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.
We have been developing products more or less successfully for centuries. There have been several disruptive innovations during that time, like the production line the rigorous approach of systems engineering and the introduction of electronics and now software.
Dealing with foreign languages has gotten so much easier recently. Even free translation tools produce superb results. Machine translation is a great example of the digital transformation that we are experiencing.
But why should we stop at translating text into human languages? Languages for describing systems, like SysML, have been around for decades. The method of applying such languages is called MBSE (Model Based Systems Engineering). While MBSE can have benefits when developing complex products, it requires significant investment for tools, training, consulting and configuration.
Model Based Systems Engineering (MBSE) Without the Overhead
Semiant provides you with the benefits of MBSE, but without the investment. Semiant “translates” specifications into a machine-readable system model. From the model, it extracts useful information, makes suggestions or even performs mundane but important activities. This prevents waste, reduces risk and speeds up development.
The idea of automatic translation into machine-readable languages is not new. For instance, asking a voice assistant like Amazon’s Alexa would first translate the question or command from the user into a query that the system would then execute. The user never sees the (automatically translated) query, only the outcome. This is how Semiant works, except that it translates the text into a systems modeling language.
Alexa is a general purpose virtual assistant while Semiant is a product development specific virtual assistant. The following figure shows the similarities between Amazon’s Alexa and Semiant:
This approach allows Semiant to immediately provide the benefits of MBSE to the practitioners without having to learn a new modeling language or tool. It provides value that is convincing to decision makers as well. Specifically, Semiant:
Prevents waste: For instance, creating a glossary (or controlled vocabulary) makes sense to support compliance activities. But a glossary created by a human will almost certainly be incomplete and outdated on day one. Semiant will create it automatically, and it will always be up to date.
Reduces risk: Having an understanding of open issues reduces the risk of slipping of delivery dates, organizational risk due to undetected gaps in compliance and so on. Semiant even makes suggestions on how to fix issues.
Speeds up product development: By automating mundane tasks, like the creation of the initial traceability, you get more done faster with your existing team. Onboarding efficiency will also increase, as new team members get answers to many early questions from Semiant, rather than jamming expert’s time.
More Skills on the Way
Today, Semiant can extract glossaries from specifications. The system model is the foundation for this capability. But in the same way that MBSE has many benefits, we’re just scratching the surface. We will soon add more and more Semiant “Skills” over time to unlock the full potential of MBSE, without anybody having to learn a modeling language.
Call them robots, Skynet or Artificial Intelligence (AI): Literature is full of machines that have a life of their own. The big question is: Will these machines create heaven on earth? Will they strive to destroy us? Or will they just be indifferent?
The scientist Ray Kurzweil introduces the concept of the “Singularity”: This is the point in time where machines will be as intelligent in humans. At this point, these machines will build even better machines. In other words, machine intelligence will surpass human intelligence. This is the runaway-point: We have no idea what will happen thereafter.
Our sole responsibility is to produce something smarter than we are; any problems beyond that are not ours to solve …”
Ray Kurzweil, The Singularity is Near: When Humans Transcend Biology
However, we are still years, if not decades away from this point. At least for now, this is good news: While Artificial Intelligence is far from showing human intelligence, it is smart enough to take over more and more mundane tasks.
One such skill is the ability to process human language. Not “understand”, mind you: A machine today can understand the structure of a sentence, extract concepts and put it in relationship to others. It can even find unusual outliers and point them out to a human. But it cannot understand.
But this still helps: We humans can do the creative work, while the AI tells us where to look.
This is where Semiant, our virtual quality assistant helps experts in product development. Semiant performs the mundane tasks, so that the humans can do the interesting work.