Supplier Relationship Management (SRM) is pivotal to modern manufacturing supply chains, enabling operational success within intricate supplier networks. Yet, traditional SRM, reliant on manual and reactive processes, falters amid global economic volatility, as evidenced by disruptions like the COVID-19 pandemic and the 2021 Suez Canal obstruction. Artificial Intelligence (AI) offers transformative potential to advance SRM, fostering proactive, resilient, and collaborative frameworks. However, its adoption poses challenges, including high costs, privacy concerns, algorithmic bias, and risks to trust, which can undermine its efficacy. This dissertation investigates how AI can be strategically integrated into SRM to enhance manufacturing supply chains while balancing technological innovation with relational and ethical considerations.
This dissertation advances supply chain management by redefining SRM through a strategic, AI-enabled lens, offering scholars a theoretical extension and practitioners an actionable roadmap. It bridges operational efficiency with relational trust, addressing ethical gaps, though achieving full equity requires further exploration. Limitations, including partial reliance on primary data and a manufacturing focus, are mitigated by recommendations for future research, such as quantitative validation and cross-sectoral applications. Ultimately, this study positions SRM as a proactive discipline in an AI-driven era, providing a robust foundation for navigating supply chain complexity.
Table of Contents
- Chapter 1: Introduction
- Chapter 2: Literature Review
- Chapter 3: Methodology
- Chapter 4: Case Studies
- Chapter 5: Framework
- Chapter 6: Results & Impact
- Chapter 7: Discussion
- Chapter 8: Innovative Supplier Relationship Management in Crane Services Procurement
Objectives and Key Themes
This dissertation investigates the strategic integration of Artificial Intelligence (AI) into Supplier Relationship Management (SRM) to enhance manufacturing supply chains. It aims to balance technological innovation with relational and ethical considerations. The research employs a mixed-methods approach, combining literature review, empirical case studies, and the development of a novel framework. * The impact of AI on SRM efficiency and resilience. * Relational and ethical challenges of integrating AI into SRM. * Development and evaluation of an AI-enhanced SRM framework. * The role of AI in fostering collaboration and trust in supplier relationships. * Ensuring equity and ethical considerations within AI-driven SRM.Chapter Summaries
Chapter 1: Introduction: This chapter introduces Supplier Relationship Management (SRM) as a strategic approach to optimizing supplier interactions within supply chains, emphasizing its critical role in manufacturing. It highlights the limitations of traditional, reactive SRM approaches in the face of global disruptions and underscores the transformative potential of AI in creating proactive, technology-driven SRM. The chapter identifies the challenges of AI integration, including high costs, trust erosion, and ethical concerns, setting the stage for the dissertation's investigation into a strategic AI-enhanced SRM framework that balances operational efficiency with relational trust and ethical considerations.
Chapter 2: Literature Review: (This section would contain a summary of the literature review chapter, which is not provided in the excerpt. A placeholder is included below.) This chapter would comprehensively review existing literature on Supplier Relationship Management, Artificial Intelligence in supply chains, and the intersection of the two. It would likely examine various theoretical frameworks relevant to SRM, AI technologies used in supply chain optimization, and the ethical and relational implications of AI adoption. The review would form the foundation for the research methodology and framework developed in subsequent chapters.
Chapter 3: Methodology: (This section would contain a summary of the methodology chapter, which is not provided in the excerpt. A placeholder is included below.) This chapter would detail the research design employed in the study, including the mixed-methods approach combining qualitative and quantitative data. It would describe the data collection methods used, such as case studies and potentially surveys or interviews. The chapter would also justify the chosen methods and address potential limitations of the research design.
Chapter 4: Case Studies: (This section would contain a summary of the case studies chapter, which is not provided in the excerpt. A placeholder is included below.) This chapter presents empirical case studies, starting with John Deere and continuing with analyses of Toyota and an SME. The studies would provide practical illustrations of how AI can be integrated into SRM and would likely examine the impact on various metrics such as procurement cycle times, supplier diversification, supplier satisfaction, and recovery times. The aim is to demonstrate the applicability and scalability of the proposed framework across different organizational contexts and sizes.
Chapter 5: Framework: (This section would contain a summary of the framework chapter, which is not provided in the excerpt. A placeholder is included below.) This chapter details the proposed AI-Enhanced SRM Triad, a novel framework integrating AI-driven optimization, human-centric governance, and ethical and inclusive design. This framework aims to provide a structured solution for incorporating AI into SRM while addressing the identified challenges related to trust, equity, and ethical considerations. The chapter would elaborate on the components of the framework and explain how it works in practice.
Chapter 6: Results & Impact: (This section would contain a summary of the results and impact chapter, which is not provided in the excerpt. A placeholder is included below.) This chapter presents the key findings from the case studies and empirical analysis of the proposed framework. It analyzes the impact of AI-enhanced SRM on key performance indicators (KPIs), such as cost efficiency, resilience, and supplier diversity. The results would likely demonstrate the benefits of AI integration while acknowledging any limitations or unintended consequences.
