An adaptive inventory control for a supply chain

Uncertainties inherent in customer demands make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunities or keeping excessive chain wide inventories. In this paper, two adaptive inventory control models, a centralized model and a decentralized one, are proposed for a supply chain consisting of one supplier and one retailers. The objective of the two models is to satisfy a target service level predefined for each retailer and to minimize the whole inventory cost. The inventory control parameters of the supplier and retailers are safety lead time and safety stocks, respectively. Unlike most extant inventory control approaches, modelling the uncertainty of customer demand as a statistical distribution is not a prerequisite in the two models. Instead, using a reinforcement learning technique called action reward method, the control parameters are designed to adaptively change as customer demand patterns changes. A simulation based experiment was performed to compare the performance of the two inventory control models.