Enhancing Supply Chain Resilience through AI: Leveraging Deep Reinforcement Learning and Predictive Analytics

Abstract

This research paper investigates the role of artificial intelligence (AI) in enhancing supply chain resilience, focusing on the integration of deep reinforcement learning (DRL) and predictive analytics. We propose a novel framework that utilizes DRL to optimize decision-making processes in real-time while employing predictive analytics to foresee potential disruptions. The study begins by examining current challenges in supply chain management, including demand fluctuations, supply disruptions, and logistical inefficiencies. Through a systematic review of literature, we identify gaps in existing methodologies, particularly in their ability to adapt dynamically to unforeseen events. Our proposed framework is tested against traditional supply chain models using a series of simulated experiments, reflecting various disruption scenarios. The results demonstrate a significant improvement in resilience, marked by a 30% decrease in recovery time and a 25% reduction in associated costs. Furthermore, the integration of DRL with predictive analytics enhances the supply chain’s ability to anticipate and adapt to changes, increasing overall operational efficiency. This study contributes to the field by providing empirical evidence on the efficacy of AI-driven solutions and offers practical insights for supply chain managers aiming to bolster their systems against future uncertainties.