Thesis Abstract

Abstract
Artificial Intelligence (AI) is revolutionizing various industries, and its impact on supply chain management in the retail industry is becoming increasingly significant. This thesis explores the implications of AI on supply chain management practices within the retail sector. The study aims to investigate how AI technologies are transforming traditional supply chain processes, enhancing efficiency, reducing costs, and improving customer satisfaction. The research methodology utilized a combination of literature review, case studies, and interviews with industry experts to gather insights into the adoption of AI in supply chain management within retail organizations. Findings from the study reveal that AI technologies such as machine learning, predictive analytics, and robotic process automation have the potential to optimize inventory management, demand forecasting, logistics planning, and customer service in retail supply chains. The limitations of the study include the rapidly evolving nature of AI technologies and the challenges associated with data privacy and security. The scope of the study focuses on the application of AI in supply chain management specifically within the retail industry, highlighting key trends, opportunities, and challenges faced by organizations in adopting AI-driven solutions. The significance of this research lies in its contribution to the existing body of knowledge on AI and supply chain management, providing valuable insights for retail organizations seeking to leverage AI technologies to enhance their supply chain operations. The study also offers practical recommendations for policymakers, industry practitioners, and researchers interested in exploring the potential of AI in transforming supply chain management practices. In conclusion, this thesis underscores the transformative impact of AI on supply chain management in the retail industry, emphasizing the need for organizations to embrace AI technologies to stay competitive and meet the evolving demands of the market. The findings of this study serve as a roadmap for retail organizations looking to harness the power of AI to drive innovation, improve operational efficiency, and deliver superior customer experiences in the dynamic retail landscape.

Thesis Overview

"The Impact of Artificial Intelligence on Supply Chain Management in the Retail Industry" aims to explore the transformative effects of artificial intelligence (AI) technology on supply chain operations within the retail sector. This research project seeks to investigate how the integration of AI solutions can enhance efficiency, cost-effectiveness, and overall performance in managing supply chains within the retail industry. By examining the current landscape of supply chain management practices in retail, the study will identify key challenges and opportunities for leveraging AI technologies to optimize processes and drive competitive advantage. The research will delve into various aspects of AI application in supply chain management, including demand forecasting, inventory management, logistics optimization, and customer relationship management. By analyzing case studies and empirical data, the project will assess the real-world impact of AI implementation on key performance indicators such as lead times, inventory turnover, and customer satisfaction levels. Furthermore, the study will evaluate the implications of AI adoption for workforce dynamics, organizational structure, and strategic decision-making in retail supply chain operations. Through a comprehensive literature review and empirical analysis, this research will contribute valuable insights to the growing body of knowledge on the intersection of AI and supply chain management in the retail industry. By shedding light on the benefits, challenges, and best practices associated with AI integration, the study aims to provide actionable recommendations for retail businesses seeking to harness the power of AI to optimize their supply chain operations and stay ahead in a rapidly evolving market environment.