From Strategic Technology Gaps to Architectural Trade-Offs: An Integrated Systems Engineering Framework

The transition towards a New Space economy requires a paradigm shift in early-phase Systems Engineering. Traditional Phase 0/A conceptual design processes struggle to balance technical performance with emerging commercial drivers. Consequently, there is an increasing need for adaptable tools that enable engineers to manage fast-paced priority shifts without losing design coherence. This paper presents a dynamic, parametrically linked SE framework designed as a decision-support model. It establishes a traceable "golden thread" from high-level strategic objectives, such as technology gaps identified in the ISECG Global Exploration Roadmap, down to rigorous system architecture selection. The framework employs a four-phase methodology filtering broad strategic needs into concrete design choices. First, capability gaps are weighted and cross-correlated to mission statements to extract prioritized Operational Capabilities. Second, an evaluation framework defines Key Performance Parameters, mission constraints, and strategic drivers (e.g., scalability, lifecycle cost). Third, a morphological analysis using system characteristics as orthonormal dimensions generates a broad trade-space of candidate architectures, pruned via viability screening. Finally, quantitative baselines for the shortlisted architectures are computed. To ensure mathematical consistency and mitigate subjective bias, the framework integrates the Analytic Hierarchy Process (AHP) for weighting and Multi-Criteria Decision Analysis (MCDA) for architectural evaluation. The framework's efficacy is demonstrated through a case study on a lunar rover, chosen as a representation of cutting-edge technology and the future of space exploration. However, the methodology is inherently domain-agnostic and equally applicable to satellites, aircraft, or other complex systems. For the rover case study, morphological analysis mapped a comprehensive trade-space. Following viability screening, the most promising candidates were quantitatively baselined. The MCDA trade-off revealed that distributed, modular architectures outperformed traditional monolithic models. While monolithic platforms scored slightly higher on raw technical margins, distributed systems achieved superior overall scores due to their unmatched scalability, redundancy, and alignment with commercial space drivers. This methodology provides a robust, repeatable tool for navigating complex trade-spaces. By channelling expert judgment into formal mathematical matrices, the framework mitigates bias, enhances traceability, and enables rapid sensitivity analyses when programmatic priorities shift. Ultimately, the results validate that in the modern commercial space environment, system-level attributes like scalability and resilience are often more critical architectural determinants than the peak performance of a single platform.