Modeling Resilient and Sustainable Supply Chain Networks with AI: A Comprehensive Approach to Designing Robust Supply Systems in the USA

Abstract

Between 2020 and 2024, U.S. supply chain disruptions generated over $4.2 trillion in cumulative economic losses, while supply chain operations simultaneously accounted for 60% of corporate carbon emissions nationally. With 79% of manufacturers citing supply chain risk as their primary operational threat, conventional static frameworks — exhibiting 3.1-month average recovery times and cascade failure rates of 67% — prove equally inadequate for resilience and sustainability imperatives. This study presents an empirically validated, AI-driven framework integrating Graph Neural Networks, Reinforcement Learning, digital twin simulation, and probabilistic forecasting across 14,800 U.S. supplier relationships spanning six industry sectors. Sustainability objectives — including Scope 3 emissions reduction, circular economy routing, and ESG supplier scoring — are embedded as co-optimization targets alongside traditional resilience metrics. GNN-based failure prediction achieved 87.3% accuracy 14–21 days before operational failure, while AI-optimized green routing reduced logistics carbon intensity by 26.3% alongside a 19.4% cost reduction. Digital twin deployments produced Time-to-Recover reductions of 41%, and NLP risk-sensing flagged disruptions 18.3 days ahead of impact. Across 31 enterprise deployments, AI-augmented systems reduced disruption costs by 31.2%, lowered emissions per shipment by 22.1%, and delivered a 3.7x ROI over 36 months, establishing AI as the dual engine of supply chain resilience and environmental sustainability.