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

Automated summary

Adaptive and highly synchronized supply chains can avoid inventory dynamic fluctuations and mitigate ripple effects caused by operational failures. The proposed deep reinforcement learning agent based on the Proximal Policy Optimization algorithm demonstrates the ability to synchronize inbound and outbound flows in stochastic environments, offering a general approach to adaptive control in multi-echelon supply chains. The paper suggests that fully fledged supply chain digital twins are essential for scalable real-world applications of adaptive control strategies.