Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement Learning

Adaptive and highly synchronized supply chains can avoid a cascading rise-and-fall inventory dynamic mitigate ripple effects caused by operational failures. This paper aims to demonstrate how deep reinforcement learning agent based on the proximal policy optimization algorithm synchronize inbound outbound flows support business continuity operating in stochastic nonstationary environment if end-to-end visibility is provided. The built upon Proximal Policy Optimization algorithm, which does not require hardcoded action space exhaustive hyperparameter tuning. These features, complimented with straightforward chain environment, give rise general task unspecific approach adaptive control multi-echelon chains. proposed compared base-stock policy, well-known method classic operations research theory. prevalent continuous-review systems. concludes statement that solution perform complex also postulates fully fledged digital twins as necessary infrastructural condition for scalable real-world applications.