Modeling Queuing System in the Banking Industry

Table of Contents

CHAPTER ONE

INTRODUCTION

1.0 Background of the Study

1.1 Statement of the Problem

1.2 Objectives of the Study

1.3 Methodology

1.4 Justification

1.5 Limitation

1.6 Organization of the Thesis

CHAPTER TWO

LITERATURE REVIEW

2.0 Introduction

2.1 Queuing Researches In The Banking Industry

2.2 Historical Perspective of Queuing Theory

CHAPTER THREE

METHODOLOGY OF THE STUDY

3.0 Introduction

3.1 Primary Data Collection

3.2 Description of Queuing System

3.2.1 Queuing Systems in the Case of Study

3.3 Fundamental Queuing Relations

3.4 Customer Arrival and Inter Arrival Distribution

3.4.1 Probability Distribution

3.4.2 Exponential Distribution

3.4.3 Poisson Distribution

3.5 Teller Utilization Factor (ρ)

3.6 Little’s Law

3.7 Queuing Model Description

3.7.1 M / M / 1 Model

3.7.2 M / M / s Model

3.8 Data Sheet Processing

CHAPTER FOUR

ANALYSIS AND DISCUSSION OF RESULTS

4.0 Introduction

4.1 Primary Data Examination

4.2 Discussion Of Findings

4.3 Queuing Model Analysis

4.3.1 Performance Analysis

CHAPTER FIVE

SUMMARY, RECOMMENDATIONS AND CONCLUSION

5.0 Introduction

5.1 Summary of Findings

5.2 Conclusion

5.3 Recommendation to Stakeholders

5.4 Recommendation to Future Researchers

Research Objectives and Themes

The primary objective of this research is to evaluate whether the existing capacity levels within the banking industry, specifically at the Barclays Bank of Ghana (Old Tafo Branch) and the Agricultural Development Bank (Kumasi Adum Branch), effectively balance customer waiting times with service delivery efficiency. By applying queuing theory, the study seeks to optimize bank operations and improve customer satisfaction through informed management decisions.

• Analysis of customer arrival patterns and banking service times using Markovian properties.
• Evaluation of queuing performance metrics (e.g., idle time, waiting time, and queue length).
• Assessment of current teller capacity and its impact on service efficiency during peak vs. off-peak hours.
• Identification of behavioural factors such as jockeying and their influence on system congestion.
• Development of model-based recommendations for optimal staffing levels in banking branches.

Excerpt from the Book

Summary of Chapters

CHAPTER ONE: INTRODUCTION: Covers the background of banking in Ghana, the problem statement regarding queuing congestion, and the primary objectives of the study.

CHAPTER TWO: LITERATURE REVIEW: Explores historical perspectives on queuing theory and existing studies regarding queuing processes within the banking industry.

CHAPTER THREE: METHODOLOGY OF THE STUDY: Explains the mathematical models, including M/M/1 and M/M/s, and the observational methods used for data collection and analysis.

CHAPTER FOUR: ANALYSIS AND DISCUSSION OF RESULTS: Presents the findings regarding arrival patterns and the performance analysis of the bank-based queuing systems.

CHAPTER FIVE: SUMMARY, RECOMMENDATIONS AND CONCLUSION: Provides a final overview of results, offers actionable recommendations for bank stakeholders, and suggests directions for future research.

Keywords

Queuing Theory, Banking Industry, Poisson Distribution, Exponential Distribution, Markovian Properties, Service Capacity, Teller Utilization, Waiting Time, Performance Analysis, M/M/s Model, Customer Satisfaction, Queue Management, Arrival Rate, Service Rate, Operational Efficiency.

Frequently Asked Questions

What is the primary focus of this thesis?

This work focuses on evaluating queuing systems in the banking industry by using mathematical models to determine whether current service capacities effectively balance waiting and service times for customers.

Which banking institutions are analyzed in this study?

The study investigates the Agricultural Development Bank (Kumasi Adum Branch) and the Barclays Bank of Ghana (Old Tafo Branch) as case study locations.

What is the central research question?

The research asks if current staffing and capacity levels in these banking branches achieve an optimal balance between minimizing customer wait times and maintaining efficient service throughput.

Which scientific methods are utilized?

The author employs queuing theory, specifically using M/M/1 and M/M/s models to perform probabilistic analysis, calculate performance measures, and evaluate the steady-state behaviour of customer lines.

What key components are examined in the main section of the paper?

The main part of the paper examines data collection, fundamental queuing relationships (such as Lambda and Mu), arrival and service distributions, and the transition from theoretical models to empirical results.

How are the key results defined?

Key results are defined by performance metrics like teller utilization factors, average queue lengths, probability of system emptiness, and average time spent in the system, which are then used to formulate management policies.

How does the number of tellers specifically affect the queue?

The study observes that reducing the number of tellers during peak times significantly increases the capacity utilization factor, which directly leads to longer queues, higher waiting times, and decreased customer satisfaction.

What is the impact of customer behaviour on the results?

The research notes that behaviours such as "jockeying" (switching between lines to wait for shorter service) are frequent at certain branches and distort the standard queuing model, necessitating strategic management interventions like queuing guards.