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Introduction

In recent years, supply chain disruptions have become a major concern for businesses around the world. These disruptions can lead to significant financial losses, delays in production, and damage to a company’s reputation. To mitigate the impact of these disruptions, many companies are turning to predictive modeling using supplier data and machine learning techniques.

This thesis aims to investigate the use of predictive modeling for supply chain disruptions using supplier data and machine learning. By analyzing historical data on supplier performance, companies can identify patterns and trends that may indicate future disruptions. By leveraging machine learning algorithms, companies can build predictive models that can help them anticipate and respond to potential disruptions in real-time.

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 Overview of supply chain disruptions
2.2 Predictive modeling in supply chain management
2.3 Supplier data analytics
2.4 Machine learning algorithms for predictive modeling
2.5 Case studies on predictive modeling for supply chain disruptions
2.6 Challenges and limitations of predictive modeling
2.7 Best practices for implementing predictive modeling
2.8 Theoretical frameworks for supply chain disruption management
2.9 Integration of predictive modeling with risk management
2.10 Future trends in supply chain disruption management

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data preprocessing techniques
3.4 Selection of machine learning algorithms
3.5 Model training and testing
3.6 Performance evaluation metrics
3.7 Development of predictive models
3.8 Validation of predictive models

Chapter 4: Discussion of Findings
4.1 Analysis of historical supplier data
4.2 Identification of key predictors for supply chain disruptions
4.3 Building predictive models using machine learning algorithms
4.4 Evaluation of predictive model performance
4.5 Comparison of different predictive modeling approaches
4.6 Recommendations for improving supply chain disruption management
4.7 Implications for practice
4.8 Areas for future research

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Implications for supply chain management
5.3 Contributions to the field of predictive modeling
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview:

This thesis aims to explore the use of predictive modeling for supply chain disruptions using supplier data and machine learning. The introduction provides a comprehensive overview of the research topic, highlighting the significance of the study, the objectives, and the structure of the thesis. The literature review synthesizes existing knowledge on predictive modeling, supplier data analytics, and machine learning algorithms in the context of supply chain disruption management. The research methodology outlines the approach taken to collect, preprocess, analyze, and model supplier data. The discussion of findings presents the results of the predictive modeling analysis, with a focus on key predictors of supply chain disruptions and the performance of different predictive models. The conclusion summarizes the key findings, implications for practice, and recommendations for future research in the field of predictive modeling for supply chain disruptions using supplier data and machine learning.

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