The dissertation explores the integration of artificial intelligence (AI) with supply chain management (SCM), highlighting both opportunities and challenges. It begins by discussing the current state of AI in SCM, identifying research gaps, and explaining foundational concepts like SCM complexities, the Supply Chain Operational Reference Model (SCOR), and AI algorithms in SCM.
Using a structured methodology, the study includes a detailed literature review, survey design, and expert interviews. Key findings reveal that AI adoption in SCM is driven by increased efficiency, improved decision-making, and cost reduction. However, barriers such as data quality issues, resistance to change, and lack of understanding and trust in AI are significant. To overcome these barriers, companies should involve all stakeholders, focus on data quality, and integrate AI solutions with existing processes.
The research emphasizes avoiding common mistakes in AI implementation, such as neglecting explainability and transparency, underestimating stakeholder involvement, and rushing into large-scale implementation without pilot projects. It concludes with actionable guidelines for businesses on developing, purchasing, and implementing AI solutions in SCM. These guidelines include prioritizing data quality, enhancing technical expertise, focusing on tangible business benefits, and upholding ethical standards.
The dissertation also discusses the implications for academia and industry, acknowledging limitations and suggesting future research directions. It highlights the potential of AI to revolutionize areas like demand forecasting, inventory optimization, and transportation through advanced predictive analytics and integration with technologies like IoT, blockchain, and robotics. The study underscores the importance of ethical AI implementation, transparency, and human-AI collaboration to address SCM challenges effectively.
Inhaltsverzeichnis (Table of Contents)
- Abstract
- Chapter 1: Introduction
- Chapter 2: Literature Review
- Chapter 3: Methodology
- Chapter 4: Results
- Chapter 5: Discussion
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This dissertation aims to explore the drivers, barriers, and social considerations surrounding the adoption of artificial intelligence (AI) in supply chain management (SCM). It investigates the potential benefits and limitations of AI integration within SCM, providing actionable guidelines for businesses.
- Drivers and benefits of AI adoption in SCM
- Barriers to AI adoption in SCM
- Social and ethical considerations of AI in SCM
- Methodological approaches to researching AI in SCM
- Practical guidelines for AI implementation in SCM
Zusammenfassung der Kapitel (Chapter Summaries)
Chapter 1: Introduction: This chapter sets the stage for the dissertation by introducing the context of AI integration within supply chain management. It highlights the significant opportunities and challenges presented by this evolving technological landscape, establishing the research problem and outlining the scope of the study. The introduction also defines key concepts like the Supply Chain Operational Reference Model (SCOR) and AI algorithms relevant to SCM, providing a foundational understanding for subsequent chapters. The chapter concludes by clearly stating the research questions and objectives that guide the investigation.
Chapter 2: Literature Review: This chapter presents a comprehensive review of existing literature on the intersection of AI and SCM. It systematically examines previous research, identifying key findings, gaps in knowledge, and areas where further investigation is needed. The review provides a critical analysis of existing studies on AI's impact on various aspects of SCM, such as efficiency, decision-making, and cost reduction, highlighting both successes and failures. This critical analysis lays the groundwork for the study's methodology and contributes to a nuanced understanding of the research field.
Chapter 3: Methodology: This chapter details the research methodology employed in the dissertation. It outlines the chosen research design, data collection methods (including surveys and expert interviews), and the criteria for participant selection. A rigorous description of the data analysis techniques is presented, ensuring transparency and reproducibility of the research process. This section also addresses potential limitations of the chosen methodology, promoting critical self-reflection and acknowledging potential biases or constraints that may influence the findings. The detailed explanation of the methodology enhances the study's credibility and provides a framework for understanding the subsequent results.
Chapter 4: Results: This chapter presents the key findings from the data analysis. It provides a detailed description of the survey results and expert interview insights, presenting the empirical evidence that supports the dissertation's arguments. The chapter systematically organizes and interprets the data, highlighting significant trends and patterns observed in the responses. The presentation of findings is structured and clear, facilitating a thorough understanding of the drivers, barriers, and social considerations identified in relation to AI adoption in SCM.
Chapter 5: Discussion: This chapter offers an in-depth analysis and interpretation of the results presented in Chapter 4. It connects the findings to the existing literature reviewed in Chapter 2, highlighting both congruences and discrepancies. The discussion section explores the implications of the findings for both academia and industry, offering insights into practical applications and suggesting strategies for overcoming identified barriers to AI adoption. This chapter synthesizes the research contributions and positions the study within the broader context of AI in SCM.
Schlüsselwörter (Keywords)
Supply Chain Management; SCM; artificial intelligence; AI; SCOR; AI adoption; data quality; stakeholder engagement; ethical considerations; pilot projects; efficiency; cost reduction; decision-making; barriers to AI adoption.
Häufig gestellte Fragen
What is the purpose of this document?
This document provides a comprehensive language preview of a dissertation related to the adoption of Artificial Intelligence (AI) in Supply Chain Management (SCM). It includes the table of contents, objectives and key themes, chapter summaries, and keywords.
What does the table of contents include?
The table of contents lists the main sections of the dissertation: Abstract, Chapter 1: Introduction, Chapter 2: Literature Review, Chapter 3: Methodology, Chapter 4: Results, and Chapter 5: Discussion.
What are the key objectives and themes of the dissertation?
The dissertation explores the drivers, barriers, and social considerations surrounding AI adoption in SCM. It investigates the potential benefits and limitations of AI integration within SCM and provides actionable guidelines for businesses.
What topics are covered in the Objectives and Key Themes section?
The section outlines the main topics, including: Drivers and benefits of AI adoption in SCM; Barriers to AI adoption in SCM; Social and ethical considerations of AI in SCM; Methodological approaches to researching AI in SCM; and Practical guidelines for AI implementation in SCM.
What does Chapter 1 (Introduction) cover?
Chapter 1 introduces the context of AI integration within SCM, highlighting opportunities and challenges. It establishes the research problem, outlines the scope of the study, defines key concepts like the Supply Chain Operational Reference Model (SCOR) and relevant AI algorithms, and states the research questions and objectives.
What is the focus of Chapter 2 (Literature Review)?
Chapter 2 presents a comprehensive review of existing literature on the intersection of AI and SCM. It identifies key findings, gaps in knowledge, and areas for further investigation, providing a critical analysis of AI's impact on various aspects of SCM.
What does Chapter 3 (Methodology) detail?
Chapter 3 outlines the research methodology employed in the dissertation, including the research design, data collection methods (surveys and expert interviews), participant selection criteria, and data analysis techniques. It also addresses potential limitations of the chosen methodology.
What kind of information can be found in Chapter 4 (Results)?
Chapter 4 presents the key findings from the data analysis, including detailed descriptions of the survey results and expert interview insights. It highlights significant trends and patterns observed in the responses related to the drivers, barriers, and social considerations of AI adoption in SCM.
What is the purpose of Chapter 5 (Discussion)?
Chapter 5 provides an in-depth analysis and interpretation of the results presented in Chapter 4. It connects the findings to existing literature, explores the implications for both academia and industry, offers insights into practical applications, and suggests strategies for overcoming barriers to AI adoption.
What are some of the key words associated with this dissertation?
The keywords include: Supply Chain Management; SCM; artificial intelligence; AI; SCOR; AI adoption; data quality; stakeholder engagement; ethical considerations; pilot projects; efficiency; cost reduction; decision-making; barriers to AI adoption.
- Citation du texte
- Dr. Johannes Hangl (Auteur), 2025, Drivers, Barriers and Social Considerations for AI Adoption in Supply Chain Management, Munich, GRIN Verlag, https://www.grin.com/document/1519036
