Supplier Relationship Management in an AI enabled Supply Chain
PAYBACK Punkte
0 °P sammeln!
Doctoral Thesis / Dissertation from the year 2025 in the subject Business economics - Customer Relationship Management, CRM, grade: B, , language: English, abstract: 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, foster...
Doctoral Thesis / Dissertation from the year 2025 in the subject Business economics - Customer Relationship Management, CRM, grade: B, , language: English, abstract: 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.
Andere Kunden interessierten sich für
