Bridging Theory and Practice: A Stochastic Learning-Optimization Model for Resilient Automotive Supply Chains

TL;DR

This paper presents a stochastic learning-optimization model combining Bayesian inference with inventory management to enhance supply chain resilience in the automotive industry, demonstrating cost savings and adaptive capabilities.

Contribution

It introduces a novel integrated framework that formalizes the synergy between AI and traditional optimization for resilient supply chain management.

Findings

Achieves 7.4% cost reduction in stable environments

Attains 5.7% improvement during supply disruptions

Reveals limitations during sudden demand shocks

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.