PIML-Driven Supply Chain Disruption Estimation and Capacity Sharing Optimization

Global supply chains are increasingly vulnerable to disruptions caused by pandemics, natural disasters, and geopolitical instability. Traditional machine learning approaches often underperform when data are limited or when disruption patterns differ from historical cases. To address these challenges, this study develops a Physics Informed Machine Learning (PIML) framework that embeds physical and operational constraints into the predictive process, including inventory balance, production capacity, and scheduling relationships. This ensures both predictive accuracy and physical consistency when estimating upstream production downtime. Based on these predictions, the study further introduces a capacity sharing outsourcing algorithm that jointly considers the production schedules of multiple firms and evaluates the loss reduction of disruption affected firms when selecting optimal products for delegated production. Simulation experiments show that the proposed framework reduces the losses of production halted firms by more than sixty percent and significantly accelerates recovery through coordinated capacity sharing. This research advances the application of physics informed artificial intelligence in supply chain disruption management and provides practical guidance for building resilient and sustainable industrial systems in line with Industry 5.0 and Sustainable Development Goal 9 (SDG9).