The Role of AI in Enhancing Supply Chain Resilience and Optimization: Applying Reinforcement Learning and Predictive Analytics to Manage Inventory and Mitigate Disruptions

Abstract

Global supply chains faced unprecedented disruptions from pandemics, geopolitical conflicts, and climate events, exposing critical vulnerabilities in traditional, efficiency-centric models. While artificial intelligence (AI) solutions had gained attention, existing research lacked comprehensive frameworks that effectively combined reinforcement learning (RL) and predictive analytics to address supply chain resilience holistically. This gap persisted as organizations continued relying on reactive strategies and static forecasting tools that proved inadequate in volatile operating environments. This study developed and validated an integrated AI framework designed to optimize inventory management and mitigate disruptions through the combined application of RL and predictive analytics. The research employed a mixed-methods approach, analyzing quantitative data from five multinational corporations alongside qualitative insights from 100 supply chain professionals. Reinforcement learning models, including Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), were trained on historical inventory records, while predictive analytics techniques such as ARIMA and LSTM neural networks were applied for demand forecasting and disruption prediction. The results demonstrated significant improvements in supply chain performance. The RL models reduced stockouts by 32.4% (p < 0.001) through dynamic inventory replenishment strategies. Predictive analytics achieved a mean absolute percentage error (MAPE) of 12.3% in disruption forecasting, outperforming traditional exponential smoothing methods by 15.2% (p = 0.008). Organizations implementing the integrated framework reported 50% faster decision-making during disruptions and 65.3% higher optimization success rates compared to non-adopters (χ² = 18.7, p < 0.001). These findings provided empirical evidence that combining RL with predictive analytics could transform supply chain operations from reactive to proactive systems. The study contributed to academic literature by establishing a validated framework for AI-driven resilience, while offering practitioners a scalable model for implementation. The results underscored the importance of workforce development to fully realize AI's potential in supply chain management, suggesting future research should explore human-AI collaboration dynamics in operational contexts.