Predicting Obsolescence Patterns in DMSMS for Critical Systems

Abstract

Diminishing Manufacturing Sources and Material Shortages (DMSMS) continue to challenge the sustainment of defense-critical systems. This study investigates predictive methods for identifying obsolescence patterns in radar and communications components using historical attrition data and life cycle phase indicators. Drawing on 1,821 observations from 2009 to 2019, the research applies regression-based modeling to forecast component availability and risk exposure. Key drivers of obsolescence include vendor consolidation, technology refresh cycles, and supply chain instability. The model supports early detection of obsolescence threats and informs proactive sustainment planning. By integrating obsolescence intelligence into acquisition and logistics workflows, the framework offers a practical tool for improving readiness and reducing life cycle costs. While the model demonstrates utility across select platforms, further refinement is needed to enhance scalability and precision across broader portfolios. This work contributes to ongoing efforts to operationalize obsolescence forecasting and strengthen long-term sustainment strategies.