Adaptive inventory control and bullwhip effect analysis for supply chains with non-stationary demand

Abstract

In this paper, two adaptive inventory control models, i.e. centralized and decentralized respectively, for a multi-echelon multi-cycle supply chain consisting of one supplier and one retailer with non-stationary stochastic demand were established. In the centralized model, the vendor managed inventory replenishment policy was used by the supplier and the retailer didn't keep any stock. An improved exponential smoothing method was used by the supplier to forecast the future demand. The EOQ model was used by the supplier to determine the replenishment quantity for the retailer and an adaptive approach was used by the supplier to determine his safety stock to against demand fluctuation. An reinforcement learning algorithm was adopted to select an proper safety factor according to the stochastic demand. On the contrary, in the decentralized model, both the supplier and the retailers hold their own inventory and safety stock for themselves respectively. That is, they control their own inventory independently. In both cases, the aim is to satisfy the given target service level predefined. In our simulation study, two types of demand patterns, stationary and non-stationary demand, are considered respectively. The bullwhip effect generated in the course of forecasting and processing of demand information were analyzed. The results show that the proposed method can satisfy the given service level and mitigate the bullwhip effect to some extent.