queue length, customer waiting time, server utilization, and idle time by simulating real-world customer arrivals and service processes. According to the research outcomes, Bank A performs more effectively than Bank B, with an average waiting time of around 9.9 minutes as compare to 17.7 minutes for Bank B. In addition, Bank a Bank A managed shorter and more stable queue waits along with more balanced server utilization whereas Bank B displayed a longer waiting time of customers and greater queue variations. Based on the results, Bank A's service system is superior in terms of managing customers during peak hours, whereas Bank B would benefit through expanding its capacity for service or improving teller performance. The study highlighted how valuable simulation tools are for operational decision-making and provide suggestions for the best manning and queue handling methods.