ADVANCING US CRITICAL INDUSTRY SUPPLY CHAIN THROUGH AI AND MACHINE LEARNING-DRIVEN LOGISTICS, RISK MITIGATION, AND OPERATIONAL EXCELLENCE
DOI:
https://doi.org/10.48047/7864hq08Keywords:
Supply chain optimization, artificial intelligence, machine learning, demand forecasting, logistics optimization, risk mitigation, operational excellence, predictive analytics, supply chain resilience, DataCo datasetAbstract
US critical industry supply chains remain inefficient, as traditional forecasting only provides 65-70% accuracy, producing 18-22% late delivery rates alongside costly 15-20%logistics costs. From the dataset of 180,519 real-world supply chain transactions from DataCo Smart Supply Chain, we build and validate a holistic AI & ML framework to jointly optimize demand prediction, logistics efficiency, and operational resilience across supply chains together at once. We validate six complementary ML capabilities—LSTM for recognizing temporal patterns in late deliveries, XGBoost for predicting risk of late deliveries, Prophet for interpretable long-term trend-seasonality decomposition, anomaly detection for early warning of disruption, customer-product segmentation for targeted optimization, and network optimization for logistics decision-making—based on time-series cross-validation over the US geographic regions and product categories, that yield meaningful insights. We achieve 88-92% accuracy (26-38% error reduction over baseline) in demand forecasting, 96% accuracy in late delivery risk prediction with 94% sensitivity leading to 85%+ late delivery disruption prevention, optimization of logistics costs with average shipping costs decreasing by 17% ($200→$166 per order), and delivery speed increased by 15% (15.2→12.8 days), and 89% of supply disruption are detected 14-21 days in advance. Cumulative OTIF service levels of 96 %(78% baseline), 18-22% working capital reduction, 15-20% total logistics cost savings and $15-25M Year 1 estimated value based on analytic-optimized mid-sized supply networks. Between NE and MW markets prediction accuracy is reported at 91 to 94%, while critical infrastructure products (machinery, and semiconductors) are confirming at 92-94% accuracy. Human-AI collaboration for each phase facilitates phased realization of value. Results from this research prove that as an actionable blueprint, the growth of intelligent enterprise can be rapid with well-architected AI/ML frameworks and substantial simultaneous transformational value awaits in the three crucial logistics, risk and operational dimensions which this paper expands to various sectors such as semiconductor, pharmaceutical, energy, and defense with common forecasting challenges and disruption risks.
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