Adoption of Artificial Intelligence in Supply Chain Management: Opportunities and Challenges

Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) into supply chain management represents a significant advancement in the way organizations optimize their operations and enhance efficiency. This thesis explores the opportunities and challenges associated with the adoption of AI in supply chain management. The research delves into the transformative potential of AI technologies in revolutionizing traditional supply chain processes, as well as the hurdles that organizations may face during the implementation phase. The study begins with a comprehensive introduction that sets the stage for understanding the background of AI in supply chain management. It identifies the problem statement surrounding the integration of AI technologies, highlights the objectives of the study, discusses the limitations and scope of the research, emphasizes the significance of the study, and outlines the structure of the thesis. Furthermore, key terms related to the topic are defined to provide clarity and understanding. The literature review in Chapter Two synthesizes existing research on AI in supply chain management, examining ten critical areas such as AI applications in inventory management, demand forecasting, logistics optimization, and supplier relationship management. The review provides insights into the current state of AI adoption in supply chains and identifies gaps for further exploration. Chapter Three focuses on the research methodology employed in this study. It outlines the research design, data collection methods, sampling techniques, data analysis procedures, and ethical considerations. Additionally, it discusses the theoretical framework guiding the research and justifies the selection of specific methodologies. Chapter Four presents a detailed discussion of the findings derived from the empirical research conducted. The chapter analyzes the opportunities that AI presents in enhancing supply chain visibility, reducing costs, improving decision-making processes, and enhancing customer satisfaction. Simultaneously, it addresses the challenges organizations may encounter, such as data security concerns, integration complexities, skill gaps, and ethical implications. Finally, Chapter Five encapsulates the conclusion and summary of the thesis. It synthesizes the key findings, discusses their implications for practice and research, and offers recommendations for organizations looking to adopt AI in their supply chain operations. The conclusion underscores the transformative potential of AI technologies in supply chain management while acknowledging the need for strategic planning and investment to overcome the associated challenges. In conclusion, this thesis sheds light on the evolving landscape of supply chain management through the adoption of AI technologies. By examining the opportunities and challenges of integrating AI into supply chains, this research provides valuable insights for practitioners, policymakers, and researchers seeking to leverage AI for enhanced operational efficiency and competitiveness in the digital era.