This article explains how the Army has been able to achieve historically high levels of performance in its tactical inventories of repair parts by applying a series of RAND-developed process and algorithm improvements. The Army’s tactical inventories are stored in mobile mini-distribution centers called Supply Support Activities that are part of each brigade-sized unit. Each SSA stocks repair parts and other supplies according to an Authorized Stockage List (ASL) tailored to the maintenance needs of the equipment specific to the unit, within storage constraints. In the late 1990s, the Army was dissatisfied with the performance of its tactical inventories and sponsored research to develop an improved algorithm for use in developing ASLs. In 1998, RAND Arroyo Center developed Dollar Cost Banding, an algorithm that added consideration of the relative costs of repair parts. DCB tied the decisions of what and how much to stock not only to the benefits produced but also to the resources required. Additionally, DCB adopted a new method for inventory quantity determination designed to deal much better with the highly variable demand for most parts, and it automated criteria for excluding some items from the ASL. After several successful pilots, the Army incorporated DCB into its policies and information systems for inventory management, making it possible for all Army units to use it to create their ASLs. The next step in improving tactical inventories was to incorporate detailed information about which repair parts were most critical to bringing broken equipment back to a ready condition. RAND developed the Equipment Downtime Analyzer to archive daily equipment maintenance reports and developed an improved and highly focused list of critical parts. The EDA data was combined with the DCB algorithm to create an approach called Enhanced Dollar Cost Banding (EDCB), which was piloted in 2002 and then applied in Southwest Asia (SWA) starting in 2004. Using feedback from implementation efforts, it was continuously improved. Then, to address process issues and fully leverage EDCB’s potential, the Army instituted a pilot policy in SWA in 2006 that shifted from SSA personnel determining their own stockage to having a centralized expert team do so, with most recommendations implemented automatically, without review by unit personnel. This has sped the ASL review process, reduced workload in the units, and improved performance consistency across SSAs. RAND continued research on the tactical inventory developed a new model for computing ASL recommendations called the Inventory Readiness Optimizer with Constraints (IROC), which optimizes ASL effectiveness subject to any number of constraints through mixed integer programming. Thus, in developing an optimal ASL, IROC, which has been successfully piloted for one brigade, is able to take into account still more valuable information, such as the number and volume of storage locations in a specific SSA and the workload associated with changing to a new ASL. Currently, the expert ASL team uses IROC to fine tune the EDCB algorithm. As a result of these cumulative improvements, the performance of Army tactical inventories has risen to an all time high.