Developing Effective MBSE Models in Digital Engineering Environments

Model-Based Systems Engineering (MSBE) is a key enabler of effective Digital Engineering (DE), and yet many implementations of MBSE lack the basics of effective modeling. By focusing on the syntax of model constructs and adhering to commercial standards, modelers ignore key details of the system and build ineffective models that cannot drive effective DE. Instead, they build overweight, unwieldly models that inhibit rather than advance our engineering practice.

Building Effective Models for our Model-Based Systems Engineering and Digital Engineering Efforts

It’s a common occurrence with Model-Based Systems Engineering (MBSE) implementations today that modeling efforts can quickly devolve into disagreements about how to use model constructs, how to adhere to a modeling language standard, or even about which modeling language to use. What is routinely overlooked is the systems engineering content of the model itself and the accuracy and completeness of the model in reflecting the system under development. 

MBSE is central to the quality and utility of the larger Digital Engineering (DE) environment, and it is critical to have an effective system model. Where and when do MBSE implementations go wrong? It is important to remember modeling is much more than drawing diagrams and dragging constructs from a palette. It’s about the fundamentals of what a model is supposed to achieve. In these respects, systems modelers can learn much from the modeling and simulation (M&S) community.

Modeling and Simulation

In the M&S community it is critical to identify the purpose of a model. Without constant vigilance over the model’s purpose, modeling efforts can lose focus and address the wrong problem. Understanding the model’s purpose and what the simulation is supposed to measure can help focus modeling efforts on addressing the right questions. Perhaps there are questions about thermal constraints of system components, or the necessary electrical power, or even bandwidth. The M&S community understands that many models may be used to gain answers to these questions.

Second, M&S techniques deal with complexity by using the notion of model granularity to achieve the right level of detail to serve the model’s purpose. A model need not include every nut or bolt to serve its purpose. If models become too detailed, they can be difficult to simulate or unwieldy to manage. If the model’s purpose is to understand thermal constraints, the model should focus on the details that affect heating and cooling. Other aspects, like power, may contribute, but may not need to be modeled in as much detail. Other aspects, such as data transmission, may have no impact and could be safely ignored.

A third concept from M&S that also applies to MBSE is the need to communicate results to a set of stakeholders and decision makers. M&S analysts use simulation to animate a model, either in the form of an emulation where the flow of action can be visualized, or with a discrete-event approach where the action can be studied stochastically to identify a range of potential inputs that could affect results. In this way, simulation is used to communicate the results of what may be a rather complicated model. 

Model-Based Systems Engineering

When we think of modeling in the context of MBSE, we should understand that MBSE does not replace M&S. M&S is still a vital part of a true DE environment. But if M&S is applied to study detailed questions about a system design, what role does MBSE play? How do the three modeling principles from M&S apply to MBSE?

The purpose of modeling efforts in MBSE should align with the purpose for systems engineering. In systems engineering we need to understand the breadth of our system so we can adequately guide system development and specify the system design. Our MBSE model can also act as a source of truth for other modeling and engineering design efforts. In a DE environment, the MBSE model can act as a “hub” that can drive a more effective engineering process.

Dealing with technical complexity is also a central challenge in systems engineering. Systems engineers deal with complexity using the concept of a layered design, where the system can be understood at both high and low levels of abstraction. The model in MBSE should then contain increasing levels of granularity to show a progressive elaboration of the system design as it moves through a design lifecycle. Even if development is making changes to a small part of an existing system, it is important to understand the layered nature of the system at each level of abstraction. Decisions can then be made as to which parts of the model need more detail and which parts may not.   

Another aspect of complexity faced by systems engineers is the growing set of engineering and business disciplines involved in the development of a system. Engineers need to understand the technical aspects of a system, while business professionals need to understand the business case. MBSE can address this aspect of complexity by allowing systems engineers to use the model to effectively communicate across this broad set of system stakeholders. Here MBSE can take a page from M&S and use simulation as one means for communicating the totality of a system design. Also, aspects of the design can be depicted visually in a set of diagrams or in text so that details may be adequately explained and defended. 

Effective DE

Modeling environments that neglect systems engineering will be ineffective and provide negative results, thereby wasting time and budget while failing to align system stakeholders, putting the system at risk. By focusing on the purpose, granularity, and communication power of the model, our modeling efforts can be more effective.   

Many vendors have developed tools that can enable effective DE, but without effective MBSE their power may certainly go unharnessed. MBSE implementations not only connect tools within the DE environment, but they also connect a broad set of stakeholders. Understanding the challenges of developing effective MBSE models and integrating tools properly in DE environments allows systems engineers to deal effectively with the complexity of today’s systems.


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