Supply chain disruptions and volatile demand pose significant challenges to the UK automotive industry, which relies heavily on Just-In-Time (JIT) manufacturing. While qualitative studies highlight the potential of integrating Artificial Intelligence (AI) with traditional optimization, a formal, quantitative demonstration of this synergy is lacking. This paper introduces a novel stochastic learning-optimization framework that integrates Bayesian inference with inventory optimization for supply chain management (SCM). We model a two-echelon inventory system subject to stochastic demand and supply disruptions, comparing a traditional static optimization policy against an adaptive policy where Bayesian learning continuously updates parameter estimates to inform stochastic optimization. Our simulations over 365 periods across three operational scenarios demonstrate that the integrated approach achieves 7.4\% cost reduction in stable environments and 5.7\% improvement during supply disruptions, while revealing important limitations during sudden demand shocks due to the inherent conservatism of Bayesian updating. This work provides mathematical validation for practitioner observations and establishes a formal framework for understanding AI-driven supply chain resilience, while identifying critical boundary conditions for successful implementation.

For the JIT model to work, the quality and supply of raw materials, the production of goods, and the customer demand for them must remain in alignment. If any one of the links in the chain breaks, stalls, or falls out of sync, the impact on the supply chains that crisscross the world can be felt immediately. For companies, unable to deliver on orders in a timely fashion, they risk losing not only efficiency gains but also brand credibility, market share, and revenue. Now, companies are seeking new ways of managing their supply chains that offer greater flexibility and transparency. In the automotive sector, some companies including Nissan and JIT pioneer Toyota are increasing chip inventory levels, while others including Volkswagen and Tesla are trying to secure their own supplies of rare metals.