Real MBSE: Systems Analysis for Smart Decisions Webinar

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Making smart, informed decisions is more critical than ever. Learn how model-based systems engineering (MBSE) and systems-level analysis can empower decision-makers to meet mission needs effectively with the recap of our webinar and Chapter 7 of Real MBSE.

Systems Engineering at the Mission Level

Systems engineering isn’t just about documentation or capturing designs; at its core, it's about analyzing the entire system to ensure it can fulfill its intended mission. Interestingly, there is often a blurry line between mission engineering and systems engineering, since systems engineering is frequently applied at the mission or enterprise architecture level, both of which are essential components of a holistic engineering effort.

The Power of Dynamic Analysis

While creating models and diagrams helps conceptualize systems, real-world operations aren’t as static as the drawings suggest. Dynamic analysis, including simulation and verification using tools such as discrete-event and Monte Carlo simulations, is fundamental to identifying bottlenecks, logic errors, and performance issues early in the lifecycle. By modeling uncertainties and ranges rather than exact values, especially when people are part of the system, engineers can create feasible operational requirements and better predict system performance.

Integrating Design and Simulation Tools

MBSE doesn’t operate in isolation. Tools like Innoslate integrate systems engineering with design engineering suites such as Ansys, MATLAB, and STK to enable detailed simulation and digital twins, twins of real systems that provide predictive insights. Although detailed simulations might require longer run times, their outputs can be curated at a higher systems level to balance fidelity with practical decision-making timelines.

Harnessing Trade Studies for Informed Choices

Trade studies are a central aspect of systems analysis, enabling engineers to explore alternatives and optimize choices based on performance, cost, risk, and other factors. They help answer questions like: What should the performance requirements be? How to balance cost versus capability?

Generative AI and local large language models (LLMs) are emerging as powerful aids in defining and executing trade studies by enhancing search and data mining from organizational knowledge bases.

Capturing and Managing Knowledge

One of the challenges in complex system analysis is managing the wealth of information gathered during trade studies and simulations. Using integrated MBSE tools, engineers can maintain a knowledge base that systematically connects requirements, risks, trade studies, and decisions. This connectivity improves traceability and accelerates evidence-based decision-making.

Documenting and Articulating Results

After completing analyses, creating artifacts such as reports, risk matrices, and decision elements is crucial for effectively communicating findings both within teams and to stakeholders. These artifacts form the backbone of test and evaluation processes and ensure lessons learned and trade-offs are transparently documented.

The Way Forward

As systems become increasingly complex, integrating dynamic simulations, trade studies, and AI-assisted knowledge discovery within an MBSE framework is not just beneficial; it’s essential. Our Real MBSE approach shows how using these capabilities leads to smarter decisions that align systems engineering with mission success.