🤖 AI Summary
UK automotive manufacturing relies heavily on just-in-time (JIT) production but faces dual challenges from demand volatility and supply disruptions. To address this, we propose the first adaptive inventory management framework integrating Bayesian inference with two-stage stochastic optimization, yielding a falsifiable stochastic learning–optimization model that establishes mathematical foundations and theoretical boundaries for AI-enhanced supply chain resilience. Our method unifies Bayesian dynamic updating, Monte Carlo simulation, and real-time decision optimization to enable online modeling of supply–demand uncertainty. In a 365-period simulation study, the framework reduces operational costs by 7.4% under stable conditions and improves performance by 5.7% during supply disruptions. Furthermore, it characterizes the applicability boundary of Bayesian conservatism under abrupt shocks. This work bridges a critical gap by providing the first quantitative validation of synergistic effects between AI and operations research methodologies.
📝 Abstract
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.