United States Air Force Supply Chain Management (SCM) processes are based upon repairable parts demand, such as Shop Replaceable Units (SRU) and Circuit Card Assemblies (CCA), and not Mean Time Between Failure (MTBF). The rule of repair being 75% of the asset cost drives many assets to be repaired many times over the lifecycle of the item. Specifically, if the estimated repair cost is < 75% of the acquisition cost, the asset will be routed for repair. Complex Electronic Warfare assets, which frequently cost $250K or more per SRU, may have CCAs with multiple layers and the repair process itself can, over time, introduce additional failure modes. This can have the effect of greatly reducing the MTBF over time. Additionally, less complex, lower cost assets might be better treated as non-repairable. If these assets were condemned and replaced with new assets versus being repaired, it might allow large life cycle cost savings in logistics sustainment and increase weapon system availability.The opportunity exists to develop an intelligent decision support tool to optimize the total life-cycle repair costs and repair time for a SCM asset utilizing predictive analytics for asset life cycle models that predicts MTBF and Remaining Useful Life (RUL).Supply Chain Management,MTBF,RUL,Predictive Analytics,Prognostics,Optimal Repair Determination,decision support tool,information reasoning
