Model Based Systems Engineering

Model based systems engineering (or MBSE) is a methodology for organizing your engineering information exchange and workflow that centers everything around models rather than around documents. Put simply, documents are textual descriptions of workflows, connections, features, requirements, and more. They’re read by engineers to understand how to work with a part of a system. Models have textual elements, but also contain graphical representations of how the elements of a system interact. The connections between different parts of the system are represented visually, rather than just being described in text.

It may seem like a minor difference, but model based systems engineering opens up wholly new ways of thinking about and interacting with your system.

What are the benefits of model based systems engineering?

Making the effort to adopt MBSE provides many benefits for your team, including:

Insights into process and connections

As you lay out your system’s features into a flowchart, graph with nodes and arrows, simulation, or other model, you will likely make some discoveries. You may find some links between processes that are parallel and could be consolidated. There may be steps that are redundant with another process, saving you time and money when removed. With more detailed modeling, such as tracking the amount of time each step takes in a process model, you can pin down inefficiencies and roadblocks.

Just remember that any insight you make about the model may not be 1:1 with the real world. Models will always have inaccuracies. Make hypotheses about how you can change your system based on your model, but test them in controlled and monitored ways.

A better testing environment

With a detailed enough model, you can simulate aspects of your system. This could be as simple as a database that automatically displays outputs of given inputs based on a mathematical formula. Or it could be as complex as an actual physics simulation of a machine in your process.

Having this resource can help you prepare for changes and externalities. Test out worst-case scenarios to see how your system might handle them. If you’re experimenting with a new idea, see how it plays out in your model. Again, your model won’t be perfectly accurate, but it will be helpful.

Consistency of thought

A downside of document-based engineering is that the documents are inevitably prepared by many different people who may have different perspectives. They may refer to things inconsistently or understand the purpose of something in a narrow way. By itself, this isn’t necessarily a problem, but can easily lead to problems if there isn’t a way to contextualize these differences.

With a model based approach, these potential disconnects are solved by integration into the model. That is to say, if person A thinks that the requirements for process 1 requires xyz, and person B thinks it requires abc, the inconsistency will be revealed when making the connection in the model. It may be that neither party is totally erroneous, and the correct requirements are xab.

Challenges in implementing model based systems engineering

If MBSE is so effective, why isn’t everyone working with it? The short answer is that it’s tough. Figuring out the interconnectivity of all the aspects of your system requires a major investment of time by your most expert teammates. It also may require a major investment of time in making data compatible, if you want your model to automatically reflect the flow of data in the real world. Sometimes companies can’t make that investment, even if they know it’ll pay off.

It can also be more time consuming to update a model with changes than creating new documentation. Depending on the process to make a model update, engineers may just quickly want to note down what they changed instead. This isn’t terrible, as any documentation is infinitely better than nothing, but can contribute to a disconnect between the model and reality.

Approaching a model can be more intimidating for a new hire than reading documentation. Having a complete perspective on a system is useful, but may be overwhelming at first. Making sure that there’s good onboarding ramps to get people to the understanding they need effectively is key.

Model based systems engineering in manufacturing

What does model based systems engineering look like in a manufacturing shop? It can exist on several levels simultaneously. On the lowest level, it can look like a model of what any given machine inputs and outputs, and how the outputs are affected by different controls. On a higher level, it can look like the process by which one product moves through the entire shop, from initial production to finishing, storing, and selling. On the highest level, it can model your entire inventory from procurement to production to selling.

To create a manufacturing model for your system, the first step is making sure you can collect data from all the necessary places. This creates a manufacturing data lake that can allow for analysis and exploration of your system’s interconnectivity.

How CADDi can help

CADDi makes modeling your system easier by providing connections across multiple data sources. Your PLM, ERP, PDM, and other software all produce information relevant to a different stage of your model. With CADDi, you can track the data for a part all the way through your model: from the drawing of the design, to the pricing and supplier data for procurement, to the defect data in operations.

‍

With CADDi, your workflows become easier to both model and execute, highlighting inefficiencies, roadblocks, and unnecessary costs. Contact us to learn more.

‍