QUEUING SYSTEM MODEL EVALUATION EFFICIENCY IN SERVICE (A CASE STUDY UNION BANK BAUCHI BRANCH, BAUCHI STATE)
Abstract
Abstract
This project evaluates the efficiency of the queuing system at Union Bank, Bauchi Branch, Bauchi State, Nigeria, with the aim of improving service delivery and customer satisfaction. The study was motivated by the persistent problem of long waiting times and overcrowded banking halls, which lead to customer dissatisfaction, despite the adoption of modern automation technologies. The primary objective was to model the existing queuing system to determine the optimal number of servers required to minimize customer waiting time.The research adopted an observational study design, with primary data collected over a two-week period during peak banking hours (8:00 am to 12:00 pm). Key metrics such as customer arrival times and service times were recorded. The data were analyzed using a Multi-Server Queuing Model (M/M/S): (∞/FCFS), and computational analysis was performed using MATLAB software. Performance measures including arrival rate (λ), service rate (μ), utilization factor (ρ), average number of customers in the system (Ls), and average waiting time in the queue (Wq) were computed.The findings revealed an average arrival rate (λ) of 0.3078 customers per minute and a service rate (μ) of 0.2217 customers per minute. The analysis demonstrated that with the current low number of servers, the system utilization was high (over 69% for two servers), leading to significant queues and delays. However, the model indicated that increasing the number of servers to five would drastically reduce the utilization factor to approximately 23%, effectively eliminating the queue and ensuring customers are served promptly upon arrival.In conclusion, the queuing system at Union Bank, Bauchi Branch, is inefficient with the current server capacity. The study strongly recommends that the bank management increases the number of servers to at least five to optimize service efficiency, significantly reduce customer waiting times, and enhance overall customer satisfaction. Additional recommendations include investing in technology and implementing a robust customer feedback mechanism for continuous improvement.
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