How MBSE Can Help You Create Better Systems Than Simple Drawing Tools

Systems engineering is the discipline of designing, developing, and managing complex systems that meet the needs and expectations of various stakeholders. However, as systems become more complex, interconnected, and dynamic, traditional systems engineering methods and tools are no longer sufficient to cope with the challenges and risks involved. This is where Model-Based Systems Engineering (MBSE) comes in.

MBSE is an approach to systems engineering that uses models as the primary means of communication, analysis, synthesis, and verification throughout the system lifecycle. Unlike simple drawing tools that only capture the static structure and behavior of a system, MBSE tools enable the creation and manipulation of executable, consistent, and traceable models that can capture the dynamic properties and interactions of a system at different levels of abstraction and perspectives.

In this article, we will discuss why MBSE is necessary and beneficial for modern systems engineering over simple drawing tools. We will also address some of the common challenges and misconceptions about MBSE and provide some recommendations and resources for those who want to learn more about it or apply it in their projects.

Benefits of MBSE over simple drawing tools

Simple drawing tools, such as Microsoft Visio or PowerPoint, are often used by systems engineers to create diagrams and sketches of a system. However, these tools have several limitations and drawbacks when compared to MBSE tools, such as:

• They do not support the validation and verification of system requirements and properties using formal methods and simulations. This can lead to errors, inconsistencies, and ambiguities in the system design and implementation.
• They do not support the generation and transformation of system artifacts, such as code, test cases, FMEA, etc. This can result in manual and tedious work, as well as loss of traceability and quality.
• They do not support the integration and reuse of system models across different domains and tools. This can cause difficulties in managing the complexity and evolution of the system.
• They do not support the collaboration and communication among system stakeholders using a common language and framework. This can create misunderstandings and conflicts in the system development process.

On the other hand, MBSE tools enable the creation and manipulation of executable, consistent, and traceable models that can capture the dynamic properties and interactions of a system at different levels of abstraction and perspectives. These models can be used to:

• Validate and verify the system requirements and properties using formal methods and simulations. This can help to ensure that the system meets the needs and expectations of various stakeholders, as well as complies with the standards and regulations.
• Generate and transform system artifacts, such as code, test cases, FMEA, etc. This can help to automate and streamline the system development process, as well as improve the traceability and quality of the system artifacts.
• Integrate and reuse system models across different domains and tools. This can help to manage the complexity and evolution of the system, as well as leverage the existing knowledge and expertise of different disciplines.
• Collaborate and communicate among system stakeholders using a common language and framework. This can help to foster a shared understanding and vision of the system, as well as facilitate feedback and coordination among different roles.

Perspective and insights on MBSE

MBSE is a powerful and effective approach to systems engineering that offers many advantages over simple drawing tools, and we have used MBSE for several complex systems engineering projects in different domains, such as aerospace, automotive, healthcare, etc. We have witnessed how MBSE helped us to:

• Solve challenging problems that require multidisciplinary collaboration and innovation.
• Create high-quality systems that satisfy customer needs and expectations.
• Avoid costly errors, rework, delays, and failures in the system development process.
• Adapt to changing requirements and environments in a flexible and agile way.

We also believe that MBSE has a great potential to leverage generative AI to assist or automate some aspects of systems engineering, such as model creation, transformation, analysis, or validation. At Reqi we have experimented with some generative AI techniques, such as natural language processing, computer vision, and deep learning, to enhance our MBSE capabilities and productivity. We have seen promising results and opportunities for further improvement and innovation.

Challenges and misconceptions about MBSE

Despite the benefits of MBSE over simple drawing tools, there are still some challenges and misconceptions that prevent some systems engineers from adopting and applying MBSE in their projects. Some of these are:

• The cost and complexity of MBSE tools and methods. Some systems engineers may think that MBSE requires expensive and sophisticated tools and methods that are hard to learn and use. They may also worry about the compatibility and interoperability of different MBSE tools and standards.
• The learning curve and adoption barriers for MBSE practitioners. Some systems engineers may lack the necessary skills and knowledge to use MBSE effectively. They may also face resistance from their managers, customers, or peers who are not familiar or convinced with MBSE.
• The trade-off between flexibility and formality in MBSE languages and notations. Some systems engineers may prefer to use informal or semi-formal languages and notations that are more expressive and intuitive, but less rigorous and precise. They may also find it difficult to balance the level of detail and abstraction in their models.
• The role and potential of generative AI in MBSE. Some systems engineers may be skeptical or unaware of the benefits of using generative AI to assist or automate some aspects of MBSE, such as model creation, transformation, analysis, or validation. They may also have concerns about the quality, reliability, and explainability of the generated models or artifacts.

