Business Intelligence Applications for Strategic Decision-Making in Automotive Supply Chains

Purpose: This chapter explores the strategic role of Business Intelligence (BI) in optimising decision-making and enhancing agility within the complex, globally integrated automotive supply chain. Design/methodology/approach: Using a qualitative approach, the chapter synthesises insights from academic literature, emerging technologies, and real-world case studies. It examines BI applications in demand forecasting, supplier performance, logistics optimisation, procurement, and production planning. The discussion also highlights BI's integration with Enterprise Resource Planning (ERP), Internet of Things (IoT), artificial intelligence (AI)/machine learning (ML), and blockchain technologies. Findings: BI empowers automotive firms with real-time visibility and predictive foresight, enabling them to navigate supply chain uncertainties and operational disruptions. Case studies illustrate how industry leaders use BI to streamline operations, reduce risks, and improve competitiveness. Despite its benefits, implementation challenges persist – such as fragmented data environments, cultural resistance, and limited digital maturity. Practical Implications: The chapter offers actionable insights for practitioners seeking to leverage BI for improved supply chain performance. It emphasises the strategic importance of embedding BI within broader digital transformation initiatives. Originality/value: By positioning BI as a strategic necessity rather than a supportive function, the chapter provides a comprehensive perspective on its transformative potential in the automotive sector. It also identifies emerging trends such as autonomous analytics and sustainability-oriented intelligence.