Bayesian Networks for Supply Chain Resilience in SAP’s Intelligent EV Production Systems

Electric vehicle (EV) supply chains are vulnerable to disruptions from component shortages, geopolitical tensions, and demand volatility. This research proposes a Bayesian network framework integrated with SAP Ariba and SAP S/4HANA to model and mitigate supply chain risks, optimizing procurement and demand forecasting. By leveraging probabilistic modeling, the framework enhances supplier reliability and inventory management. A dataset from an EV manufacturer's supply chain, spanning 12,000 procurement records, 8,000 supplier interactions, and 5 million sensor logs, was analyzed, achieving a 20% improvement in supply chain stability and a 25% reduction in inventory costs. Visualizations, including risk probability graphs and supplier reliability heatmaps, support strategic decision-making, establishing SAP as a leader in automotive supply chain resilience.