Engineering Decisions in MBSE: Insights for a Decision Capture Framework Development

2025-12-01

INSIGHT

AI Summary

This work proposes a lightweight framework to address the high cost and poor contextual retention inherent in traditional engineering decision capture methods. By modeling decision alternatives as slices of system models and embedding them directly into model-based systems engineering (MBSE) workflows, the approach explicitly links decision knowledge with requirements, behavioral elements, and architectural components. This integration significantly reduces the overhead of decision documentation while enhancing the preservation of contextual information. The feasibility of the framework is demonstrated through a case study on aircraft architecture simplification, which confirms its effectiveness in improving decision reusability and integration efficiency within MBSE environments.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Decision‐making is a core engineering design activity that conveys the engineer's knowledge and translates it into courses of action. Capturing this form of knowledge can reap potential benefits for the engineering teams and enhance development efficiency. Despite its clear value, traditional decision capture often requires a significant amount of effort and still falls short of capturing the necessary context for reuse. Model‐based systems engineering (MBSE) can be a promising solution to address these challenges by embedding decisions directly within system models, which can reduce the capture workload while maintaining explicit links to requirements, behaviors, and architectural elements. This article discusses a lightweight framework for integrating decision capture into MBSE workflows by representing decision alternatives as system model slices. Using a simplified industry example from aircraft architecture, we discuss the main challenges associated with decision capture and propose preliminary solutions to address these challenges.

Problem

• decision capture
• Model-Based Systems Engineering
• engineering decisions
• knowledge reuse
• context preservation

Innovation

• MBSE
• decision capture
• model slicing
• engineering decision-making
• systems engineering