Case Study: MBSE for Complex Rail Transport Systems

Rail transport systems are complex socio-technical systems that involve multiple subsystems, components, and stakeholders. They have to meet high standards of safety, reliability, efficiency, and sustainability. To cope with the challenges and risks of designing and developing such systems, MBSE can be a valuable approach.

A case study reported by Shoal Group demonstrates how MBSE was applied to the Digital Systems Program (DSP) in Australia, which aims to modernize the rail network in Sydney and other regions by replacing legacy signaling and train control systems with advanced technologies. The case study shows how MBSE helped to:

• Define and communicate the system scope, context, and architecture using SysML diagrams and models.
• Integrate and harmonize the system requirements from various sources and stakeholders using a requirements management tool.
• Analyze and verify the system behavior and performance using model execution and simulation tools.
• Generate and transform system artifacts, such as interface specifications, test cases, etc., using model-based engineering tools.

The case study also discusses some of the benefits and challenges of MBSE adoption and implementation for the DSP, such as:

• Improved system understanding, traceability, consistency, and quality.
• Reduced errors, rework, delays, and costs.
• Enhanced collaboration and communication among system stakeholders.
• Increased complexity and learning curve of MBSE tools and methods.
• Limited availability and maturity of MBSE standards and guidelines.
• Resistance to change and cultural barriers among system practitioners.

Best Practices and Tips for MBSE Adoption and Implementation

In this section, we will share some of the best practices and tips that we have learned from our experience and knowledge of applying MBSE in different projects and domains. These practices and tips can help you to choose, use, and benefit from MBSE tools and methods effectively and efficiently.

Choosing an MBSE Tool

One of the first steps in adopting MBSE is to select an MBSE tool that suits your project’s requirements and supports the desired modeling standards, languages, and methods. Here are some factors to consider when choosing an MBSE tool:

• The functionality and features of the tool, such as model execution, simulation, verification, validation, transformation, integration, collaboration, etc.
• The compatibility and interoperability of the tool with other tools and standards that you use or plan to use in your project.
• The usability and user-friendliness of the tool, such as the user interface, documentation, tutorials, support, etc.
• The cost and availability of the tool, such as the license fee, maintenance fee, trial period, etc.
• The reputation and reliability of the tool vendor, such as the customer reviews, ratings, feedback, etc.

Training the Design Team

Another important step in adopting MBSE is to train the design team on how to use the MBSE tool effectively and efficiently, as well as how to collaborate and communicate using a common language and framework. Here are some suggestions for training the design team:

• Provide a clear and comprehensive introduction to the concepts and principles of MBSE, as well as the benefits and challenges of using it.
• Demonstrate how to use the MBSE tool for creating, manipulating, analyzing, and verifying system models using various examples and exercises.
• Explain how to use the modeling standards, languages, and methods that are relevant and appropriate for your project domain and scope.
• Encourage the design team to practice using the MBSE tool regularly and provide feedback and support for improving their skills and confidence.
• Foster a culture of collaboration and communication among the design team members using the MBSE tool as a common platform and medium.

Using Models as a Single Source of Truth

One of the main advantages of MBSE is that it allows you to use models as a single source of truth for the system’s requirements, design, and architecture. This can help you to ensure the consistency, accuracy, and traceability of your system artifacts throughout the system lifecycle. Here are some ways to use models as a single source of truth:

• Create and maintain models that capture and represent the system’s properties and behavior in a formal and precise way.
• Use models to generate and transform system artifacts, such as code, test cases, FMEA, etc., using automated or semi-automated tools and methods.
• Use models to validate and verify the system’s properties and behavior using formal methods and simulations, as well as compare and evaluate different design alternatives.
• Use models to document and communicate the system’s requirements, design, and architecture to all stakeholders using various views and perspectives.

Integrating and Reusing System Models

Another benefit of MBSE is that it enables you to integrate and reuse system models across different domains and tools, as well as leverage existing knowledge and expertise from different disciplines. This can help you to reduce the complexity and redundancy of your system development process. Here are some tips for integrating and reusing system models:

• Use standard and interoperable modeling languages and notations, such as SysML, UML, etc., that can support cross-domain modeling and integration.
• Use common and consistent modeling frameworks and methodologies, such as RUP, Agile, etc., that can facilitate cross-tool collaboration and communication.
• Use model repositories and libraries that can store and manage system models in a centralized and accessible way.
• Use model patterns and templates that can provide reusable solutions for common or recurring system problems or scenarios.

