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October 2, 20250 citations

Applications of Waiting Lines Models for Improving the Performance Levels of Banking Service System

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MAMahmud E. AbushaalaAEAhmed A. Essdai

Key Points

  • The study finds that the bank's service system operates under stable conditions, with a utilization rate of 79% in the first half.
  • Utilization rates indicate the system's capacity, as it shows a busy probability of 79% in the first period and 62% in the second.
  • The research applied waiting lines models to evaluate service efficiency over two distinct periods in a banking context.
  • Quantitative methods confirmed that the bank still faces challenges with waiting times, particularly notable during peak service periods.

Abstract

In this study, the theory of waiting queues was applied to improve the quality of banking services provided by Jumhouria Bank, Ahmed Al-Sharif Branch. By using a single- row waiting queue model and several service centers with one service stage (MM/s), and to determine the requirements for building the model, the study focused on banking service centers, where the research relied on studying two periods (the first period is first half of the month and the second period is second half of the month), according to the bank’s system, which usually adopts five service centers in the first half of the month and three service centers in the second half of the month. The importance of the study lies in that it addresses the problem of the unwanted waiting phenomenon that most industrial and service institutions suffer from. The aim of study is to develop a model that optimizes the waiting line quantitatively. The service rate and arrival rate were calculated and queuing equations were applied using Quantitative Methods (QM) software (Waiting Lines). The study concluded that the system operates under a stable state, where the value of the utilization rate, which is the probability that the system will be busy in the first period, is 79%, the probability that the system will be empty is 1%, the probability that the system will be busy in the second period is 62%, and the probability is 13% that the system is empty in the second period.

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Cite This Study

Abushaala et al. (2024) studied this question.

synapsesocial.com/papers/68de6f4283cbc991d0a22fe8https://doi.org/10.26629/jtr.2024.03
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