Strategic Decision-Making Dynamics in AI-Driven Supply Chain Management: A Case Study of the Automotive Sector

Abstract

The increasing integration of artificial intelligence (AI) into supply chain management (SCM) presents organizations with both unprecedented opportunities and significant challenges. This empirical study explores the dynamic processes underlying strategic decision-making in AI-driven supply chain frameworks, focusing specifically on the automotive sector. Employing a mixed-methods approach, we collected qualitative data through interviews with 30 industry experts and quantitative data from 150 automotive companies, analyzed using structural equation modeling (SEM). Our findings reveal that while AI enhances data accuracy and operational efficiency, it also introduces complexities in decision-making processes that can lead to resistance among stakeholders. This research contributes to a nuanced understanding of the interplay between AI technologies and strategic decision-making in SCM, highlighting critical factors that influence effective implementation and adoption. The study ultimately offers actionable insights for practitioners seeking to navigate the evolving landscape of AI integration in supply chains.