Bridging Theory and Practice: A Stochastic Learning-Optimization Model for Resilient Automotive Supply Chains
This work addresses supply chain resilience for the automotive industry, providing a formal quantitative demonstration that is incremental in integrating AI with traditional optimization.
The paper tackles the challenge of supply chain disruptions and volatile demand in the UK automotive industry by introducing a stochastic learning-optimization framework that integrates Bayesian inference with inventory optimization. The result is a 7.4% cost reduction in stable environments and a 5.7% improvement during supply disruptions, though it shows limitations during sudden demand shocks.
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.