
Over the last 18 years, I’ve had a variation on this conversation on every project I have worked on. (Dramatized for a blog audience)
- Talented Engineer (TE): This model based stuff is interesting and all, but I can write the same code in 1/2 the time and it is 10% more efficient.
- Me (also a talented engineer): That is probably true, you write very good code. Do you enjoy writing code?
- TE: Well no, I write code because that is what I need to do to implement my algorithm. But wait, you are admitting my code is better?
- ME: Yes, yours is. How many other people in your group are as proficient in C? And if you don’t enjoy writing code, do you enjoy designing <MAGICAL SPACE WIDGETS>(1)?
- TE: I Went to school so I could work on MSWs(2), I love working at MSW Co on them; and really, maybe one out of the 20 can program as well as I do.
- ME: Ok, well, how much time do you spend coding versus designing? Debugging versus testing?
- TE: Tell me more about MBD stuff…

Realizing the benefit
The definition of Model-Based Design that I use is simple:
The use of a model(3) that is the single source of truth to execute two or more tasks in the design cycle
I work for The MathWorks, but, for a minute I will be agnostic. The definition simply says “a model.” The “model” can be a physical prototype, an analog computer, C code or, I hope, a Simulink or Simscape model. The important part is that the same model, without changes (4), is used at multiple points in the design cycle.

If we think back to our TE in the opening section, what did they want to do? They wanted to design MSWs. They did not want to spend time creating test harness, writing test vectors, generating reports, and integrating with hardware. And why should they? TE was hired because he studied MSW and knows how to design the best MSWs; why take him away from that task? Since MBD allows users to use the same model at multiple points means that when our TE in design is done he can hand it off to another TE in the testing group who when they finish it can hand it off to a TE in integration who hands it off to a TE in release engineering. And why is this possible? Because it is much easier to find talented engineers who cover a given area very well (e.g. just test, or just release) than it is to find the magical unicorn(6) who can do all of the tasks well.
But wait! I can do all that in X
At some point down the line our TE comes back and says
- TE: wait, just two paragraphs ago you said a Model could be C code, why should I use this graphical language?
- ME: Wait! I just wrote that, so how did you see that? But OK, depending on your application, you may use Simulink, Stateflow or the MATLAB (textual) environment. The key is the infrastructure built up around the environment that enables the “more than one uses of the model.”

Can and should are two different beasts(7). Modern graphical modeling languages have supporting tools directly integrated into their environment. The set-up and integration is reasonably straightforward. Textual language, by their open nature, often have higher set-up and integration costs.
Making the transition / learning your way around
At first the transition to a graphical development environment (8) can seem daunting; Simulink’s base pallet has over 200 blocks(9), and knowing at first which one is the correct one to use can be confusing. However, like learning any other language you will quickly pick up the basics once you throw yourself in. Unlike learning a new programming language there are multiple transformation technologies you can apply directly to the model. When you start adopting Model-Based Design you should determine what “second task to execute” you want to adopt first. For more insights on this I would recommend viewing this roadmap.
Putting it all together
Ultimately the adoption of model based design isn’t about the tools, it is about the process. How you use each tool at each step along the way to the best effect. I welcome you to continue to join me in this space as upcoming blog posts delve more into Model-Based Design processes.

Model-Based Design for the VP/CTO
In past blogs I have written and talked about the Return On Investment (ROI) for adopting Model-Based Design. This link, from The MathWorks, provides another good overview on the ROI question. I want to propose another reason for this migration / adoption. Finding an engineer / scientist who knows how to develop “magical space widgets” takes time; on-boarding them them takes time. Losing them happens from frustration and boredom. This is one of the “hidden” drivers of ROI for MBD; when your people spend most of their time working on the things that interest them in ways that use their abilities and knowledge you have highly engaged employees which leads to greater innovation and higher quality.

Footnotes
- MAGICAL SPACE WIDGETS is a generic term for a customer project. Sometimes it is a car or a plane, or sometimes an actual spacecraft.
- MSW Is the agreed upon TLA for Magical Space Widgets.
- In the actual MBD workflow it will be multiple models, but let’s start simply
- Without changes is a simplification. The model you start off with at the start of the design cycle will be elaborated as it is developed. The important point is that if you took that elaborated model back to the earlier stages of the process it should still function in that stage (at a higher level)
- The version that I like best of the V diagram reflects the iterative nature of design, that within each stage there are iterations moving forward and back. Much like a PID controller, a good process is self correcting to errors in the process.
- Magical unicorns do exist, just don’t count on your process depending on them.
- Or in the image’s case, T-Rex
- OK, I’m not trying to be subtle here; once you start seeing them as development environments where you don’t throw away your work at each step along the way, the benefits become clear.
- Honestly I’m not sure how to count the number of “basic” functions in a textual language like C; while those 200+ blocks at first may seem like a lot, but once you realize they are targeted at the design of models you quickly pick them up.