This paper presents an approximation model for optimizing reorder points in one-warehouse N-retailer inventory systems subject to highly variable lumpy demand. The motivation for this work stems from close cooperation with a supply chain management software company, Syncron International, and one of their customers, a global spare parts provider. The model heuristically coordinates the inventory system using a near optimal induced backorder cost at the central warehouse. This induced backorder cost captures the impact that a reorder point decision at the warehouse has on the retailers’ costs, and decomposes the multi-echelon problem into solving N +1 single-echelon problems. The decomposition framework renders a flexible model that is computationally and conceptually simple enough to be implemented in practice. A numerical study, including real data from the case company, shows that the new model performs very well in comparison to existing methods in the literature, and offers significant improvements to the case company. With regards to the latter, the new model in general obtains realized service levels much closer to target while reducing total inventory. 
A model for heuristic coordination of real life distribution inventory systems with lumpy demand