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Introduction

In recent years, anomaly detection in supply chain logistics has become increasingly important due to the complexity and interconnectedness of global supply chains. Anomaly detection involves identifying abnormalities or deviations from normal patterns within the supply chain, which can be indicative of fraud, errors, or potential disruptions. Traditional methods of anomaly detection rely on rule-based systems or supervised machine learning techniques, which require labeled training data. However, these methods are often limited in their ability to adapt to new and evolving patterns within the supply chain.

In this thesis, we will investigate the use of unsupervised learning techniques in conjunction with GPS data to detect anomalies in supply chain logistics. Unsupervised learning algorithms, such as clustering and anomaly detection algorithms, do not require labeled training data and are able to identify patterns and anomalies in an unsupervised manner. GPS data, which provides real-time and accurate location information, can be used to track the movement of goods and vehicles within the supply chain.

The goal of this thesis is to develop a novel approach to anomaly detection in supply chain logistics that leverages unsupervised learning and GPS data. By doing so, we aim to provide supply chain managers with a more robust and adaptable method for detecting anomalies and mitigating potential risks within the supply chain.

Table of Contents

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Introduction to Anomaly Detection in Supply Chain Logistics
2.2 Traditional Methods of Anomaly Detection
2.3 Unsupervised Learning Techniques
2.4 GPS Data in Supply Chain Logistics
2.5 Integration of Unsupervised Learning and GPS Data
2.6 Case Studies of Anomaly Detection in Supply Chain Logistics
2.7 Challenges and Opportunities in Anomaly Detection
2.8 Current Trends in Supply Chain Anomaly Detection
2.9 Gaps in Existing Literature
2.10 Conclusion

Chapter 3: Research Methodology
3.1 Introduction
3.2 Research Design
3.3 Data Collection and Preprocessing
3.4 Selection of Unsupervised Learning Algorithms
3.5 Integration of GPS Data
3.6 Evaluation Metrics
3.7 Implementation Plan
3.8 Ethical Considerations
3.9 Data Analysis Techniques
3.10 Conclusion

Chapter 4: Discussion of Findings
4.1 Introduction
4.2 Analysis of Anomaly Detection Results
4.3 Interpretation of Detected Anomalies
4.4 Comparison with Traditional Methods
4.5 Implications for Supply Chain Managers
4.6 Recommendations for Future Research
4.7 Strengths and Limitations of the Approach
4.8 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Limitations of the Study
5.5 Future Research Directions
5.6 Conclusion

Thesis Overview

Anomaly detection in supply chain logistics using unsupervised learning and GPS data is a critical area of research that addresses the need for more adaptive and robust methods for detecting abnormalities within supply chains. This thesis aims to develop a novel approach that leverages the power of unsupervised learning algorithms and GPS data to identify anomalies in supply chain logistics.

Chapter 1 provides an introduction to the topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter 2 presents a comprehensive literature review on anomaly detection in supply chain logistics, unsupervised learning techniques, GPS data, integration of unsupervised learning and GPS data, case studies, challenges, opportunities, current trends, and gaps in existing literature.

In Chapter 3, the research methodology is discussed, including research design, data collection and preprocessing, selection of unsupervised learning algorithms, integration of GPS data, evaluation metrics, implementation plan, ethical considerations, and data analysis techniques. Chapter 4 presents a detailed discussion of the findings, including analysis of anomaly detection results, interpretation of detected anomalies, comparison with traditional methods, implications for supply chain managers, recommendations for future research, strengths, and limitations.

Chapter 5 concludes the thesis by summarizing the findings, highlighting contributions to the field, discussing practical implications, identifying limitations of the study, suggesting future research directions, and offering a final conclusion on anomaly detection in supply chain logistics using unsupervised learning and GPS data.

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