Thesis Abstract

Abstract
The integration of artificial intelligence (AI) technologies in supply chain management has emerged as a strategic imperative for enhancing operational efficiency and competitiveness in the retail industry. This thesis investigates the implementation of AI in supply chain management through a case study approach focused on the retail sector. The study explores how AI applications such as machine learning, predictive analytics, and robotic process automation can optimize various aspects of the supply chain, including demand forecasting, inventory management, logistics, and customer service. The research methodology involves a combination of qualitative and quantitative approaches, including interviews with industry experts, analysis of case studies, and empirical data collection from the selected retail organization. Chapter 1 provides an introduction to the research topic, presenting the background of the study, problem statement, research objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter 2 offers a comprehensive review of the literature on AI in supply chain management, covering topics such as the benefits and challenges of AI adoption, best practices, and current trends in the retail industry. Chapter 3 outlines the research methodology, including the research design, data collection methods, sampling techniques, data analysis procedures, and ethical considerations. Chapter 4 presents a detailed discussion of the findings from the case study analysis, highlighting the key insights and implications for practice. The findings reveal the significant impact of AI technologies on supply chain efficiency, cost reduction, customer satisfaction, and competitive advantage in the retail sector. The discussion also addresses potential barriers to AI implementation and offers recommendations for successful integration into supply chain operations. Finally, Chapter 5 provides a conclusion and summary of the thesis, reiterating the research objectives, summarizing the key findings, and discussing the practical implications of the study. The conclusion also identifies areas for future research and outlines recommendations for retail organizations looking to leverage AI in supply chain management. Overall, this thesis contributes to the growing body of knowledge on the application of AI in supply chain management and offers valuable insights for industry practitioners, researchers, and policymakers seeking to enhance supply chain performance in the retail sector through intelligent technologies.

Thesis Overview

The project titled "Implementing Artificial Intelligence in Supply Chain Management: A Case Study in the Retail Industry" aims to explore the application of artificial intelligence (AI) in optimizing supply chain management processes within the retail sector. With the rapid advancements in technology, the integration of AI tools and techniques offers significant potential for enhancing operational efficiency, reducing costs, and improving decision-making in supply chain operations. The research will begin with an introduction to the topic, providing a background of the study to establish the context for the research. The problem statement will highlight the existing challenges and inefficiencies in traditional supply chain management practices within the retail industry, emphasizing the need for innovative solutions. The objectives of the study will outline the specific goals and outcomes the research aims to achieve, focusing on the implementation of AI technologies to address supply chain issues. Limitations of the study will be acknowledged to provide a transparent view of the potential constraints and boundaries of the research. The scope of the study will define the boundaries within which the research will operate, specifying the specific aspects of supply chain management and AI implementation that will be covered. The significance of the study will be emphasized to highlight the potential contributions of the research to both academia and industry, emphasizing the importance of leveraging AI in supply chain management. The structure of the thesis will outline the organization of the research work, providing a roadmap for the reader to navigate through the different sections and chapters. Definitions of key terms and concepts will be provided to ensure clarity and understanding of the terminology used throughout the research. The literature review will delve into existing research and scholarly works related to AI in supply chain management, exploring the theoretical foundations and practical applications of AI technologies in optimizing supply chain processes. Key themes and trends in the literature will be identified to provide a comprehensive overview of the current state of research in the field. The research methodology section will detail the approach and methods used to conduct the study, including data collection techniques, research design, and analysis procedures. The chapter will also discuss the sampling strategy, data sources, and analytical tools employed to achieve the research objectives. The discussion of findings chapter will present the results of the research, analyzing the impact of AI implementation on supply chain management within the retail industry. Key insights, trends, and implications of the findings will be discussed, highlighting the practical implications for retail businesses looking to adopt AI technologies in their supply chain operations. Finally, the conclusion and summary chapter will provide a comprehensive overview of the research findings, reiterating the key contributions, implications, and recommendations arising from the study. The chapter will also discuss the limitations of the research and propose avenues for future research in the field of AI-driven supply chain management in the retail industry.