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
CHAPTER 9: CONCLUSION
Research Objectives and Themes
This thesis investigates the strategic integration of Artificial Intelligence (AI) into Supplier Relationship Management (SRM) within manufacturing to foster resilience and collaboration while balancing operational efficiency with ethical and relational considerations.
- Transformation of traditional reactive SRM into proactive, AI-enabled processes.
- Development of the AI-Enhanced SRM Triad framework for manufacturing contexts.
- Analysis of the balance between AI-driven optimization, human-centric governance, and ethical design.
- Evaluation of AI’s impact on resilience, supplier diversity, and cost-efficiency.
- Mitigation of ethical risks such as algorithmic bias and the inclusion of small-to-medium enterprises (SMEs).
Excerpt from the Book
Chapter 1: Introduction
Supplier Relationship Management (SRM) is a strategic approach to optimizing supplier interactions, driving value creation, risk mitigation, and innovation within supply chains (Monczka et al., 2015). In manufacturing, SRM is critical due to complex supplier networks, just-in-time (JIT) delivery requirements, and the imperative for operational resilience. Historically, SRM relied on manual, transactional processes prioritizing cost reduction. However, global disruptions—such as the 2011 Japanese tsunami and the COVID-19 pandemic—exposed the limitations of these reactive approaches, underscoring the need for proactive, technology-driven SRM (Choi et al., 2020).
The emergence of artificial intelligence (AI), including machine learning (ML) and the Internet of Things (IoT), has revolutionized supply chain management. AI enables predictive analytics, real-time monitoring, and automated decision-making, significantly enhancing SRM’s efficiency and resilience. For instance, enterprises invest approximately $5 million annually in generative AI, with applications in supplier selection and inventory optimization (Bain & Company, 2024). Despite these advancements, integrating AI into SRM presents challenges, including high implementation costs, potential erosion of trust in supplier relationships, and ethical concerns such as algorithmic bias and transparency (Daugherty et al., 2021). This dissertation examines how AI can strategically enhance SRM in manufacturing supply chains while addressing these challenges to foster resilience, collaboration, and ethical integrity.
Summary of Chapters
CHAPTER 1: INTRODUCTION: This chapter outlines the background, problem statement, and objectives of applying AI to SRM in manufacturing to move beyond reactive practices.
CHAPTER 2: LITERATURE REVIEW: This chapter synthesizes existing research on the evolution of SRM, the role of AI in supply chains, and the associated ethical and relational challenges.
CHAPTER 3: METHODOLOGY: This chapter details the mixed-methods, multiple-case study design used to collect and analyze data across diverse manufacturing contexts.
CHAPTER 4: CASE STUDIES: This chapter presents empirical insights from John Deere and other cases, highlighting AI's real-world impact and implementation hurdles.
CHAPTER 5: FRAMEWORK: This chapter proposes the AI-Enhanced SRM Triad, a structured framework integrating optimization, governance, and ethical design.
CHAPTER 6: RESULTS & IMPACT: This chapter quantifies the impact of the proposed Triad on resilience, collaboration, and equity metrics within manufacturing firms.
CHAPTER 7: DISCUSSION: This chapter synthesizes the study's findings, evaluating the efficacy of the Triad and discussing its theoretical and practical implications.
CHAPTER 8: INNOVATIVE SUPPLIER RELATIONSHIP MANAGEMENT IN CRANE SERVICES PROCUREMENT: This chapter examines a specialized procurement strategy that leverages underutilized resources to achieve significant cost savings.
CHAPTER 9: CONCLUSION: This chapter summarizes the dissertation's key findings, contributions to supply chain management, and recommendations for future research.
Keywords
Supplier Relationship Management, Artificial Intelligence, Manufacturing, Supply Chain Resilience, AI-Driven Optimization, Human-Centric Governance, Ethical Design, Machine Learning, IoT, Procurement, Predictive Analytics, SME Inclusion, Strategic Sourcing, Risk Mitigation, Digital Transformation
Frequently Asked Questions
What is the core focus of this dissertation?
The dissertation explores how to strategically integrate Artificial Intelligence into Supplier Relationship Management within the manufacturing sector to transition from reactive to proactive, resilient, and ethical supply chain operations.
What are the primary themes discussed in the work?
The work centers on three pillars: AI-driven operational optimization, the preservation of human-centric governance to maintain trust, and the implementation of ethical and inclusive design principles.
What is the main research objective?
The objective is to develop and validate the AI-Enhanced SRM Triad framework, which balances the efficiency gains of AI with the relational and ethical requirements of modern manufacturing supplier networks.
Which scientific methodology is employed?
The study utilizes a pragmatic, mixed-methods, multiple-case study design, triangulating secondary data from large industry leaders with planned primary data to ensure robust and practical findings.
What does the main body of the text cover?
The main body spans from the historical evolution of SRM and AI integration to the presentation of empirical case studies, the development of the Triad framework, and the final synthesis of performance results.
Which keywords define this research?
Key terms include Supplier Relationship Management, Artificial Intelligence, supply chain resilience, machine learning, ethical design, procurement, and strategic sourcing.
How does the proposed framework address SME concerns?
The framework includes an 'Ethical and Inclusive Design' pillar that advocates for bias audits and simplified tools to mitigate the exclusion of SMEs, which often struggle with the high costs of AI adoption.
What specific practical insight does the crane services case study offer?
The case demonstrates that SRM can move beyond mere cost-cutting by identifying underutilized organizational assets to create win-win partnerships, achieving over 30% savings in high-setup-cost procurement environments.
- Quote paper
- Neji Isaac (Author), 2025, Supplier Relationship Management in an AI enabled Supply Chain, Munich, GRIN Verlag, https://www.grin.com/document/1604267
