Dynamic Inventory Replenishment with Reinforcement Learning in Managing E-Fulfilment Centres

The demand of retail e-commerce has been rapidly growing due to the digitalization and the COVID-19 pandemic, and thus, the stress on e-fulfilment services continues to increase nowadays. To fulfil daily customers’ orders, effective inventory replenishment is of the essence in order to strike a balance between inventory management costs and service level. This paper describes an enhanced inventory replenishment approach by using reinforcement learning to deal with non-stationary and uncertain demand from customers. The proposed approach relaxes the assumption of stationary demand distribution considered in typical inventory models. Conventional policies derived from such models cannot guarantee optimal re-order quantities, when demand distribution is non-stationary over time. Consequently, reinforcement learning is adopted in the proposed approach to improve feasible solutions continuously in a dynamic business environment. In comparison to the conventional base stock policy, our proposed approach provides cost saving opportunities ranging from 28.5 to 41.3% in a simulated environment. It is found that the value of using data-driven solution approaches to deal with the practical inventory management problem is effective.