Optimizing Supply Chain Efficiency Using Reinforcement Learning and Predictive Analytics
Abstract
Supply chain management faces challenges related to demand forecasting, inventory optimization, and logistics planning. This paper proposes a hybrid approach combining reinforcement learning (RL) with predictive analytics to enhance decision-making in supply chain operations. The RL agent learns optimal strategies for inventory replenishment and distribution, while predictive models forecast demand and supply variations. Experiments on retail and manufacturing datasets demonstrate significant improvements in cost reduction, lead time, and service levels, showcasing the potential of AI-driven supply chain optimization.
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