Research on Inventory Control Policies for Nonstationary Demand based on TOC

An effective inventory replenishment method employed in the supply chain is one of the key factors to achieving low inventory while maintaining high customer delivery performance. The state of demand process is often not directly observed by the decision maker. Thus, in many literatures, the inventory control problem is a composite-state, partially observed Markov decision process (POMDP), which is an appropriate model for a number of dynamic demand problems. In practice, managers often use certainty equivalent control (CEC) policies to solve such a problem. However, in reality, Theory of Constraints (TOC) has brought a practical control policy that almost always provides much better solutions for this problem than the CEC policies commonly used in practice. In this paper, we proposed three different inventory control policies based on TOC buffer management framework, and use simulation approach to compare them with traditional adaptive (s,S,T) policy. The computational results indicate how specific problem characteristics influence the performance of whole system and demonstrate the efficiency of the proposed control policy