Development of a Structured Approach for Establishing Mission Engineering Requirements

2026-06-05 · Source: cs.SE updates on arXiv.org · Field: Science & Research — Engineering & Applied Sciences, Research Methodology & Innovation · Depth: Expert, extended

Summary

This paper introduces a structured approach for establishing mission engineering requirements in scenarios where customer input is ill-defined or absent, a common challenge in rapid build programs like military acquisition. The proposed method systematically decomposes mission intent into context, functions, constraints, critical dimensions, effectiveness attributes, and architecture alternatives. It incorporates a Mission Feasibility Assessment, prioritizes critical dimensions using Best-Worst Scaling, and quantifies external difficulties, technology maturity, and utility through a Mission Complexity Factor (MCF). This framework provides a traceable basis for deriving Tier 1 and Tier 2 requirements, supporting future integration with the Unified Architecture Framework (UAF) and Systems Modeling Language (SysML). A notional close air support mission example demonstrates the approach's practical application.

Key takeaway

For AI Architects or Systems Engineers developing complex systems with evolving or undefined customer requirements, adopt this structured mission engineering approach. By systematically decomposing mission intent, assessing feasibility, and quantifying technology complexity and utility, you can establish a traceable basis for Tier 1 and Tier 2 requirements. This method helps mitigate technical risk and ensures design decisions align with mission priorities, even in early conceptual phases.

Key insights

A structured approach defines mission requirements and assesses technology impact without explicit customer input.

Principles

• Decompose mission intent systematically.
• Prioritize mission-critical dimensions.
• Balance technology benefit with integration risk.

Method

The method involves defining mission context, assessing feasibility, ranking critical dimensions via Best-Worst Scaling, and calculating a Mission Complexity Factor by integrating technology utility and complexity.

In practice

• Use OV-1 and mission threads for context.
• Apply MaxDiff for critical dimension ranking.
• Model technology utility with MAUT curves.

Topics

• Mission Engineering
• Requirements Engineering
• Model-Based Systems Engineering
• Digital Engineering
• Unified Architecture Framework
• Mission Complexity Factor

Best for: AI Scientist, Research Scientist, AI Architect, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.