This paper presents an approximation model for optimizing reorder points in one-warehouse N-retailerinventory systems subject to highly variable lumpy demand. The motivation for this work stems fromclose cooperation with a supply chain management software company, Syncron International, and oneof their customers, a global spare parts provider. The model heuristically coordinates the inventory systemusing a near optimal induced backorder cost at the central warehouse. This induced backorder costcaptures the impact that a reorder point decision at the warehouse has on the retailers’ costs, and decomposesthe multi-echelon problem into solving N + 1 single-echelon problems. The decomposition frameworkrenders a flexible model that is computationally and conceptually simple enough to beimplemented in practice.A numerical study, including real data from the case company, shows that the new model performsvery well in comparison to existing methods in the literature, and offers significant improvements tothe case company. With regards to the latter, the new model in general obtains realized service levelsmuch closer to target while reducing total inventory.