Balancing the Level of Detail and Abstraction

A key challenge of MBSE is to balance the level of detail and abstraction in the models, as well as the trade-off between flexibility and formality in the modeling languages and notations. This can help you to achieve the optimal level of completeness and correctness in your system models. Here are some guidelines for balancing the level of detail and abstraction:

• Use different levels of abstraction for different purposes and audiences, such as conceptual, logical, physical, etc.
• Use different levels of detail for different stages of the system development process, such as high-level, low-level, etc.
• Use different levels of formality for different types of system properties and behavior, such as descriptive, prescriptive, normative, etc.
• Use different levels of flexibility for different degrees of uncertainty and variability in the system environment, such as adaptive, robust, resilient, etc.

Leveraging Generative AI Techniques

A recent trend in MBSE is to leverage generative AI techniques to assist or automate some aspects of MBSE, such as model creation, transformation, analysis, or validation6. This can help you to enhance the efficiency and effectiveness of your MBSE process. Here are some examples of generative AI techniques that can be applied to MBSE:

• Use natural language processing (NLP) techniques to extract or generate system requirements from natural language texts or speech.
• Use computer vision techniques to recognize or generate system diagrams from images or sketches.
• Use machine learning techniques to learn or predict system behavior from data or examples.
• Use evolutionary algorithms techniques to optimize or evolve system design from fitness criteria or constraints.

Defining the Scope of the MBSE Project

A crucial step in applying MBSE is to define the scope of the MBSE project in terms of the system’s boundaries, interfaces, functions, and performance requirements. This can help to establish a clear and consistent vision and direction for the system development process. Here are some tips for defining the scope of the MBSE project:

• Identify and prioritize the needs and expectations of various stakeholders who are involved or affected by the system.
• Specify and document the system’s boundaries, interfaces, functions, and performance requirements using models that are understandable and verifiable by all stakeholders.
• Use models as a single source of truth for the system’s requirements, design, and architecture, and ensure their consistency, accuracy, and traceability throughout the system lifecycle.
• Communicate and validate the system’s scope with all stakeholders using models that can convey the system’s properties and behavior in an intuitive and interactive way.

Conclusion

In this article, we have discussed why MBSE is necessary and beneficial for modern systems engineering over simple drawing tools. The team at Reqi have brought our own perspective and insights on the topic, based on our experience and knowledge and addressed some of the common challenges and misconceptions about MBSE and provided some recommendations and resources for overcoming them.

We hope that this article has helped you to understand the value and potential of MBSE for complex systems engineering, and we believe that MBSE is not only a tool or a method, but also a mindset and a culture that can foster innovation, collaboration, and quality in system development. We encourage you to explore and apply MBSE in your projects and share your feedback and experiences with us.

Read our article on Unlocking MBSE: Choosing the Best Books for Model-Based Systems Engineering.

Further Reading

• A Practical Guide to SysML: The Systems Modeling Language by Sanford Friedenthal, Alan Moore, and Rick Steiner
• Model-Based Systems Engineering with OPM and SysML by Dov Dori – MBSE: The Solution to the Systems Engineering Crisis by Jon Holt
• The State of the Art of Model-Based Systems Engineering: An Interview with David Long by Mark Sampson

Tools and platforms that support MBSE

• MagicDraw: A comprehensive modeling tool that supports SysML, UML, BPMN, and other standards
• Cameo Systems Modeler: A specialized version of MagicDraw for systems engineering that supports model execution, simulation, verification, validation, and transformation
• No Magic Teamwork Cloud: A collaborative platform that enables model sharing, versioning, branching, merging, and reviewing among system stakeholders
• Cameo Simulation Toolkit: An add-on product that enables model execution and simulation using various solvers and engines
• Cameo Enterprise Architecture: An add-on product that enables enterprise architecture modeling using various frameworks and standards
• Cameo DataHub: An add-on product that enables data integration and synchronization among different tools and repositories
• Cameo Collaborator for Teamwork Cloud: An add-on product that enables web-based access and interaction with models stored in Teamwork Cloud
• Cameo Concept Modeler Plugin: An add-on product that enables ontology modeling using OPM
• Cameo Inter-Op: An add-on product that enables model interoperability using various formats and protocols
• Cameo Analysis & Design Plugin: An add-on product that enables code generation, reverse engineering, testing, debugging, and deployment using various languages and platforms
• Cameo Business Modeler Plugin: An add-on product that enables business process modeling using BPMN
• Cameo Requirements Modeler Plugin: An add-on product that enables requirements management using SysML or UML
• No Magic Training Center: A learning platform that offers online courses, webinars, tutorials, and certifications on various topics related to MBSE and modeling tools

Thank you for reading this article. We hope you enjoyed it and learned something new. Please feel free to share your comments, questions, or feedback with us. We would love to hear from you.