Development of an Integrated ASRP-LCSP Linkage Model for Defense Life Cycle Quality Management: A Data-Driven PDCA Feedback Approach

Purpose: This research addresses the critical structural disconnection between the Ammunition Stockpile Reliability Program (ASRP) and the Life Cycle Sustainment Plan (LCSP) within the framework of Total Life Cycle Systems Management (TLCSM). By identifying the information silos and the breakdown of the PDCA (Plan-Do-Check-Act) cycle in current missile management, this study aims to develop a data-driven quality governance model that optimizes both operational readiness and life cycle costs (LCC).Methods: We proposed an Integrated Closed-loop Linkage Model structured into three functional layers: a Total Life Cycle Data Hub, an Adaptive Reliability Analysis Engine, and a Policy Feedback Layer. The model integrates fragmented data from development, production, and operation phases into a single pipeline. To validate its practical utility, a scenario-based economic impact analysis was conducted, comparing traditional static maintenance plans with the proposed dynamic, data-driven service life extension.Results: The study demonstrated that empirical reliability data from ASRP can serve as a digital trigger to update LCSP maintenance strategies dynamically. The results indicate that extending the missile's life from 10 to 15 years can achieve a 33.3% extension in the total operational lifecycle period (from 30 to 40 years). This extension creates a significant denominator effect that substantially reduces the annualized budget burden by spreading high fixed costs, such as initial acquisition and multi-stage overhauls, over the extended operational lifespan while potentially eliminating one major overhaul cycle.Conclusion: This research provides a foundational framework for shifting defense quality management from passive status monitoring to proactive, data-driven strategic decision support. The proposed integrated architecture not only enhances technical safety but also offers a logical basis for developing future AI-powered Remaining Useful Life (RUL) prediction models, ensuring maximum budget efficiency and combat readiness.