Managing inventory systems of slow-moving items

Slow‐moving demand patterns frequently occur with spare parts as well as items in decentralized retail supply chains with large assortments. These patterns are commonly called lumpy since they exhibit comparably high demand variation and a high fraction of zero‐demand events. In this paper, we examine two distribution‐based approaches to model lumpy demand processes for inventory control: (i) a generalized hurdle negative binomial model, and (ii) a worst‐case non‐parametric model that is derived using a test‐based approach. Considering a base stock inventory policy, we examine a set of lumpy time series from the industry to exemplify the suitability and benefit of the proposed approaches for managing inventory systems of slow‐moving items.