Aerospace products operate within highly complex, safety-critical environments and endure extended lifecycles, often spanning decades. Sustaining their operational value requires rigorous management of Safety, Reliability, and Availability (SRA), while global Environmental, Social, and Governance (ESG) mandates demand parallel progress toward sustainability goals. This paper introduces an AI-driven strategy that integrates these dual imperatives—Sustenance Management and Sustainability Management—within a unified Product Lifecycle (PLC) framework.
The proposed approach leverages Artificial Intelligence across five PLC phases: Generative Design, Detailed Design & Verification, Manufacturing & Industrialization, Operations & Maintenance, and End-of-Life Circularity. Anchored by a certified Digital Thread, this framework ensures seamless, auditable data flow from concept to disposal. Using Life-Limiting Parts (LLPs)—such as high-stress turbine discs—as a case study, the paper demonstrates how AI interventions enhance operational efficiency while reducing embedded carbon emissions. For example, Generative AI optimizes component geometry for performance and material efficiency, Physics-Informed Machine Learning (PIML) improves Remaining Useful Life (RUL) predictions for certification readiness, and predictive analytics extend Time-on-Wing (ToW), deferring Scope 3 emissions from replacement manufacturing. At end-of-life, AI-guided valuation of Used Serviceable Material (USM) enables circularity and compliance with ISO 14067 and ISO 14040/14044 standards.
The paper also discusses sustainability metrics such as Design Simulation Energy Intensity (DSEI) and the Sustainable AI Quotient (SAIQ) [25], to address the AI-energy paradox, ensuring that digital transformation remains net-positive for environmental stewardship. By positioning sustenance as the most immediate lever for sustainability, this AI-led framework delivers measurable improvements in lifecycle cost, operational resilience, and carbon footprint reduction. The discussion concludes with challenges in data governance, regulatory compliance, and model explainability, offering mitigation strategies for safe and scalable adoption.