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Introduction:

With the rise of e-commerce and changing consumer expectations, retailers are increasingly turning to technology to streamline their operations and enhance the customer experience. One such technology that has gained momentum in recent years is Edge AI, which combines artificial intelligence with edge computing to enable real-time data processing and decision-making at the edge of the network. This thesis explores the application of Edge AI in autonomous retail inventory management, aiming to optimize inventory levels, reduce out-of-stock incidents, and improve overall operational efficiency in retail stores.

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 Evolution of retail inventory management
2.2 Core concepts of Edge AI
2.3 Applications of Edge AI in retail
2.4 Benefits and challenges of using Edge AI in inventory management
2.5 Comparison with traditional inventory management techniques
2.6 Case studies of successful implementation
2.7 Impact on customer satisfaction
2.8 Integration with other technologies
2.9 Future trends and opportunities
2.10 Summary of key findings

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 Sampling strategy
3.5 Ethical considerations
3.6 Research instruments
3.7 Validation and reliability
3.8 Limitations of the study
3.9 Potential biases
3.10 Conclusion

Chapter 4: Discussion of Findings
4.1 Analysis of data
4.2 Comparison with research objectives
4.3 Implications for retail industry
4.4 Recommendations for retailers
4.5 Practical implications
4.6 Theoretical contributions
4.7 Areas for future research
4.8 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to knowledge
5.3 Practical implications for retailers
5.4 Limitations of the study
5.5 Recommendations for future research
5.6 Conclusion

Thesis Overview:

Edge AI has emerged as a powerful technology for retailers looking to automate and optimize their inventory management processes. By combining artificial intelligence with edge computing, retailers can now make real-time decisions at the edge of the network, leading to improved accuracy, efficiency, and customer satisfaction. This thesis explores the application of Edge AI in autonomous retail inventory management, aiming to provide a comprehensive overview of the benefits, challenges, and opportunities associated with this technology.

Chapter 1 sets the stage by introducing the research topic, providing background information, outlining the problem statement, objectives, scope, and significance of the study. The chapter also includes the structure of the thesis and definitions of key terms.

Chapter 2 delves into the existing literature on retail inventory management, Edge AI, and their intersection. It examines the evolution of inventory management, core concepts of Edge AI, applications in retail, benefits, challenges, and future trends. Case studies and comparisons with traditional techniques are also discussed.

Chapter 3 outlines the research methodology, including design, data collection, analysis, sampling, ethical considerations, and validation. Potential biases and limitations of the study are also highlighted.

Chapter 4 presents the findings of the research, analyzing data, comparing with objectives, discussing implications for the retail industry, providing recommendations, and suggesting areas for future research.

Chapter 5 concludes the thesis by summarizing key findings, contributions to knowledge, practical implications, limitations, recommendations for future research, and a final conclusion. Through this comprehensive exploration of Edge AI for autonomous retail inventory management, this thesis aims to contribute to the growing body of knowledge in this field and provide valuable insights for retailers looking to leverage technology for competitive advantage.

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