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.

Thesis Overview

The project titled "Adoption of Artificial Intelligence in Supply Chain Management: Opportunities and Challenges" aims to explore the potential benefits and obstacles associated with integrating artificial intelligence (AI) technologies in supply chain management practices. In recent years, AI has been increasingly recognized as a transformative tool in various industries, offering opportunities to streamline operations, enhance decision-making processes, and optimize resource utilization. Within the realm of supply chain management, AI holds promise in revolutionizing traditional practices by introducing automation, predictive analytics, and real-time data processing capabilities. The research seeks to provide a comprehensive overview of the current landscape of AI adoption in supply chain management, highlighting the key opportunities it presents for organizations seeking to improve efficiency, reduce costs, and enhance overall performance. By examining case studies and industry best practices, the study aims to showcase successful implementations of AI technologies in supply chain operations and their impact on business outcomes. However, alongside the potential benefits, the project also aims to address the challenges and barriers that organizations may face when implementing AI in supply chain management. These challenges may include issues related to data privacy and security, integration with existing systems, workforce readiness, and the need for specialized expertise in AI technologies. By identifying these challenges, the research aims to provide insights into how organizations can overcome barriers to successful AI adoption in supply chain management. Overall, the project seeks to contribute to the existing body of knowledge on the adoption of AI in supply chain management by offering a comprehensive analysis of the opportunities and challenges associated with integrating AI technologies into supply chain practices. By providing practical recommendations and insights for organizations looking to leverage AI for supply chain optimization, the research aims to offer valuable guidance for decision-makers seeking to harness the full potential of AI in transforming supply chain management practices.