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Introduction

The use of Artificial Intelligence (AI) in the retail industry has seen significant growth in recent years, revolutionizing the way businesses manage their operations. One of the key areas where AI is making a big impact is in inventory management. Traditional inventory management systems are often inefficient and prone to human error, leading to stockouts, overstocking, and lost sales opportunities.

Edge AI, a subset of AI that enables data processing to be done on the device itself, rather than relying on a central server, offers a promising solution for improving inventory management in retail settings. By deploying AI algorithms directly on in-store devices such as sensors, cameras, and smart shelves, retailers can make real-time decisions based on accurate and up-to-date data, leading to better inventory control, reduced costs, and improved customer satisfaction.

This thesis aims to explore the use of Edge AI for smart retail inventory management, investigating its effectiveness in optimizing stock levels, reducing waste, and enhancing supply chain efficiency. By leveraging the capabilities of Edge AI, retailers can gain a competitive edge in the fast-paced and competitive retail landscape.

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 AI in Retail Inventory Management
2.2 Edge AI Technology in Inventory Management
2.3 Benefits of Edge AI for Retail Inventory Management
2.4 Challenges and Limitations of Implementing Edge AI
2.5 Case Studies on Edge AI Implementation in Retail
2.6 Emerging Trends in Edge AI for Inventory Management
2.7 Comparison of Edge AI with Traditional Inventory Management Systems
2.8 Integration of Edge AI with IoT in Retail Inventory Management
2.9 Security and Privacy Concerns in Edge AI Deployment
2.10 Future Directions of Edge AI in Retail Inventory Management

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Sampling Strategy
3.5 Measurement Instrument
3.6 Ethical Considerations
3.7 Operationalization of Variables
3.8 Data Validation and Reliability
3.9 Research Limitations
3.10 Delimitations

Chapter 4: Discussion of Findings
– Detailed analysis and interpretation of research findings
– Comparison of findings with existing literature
– Implications for practice and future research
– Recommendations for retailers and policymakers
– Suggestions for improving Edge AI implementation in retail

Chapter 5: Conclusion and Summary
– Recap of main findings and contributions
– Discussion of key insights and implications
– Limitations of the study and suggestions for future research
– Conclusion on the effectiveness of Edge AI for smart retail inventory management

Thesis Overview: Edge AI for Smart Retail Inventory Management

The integration of Edge AI technology in retail inventory management holds the potential to revolutionize how businesses track, manage, and optimize their inventory levels. By leveraging the power of AI algorithms directly on in-store devices, retailers can make data-driven decisions in real-time, leading to improved efficiency, reduced costs, and enhanced customer satisfaction.

In this thesis, we will delve into the use of Edge AI for smart retail inventory management, exploring the benefits, challenges, and future trends of this innovative technology. Through a comprehensive literature review, research methodology, discussion of findings, and conclusion, we aim to provide valuable insights for retailers looking to embrace Edge AI in their inventory management processes.

By shedding light on the potential of Edge AI in transforming retail inventory management, this thesis aims to contribute to the advancement of AI technologies in the retail sector and help businesses stay ahead in an increasingly competitive market.

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