Speaker
Description
Life Cycle Assessment (LCA) is becoming a key requirement for space sustainability and ecodesign, yet the growing adoption of New Space development practices, characterized by agile iterations, accelerated schedules, and wide reliance on COTS based architectures, creates significant challenges for robust LCA implementation. Environmental data are often incomplete, unavailable, or inconsistently reported across product trees and subcontractor layers, particularly for major COTS suppliers in both space and ground segment platforms. In parallel, Integrated Product Support (IPS) and Integrated Logistic Support (ILS) bases manges lifecycle information.
This work proposes a structured approach to strengthen LCA data quality by integrating IPS/ILS frameworks, which already capture detailed lifecycle information such as configuration baselines, maintenance task analyses, reliability data, spares lifecycles, logistics resource flows, and operational duty cycles. Mapping these datasets to LCA inventory needs provides a powerful mechanism to enhance completeness, traceability, and automation in life cycle modelling, enabling earlier and more reliable ecodesign feedback.
We analyse the mapping between IPS/ILS datasets, particularly those structured under S3000L such as product trees, maintenance task analyses, logistics resource footprints, failure modes, and spare parts lifecycles, and the key inventory requirements of Life Cycle Assessment (LCA). This integration shows how enhanced IPS/ILS tools can deliver structured, high granularity data relevant to environmental indicators, from manufacturing impacts and operational emissions to end of life processes. At the same time, the approach highlights the need to extend LCA data provision requirements to major COTS manufacturers, ensuring that platform providers across both space and ground segments supply minimum environmental datasets necessary for credible assessments. By positioning IPS/ILS as the operational backbone for LCA, particularly in New Space environments where rapid development cycles and COTS based architectures often create significant data gaps, this methodology strengthens data completeness, traceability, and automation. The analysis demonstrates that IPS/ILS datasets already contain many parameters required for LCA, including mass breakdowns, manufacturing and replacement frequencies, operational energy consumption, logistics footprints, and end of life pathways.
Ultimately, it directly supports the objectives of Clean Space by enabling simplified LCA methodologies, greener technologies, and improved assessment of environmental impacts across mission phases, positioning IPS/ILS as a critical enabler of robust, data driven environmental performance evaluation for future space systems.