Chapter 7: Discussion: (This section would contain a summary of the discussion chapter, which is not provided in the excerpt. A placeholder is included below.) This chapter interprets and discusses the findings presented in Chapter 6, placing them within the broader context of existing research. It would explore the implications of the research for both theory and practice, drawing connections to the literature review and highlighting the significance of the study's contributions. The chapter would also address any limitations of the research and suggest directions for future research.
Chapter 8: Innovative Supplier Relationship Management in Crane Services Procurement: (This section would contain a summary of Chapter 8, which is not provided in the excerpt. A placeholder is included below.) This chapter likely provides a specific case study focusing on the application of the proposed AI-enhanced SRM framework within the context of crane services procurement. It could illustrate how the framework is implemented in a specific industry and highlight any unique challenges or opportunities presented by that sector.
Keywords
Supplier Relationship Management (SRM), Artificial Intelligence (AI), Supply Chain Management, AI-enhanced SRM, Manufacturing, Resilience, Collaboration, Ethics, Equity, Predictive Analytics, Small and Medium Enterprises (SMEs), Trust, Algorithmic Bias, Case Studies, Framework.
Frequently asked questions
What is the purpose of this document?
This document is a language preview from a publishing company, providing an overview of a dissertation or research paper focused on Artificial Intelligence (AI) integration into Supplier Relationship Management (SRM). It includes a table of contents, objectives, key themes, chapter summaries, and keywords.
What topics are covered in the Table of Contents?
The table of contents lists the following chapters: Introduction, Literature Review, Methodology, Case Studies, Framework, Results & Impact, Discussion, and a specific case study on Innovative Supplier Relationship Management in Crane Services Procurement.
What are the main objectives and key themes of the research?
The dissertation investigates the strategic integration of AI into SRM to enhance manufacturing supply chains. It aims to balance technological innovation with relational and ethical considerations. Key themes include:
- The impact of AI on SRM efficiency and resilience.
- Relational and ethical challenges of integrating AI into SRM.
- Development and evaluation of an AI-enhanced SRM framework.
- The role of AI in fostering collaboration and trust in supplier relationships.
- Ensuring equity and ethical considerations within AI-driven SRM.
What does Chapter 1 (Introduction) cover?
Chapter 1 introduces SRM as a strategic approach to optimizing supplier interactions in manufacturing supply chains. It highlights the limitations of traditional SRM and the transformative potential of AI. It also identifies the challenges of AI integration, such as high costs, trust erosion, and ethical concerns.
What topics can be expected in the Literature Review chapter?
The Literature Review chapter would comprehensively review existing literature on Supplier Relationship Management, Artificial Intelligence in supply chains, and the intersection of the two. It would likely examine various theoretical frameworks relevant to SRM, AI technologies used in supply chain optimization, and the ethical and relational implications of AI adoption.
What would the methodology chapter likely cover?
The Methodology chapter would detail the research design, including the mixed-methods approach combining qualitative and quantitative data. It would describe the data collection methods used, such as case studies and potentially surveys or interviews.
What's the focus of the Case Studies chapter?
The Case Studies chapter presents empirical case studies, examining practical examples of how AI can be integrated into SRM, including case studies such as John Deere, Toyota, and an SME. It would likely examine the impact on various metrics such as procurement cycle times, supplier diversification, supplier satisfaction, and recovery times.
What is covered in the Framework chapter?
The Framework chapter details the proposed AI-Enhanced SRM Triad, integrating AI-driven optimization, human-centric governance, and ethical and inclusive design. It provides a structured solution for incorporating AI into SRM while addressing challenges related to trust, equity, and ethical considerations.
What kind of information is in the Results & Impact chapter?
The Results & Impact chapter presents the key findings from the case studies and empirical analysis of the proposed framework. It analyzes the impact of AI-enhanced SRM on key performance indicators (KPIs), such as cost efficiency, resilience, and supplier diversity.
What is the purpose of the Discussion chapter?
The Discussion chapter interprets and discusses the findings presented in the Results & Impact chapter, placing them within the broader context of existing research. It explores the implications of the research for both theory and practice.
What's the topic of the Crane Services Procurement chapter?
This chapter likely provides a specific case study focusing on the application of the proposed AI-enhanced SRM framework within the context of crane services procurement.
What are the keywords associated with this research?
The keywords include: Supplier Relationship Management (SRM), Artificial Intelligence (AI), Supply Chain Management, AI-enhanced SRM, Manufacturing, Resilience, Collaboration, Ethics, Equity, Predictive Analytics, Small and Medium Enterprises (SMEs), Trust, Algorithmic Bias, Case Studies, Framework.
- Arbeit zitieren
- Neji Isaac (Autor:in), 2025, Supplier Relationship Management in an AI enabled Supply Chain, München, GRIN Verlag, https://www.grin.com/document/1604267
