In the field of model-based system engineering (MBSE), creating a consistent, high-quality model is considered fundamental to ensuring a project’s success. A well-structured model benefits stakeholders in a number of ways, including facilitating the understanding of complex systems, enabling informed decision-making and effective risk management. However, first we need to define what constitutes a ‘good’ MBSE model and how its quality can be assured.
In this post, we address these questions by examining the characteristics of high-quality MBSE models, the human and automated processes required to maintain model integrity, and the techniques for ensuring consistency and usability throughout the engineering processes.
By Paloma Maestro Redondo, System Engineer and Project Manager
The importance of high-quality models in MBSE
A high-quality MBSE model is a fundamental requirement at every stage of a project’s life cycle, because it serves as the ultimate source of information about the system that’s being developed. Maintaining model quality is essential to ensure that all stakeholders have a common understanding of the system, meaning they are aware of the latest decisions and share the same interpretation of the design; this, in turn, reduces miscommunication and errors. A well-maintained model supports effective decision-making by providing accurate and relevant information, enabling stakeholders to evaluate trade-offs and make informed choices with confidence.
In addition, high-quality models improve traceability by clearly linking requirements to design decisions and outcomes. This traceability ensures that systems meet their intended objectives and facilitates continuous verification and validation. Furthermore, as projects evolve, a well-constructed model can be more easily adapted to match any changes in requirements or constraints without losing consistency.
Identifying inconsistencies, gaps or uncertainties early in the development process helps us to create robust models, which in turn helps to mitigate risk and prevent costly mistakes later in a project’s life cycle. They also promote collaboration by acting as a reference point for diverse teams, enabling them to work together effectively across multiple disciplines. Failing to preserve the quality of the models can result in misaligned objectives, flawed designs and increased costs and/or delays. This highlights the importance of monitoring and maintaining consistency and integrity at every stage.
What defines a high-quality MBSE model?
A high-quality MBSE model should have certain characteristics and include some important information in order to effectively support system design and decision-making.
First, it should be purpose driven, containing only the necessary details to achieve the study goals and avoiding unnecessary complexity. Models should enable stakeholders to maintain an overview of the system, so the information should be organised following logical abstraction levels to provide clarity and structure. It is also really important to be consistent with the language you use and the selected method, having the right elements defined at the right abstraction levels and rigorously adhering to the selected modelling syntax.
While the data being included in the models might come from different sources, all modelling elements should be assigned to the appropriate domains to make sure that responsibility and expertise are well defined and reflected.
Another way to ensure the models are useful throughout the project life cycle is to maintain a good traceability of all modelling elements. By defining connections between the modelling elements and the requirements, we can check whether the design decisions are aligned with the project goals, and identify potential issues in advance. In the same way, teams that will work in latter project phases will be able to understand the impact of past decisions on the design.
The modelling process should be as efficient as possible, so reusing building blocks will help to leverage standardised or pre-defined components, improve consistency of the design and also help to reduce unnecessary redundancies.
Model elements also need to incorporate margins to address uncertainties while maintaining clarity in the information that’s included. Defining a simple margin philosophy should therefore be one of the first tasks to be that teams complete.
Finally, models should include supporting visual tools, such as icons or concise labels, to make it easier for all stakeholders to understand in a consistent way.
Criteria for assessing ‘model quality’
Taking into account the characteristics we have just mentioned, we can conclude that for a model to serve as a reliable source of information in the application of MBSE, it must satisfy the following criteria:
- Consistency – Terminology and classifications are unified, and elements are positioned at the right level of abstraction.
- Completeness – Parameters are systematically added to the appropriate components, ensuring no critical elements are missing.
- Compliance – Adherence to modelling syntax and standards enhances interoperability and usability.
- Communicativeness – The model fosters shared understanding among stakeholders and facilitates collaboration.
- Traceability – Clear connections between model elements and requirements support verification and validation processes.
- Insight into performance – The model helps evaluate whether the system meets user needs and performance goals.
How can we guard a model’s quality?
To maintain the quality of an MBSE model, it is essential to combine human rigour with automated tools. Each approach plays a unique yet complementary role to guarantee consistency, completeness and reliability throughout the system’s life cycle.
From a human effort perspective, it’s critical to foster an environment of collaboration and psychological safety. Team members must feel comfortable identifying and addressing potential problems without fear of recrimination. Risk awareness is equally important; problems should be acknowledged and acted upon promptly rather than ignored.
A disciplined approach to rigour is essential – the aim is to avoid excessive assumptions and ensure that all aspects of the system are thoroughly analysed. Clear, organised models help stakeholders keep track of modifications and improvements, and reduce the likelihood of errors being overlooked. In addition, acknowledging and accounting for uncertainty by incorporating appropriate margins helps to manage risk effectively.
Automation enhances these efforts by providing consistency and reducing the likelihood of human error. Establishing a common meta-model structure early in the process creates a unified framework for the team, minimising potential discrepancies. By categorising model elements, maintenance tasks can be also simplified.
Automated verification tools can be used for completeness and consistency, and ensuring adherence to the chosen modelling syntax. These tools can include specific rules that act as an additional layer of quality control, enabling teams to identify and resolve issues efficiently.
By combining the strengths of human intuition and collaboration with the precision and efficiency of automated processes, teams can maintain a high standard of model quality, ensuring that their MBSE activities remain aligned with the best practices and continue to deliver effective results.
What methods and techniques can we use to ensure high-quality models?
Achieving and maintaining model quality requires a combination of good practice and collaborative methods. The main recommendations for teams working with models are:
- Constructive criticism – Encourage a ’Devil’s advocate‘ approach to identify potential issues.
- Collaboration and peer review – Regularly review models with colleagues to identify errors and align perspectives.
- Open atmosphere – Foster an environment where questions and clarifications are welcomed to ensure shared understanding.
- Margin philosophy – Define clear guidelines on incorporating margins to handle uncertainties.
- Training and alignment – Provide training on modelling methodologies and rules, and establish a consistent approach across the different teams.
- Integrated risk analysis – Continuously assess risks throughout all project phases.
In summary
A consistent, high-quality model is the basis for successfully implementing MBSE in projects. It provides clarity, facilitates collaboration and ensures traceability throughout the project lifecycle. By combining human rigour with automated tools, teams can create models that are not only complete and consistent, but also adaptable if there are changes and/or uncertainties.
High-quality models enable teams to make informed decisions, manage risk and maintain alignment across disciplines, ultimately contributing to the development of systems that meet user needs and perform as intended. Ensuring and maintaining this quality is not only a technical challenge, but also a collaborative effort that is essential to the success of any project.
If you would like to have additional support in your projects to achieve high-quality results when implementing MBSE, please get in touch with our digital engineering experts.
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