MBSE – Model Based Systems Engineering – is experiencing a renaissance. Organizations are looking at MBSE to address the challenges of raising complexity and more stringent regulatory requirements. But proper MBSE takes a large investment over a long period of time. And unfortunately, many initiatives wither away due to full commitment from leadership, acceptance issues and underfunded initiatives.
To be successful with MBSE, the trick is to start with specific value-driven use cases. And here is the good news: It is possible to implement such value-driven use cases, without having to confront the team with having to to learn MBSE.
What is MBSE?
MBSE is a formal approach for capturing product information, like requirements, in a model. There are many different modeling notations, UML and SysML two of the better known ones. But there are many others simple and complex.
Using MBSE has many benefits, as outlined in this case study on the automotive manufacturer Changan. However, the drawback is the need to learn a new language (the modeling notation). And practitioners not only have to learn the language: They also have to learn how to use it. To give you an example: Just because you can read and write English, you are not necessarily in the position to write poetry, or a novel.
Why MBSE isn’t mainstream yet
There are two big problems that prevented MBSE from becoming mainstream so far:
- First, introducing MBSE requires a large investment for training, tools, consulting and the like. Training in particular is very important, and is often met with resistance. Convincing engineers to learn modeling can be challenging, but forget teaching modeling to marketing, sales or the leadership team.
- Second, it takes months or years until you the benefits of MBSE materialize. And if done incorrectly, they may never materialize. Leadership is hesitant to commit to such an investment. Even worse, if we only get lip service from the management team, then the MBSE initiative is pretty much doomed.
MBSE can be very successful, and there are many good examples, especially in aerospace, avionics and rail. But this is a far cry from “mainstream”.
AI can apply MBSE without anyone noticing
The truth is, decision makers do not care about MBSE. They care about the benefits of MBSE. This is where AI can help, and Semiant is taking this approach.
Semiant processes specifications with Natural Language Processing (NLP) and extracts a systems model from the text. But rather than presenting that model to the user directly, Semiant realizes a value-adding use case with the aid of the model.
Example: Traceability for Compliance
Consider traceability, an important aspect of regulatory compliance.
An AI is capable of extracting an entity-relationship model from natural language. Based on machine learning, it can identify traces that are likely to be wrong and traces that are probably missing.
During a review, the quality manager does not need (or want) to see the model. She wants to see the list of suspicious traces. This can accelerate the process of traceability analysis drastically, while improving the quality and thereby reducing risk.
Semiant identifies entities
We invite you to try out the demonstrator of Semiant today. Semiant already extracts an entity-relationship model from your specifications, although it only visualizes the entities in the form of a glossary today. But this is already useful, as it helps align the team’s language.
We have a number of ideas that we plan on realizing in the near future – feel free to check out our roadmap.
Did we catch your interest? Then click the button below to schedule a short conversation with Dr. Michael Jastram to discuss how your organization could benefit from MBSE without MBSE.