Optimizing strategic safety stock placement in supply chains with clusters of commonality

Multiechelon inventory optimization is increasingly being applied by business users as new tools expand the class of network topologies that can be optimized. In this paper, we formalize a topology that we call networks with clusters of commonality (CoC), which captures a large class of real-world supply chains that contain component commonality. Viewed as a modified network, a CoC network is a spanning tree where the nodes in the modified network are themselves maximal bipartite subgraphs in the original network. We first present algorithms to identify these networks and then present a single-state-variable dynamic program for optimizing safety stock levels and locations. We next present two reformulations of the dynamic program that significantly reduce computational complexity while preserving the optimality of the resulting solution. This work both incorporates arbitrary safety stock cost functions and makes possible optimizing a large class of practically useful but previously intractable networks. It has been successfully applied at several Fortune 500 companies, including the recent Edelman finalist project at Hewlett Packard described in detail in Billington et al.