Model-Based Systems Engineering (MBSE) is evolving before our eyes. Once a methodology built purely on system diagrams, today’s MBSE efforts have connected databases and unlocked an incredible potential for analysis. But MBSE is not a panacea for all that ails our increasingly complex development processes. Now more than ever, it’s important to focus on the intent of the models we build, and to keep a sharp, critical eye on our systems — from conception and implementation through operation and optimization.
The early-stage benefits of MBSE are well established: Accelerated iteration, low-risk exploration of new design ideas, and simpler collaboration based on common understanding. The long-term indicators have been just as encouraging. In many cases, MBSE has not only enabled next-generation traceability, but has become a powerful tool for validation, verification, testing and change propagation.
Consider BMW: Optimizing its suspension systems used to mean physically swapping out individual springs, dampers and chassis components, and hoping for the best. But with MBSE, the company was able to create a new, predictive model of its vehicles that virtualized each suspension component — and simulated their effects on ride quality. This allowed for extensive iteration and experimentation, without costly early-run manufacturing and time-consuming physical testing.
A New Model for Skepticism
While benefits like these can be an irresistible siren call for an advanced manufacturer, it’s important to note that MBSE isn’t right for everyone. Early results are encouraging, but there simply isn’t the data to back up many of MBSE’s loftiest claims. Furthermore, implementing MBSE calls for a substantial front-end investment, which can elicit a chill from even the fieriest organizational innovator.
On such a perilous path, how do you find the best way forward? How do you determine if MBSE is right for your company, implement a system that solves your key problems, and keep it running smoothly?
In my course, “Model-Based Systems Engineering: Documentation and Analysis”, offered online through the Massachusetts Institute of Technology, I explore these questions at length. But here are a few key concepts that can provide a useful foundation for your thinking moving forward.
Ask the Right Questions
I have found that MBSE is most effective when there is an underlying challenge or question that needs to be solved. Taking the time to identify these goals gives your methodology a clear focus, and provides a useful lens for all future decision making.
Take NASA’s Jet Propulsion Laboratory: In planning for its Europa mission, the Lab had two clear goals in mind: First, to reduce the number of product and mission defects in the face of growing complexity. Second, to increase productivity. To accomplish these goals, they implemented MBSE for a wide variety of tasks, including integrated power and energy analysis and fault protection design verification. In the end, their team was “able to study three distinct mission concepts for the resources usually sufficient to study only one or two, and the high quality of all three studies was lauded by the Hubbard Review Board and by NASA HQ.”*
Challenge Your Assumptions
During “Model-Based Systems Engineering: Documentation and Analysis” participants focus on the intent, representations, and critique of MBSE, and are tasked with defending the purpose of these investments. We also focus a great deal of time on analysis and critique of systems through real-world case studies. After all, it’s only by learning from the past, from questioning common knowledge—and each other—that can we truly unlock the potential of MBSE.
About the Course
“Model-Based Systems Engineering: Documentation and Analysis” starts January 2, 2017. It is the third course in MIT’s four-course professional-certificate program Architecture and Systems Engineering: Models and Methods to Manage Complex Systems. This course may be taken individually, without enrolling in the professional certificate program.
Available online only, classes are open to all, and are particularly relevant for systems engineering professionals, directors and senior managers looking to innovate in complex product development environments. Departmental teams are encouraged to apply.
Bruce G. Cameron is faculty director for MIT’s new professional-certificate program, Architecture and Systems Engineering: Models and Methods to Manage Complex Systems. He is the director of MIT’s System Architecture Lab, a lecturer in engineering management in MIT’s existing System Design and Management master’s program, and a co-founder of Technology Strategy Partners, a boutique consulting firm. His research interests at MIT include technology strategy, system architecture, and the management of product platforms.