What is Model-Based Systems Engineering (MBSE)?

Model-Based Systems Engineering (MBSE) involves using digital models (rather than documents or informal diagrams) to define and analyze system functions, structure, and behavior. It supports system requirements, design, analysis, verification, and validation activities beginning in the conceptual design phase and continuing throughout development and later lifecycle phases. MBSE methodology is essential for managing the increasing complexity of modern systems.

MBSE is a pivotal driver in moving from traditional document-centric approaches to a model-centric approach. This approach uses graphical behavioral models to explore design space and to collaborate between system engineers and stakeholders. This methodology enables teams to create complete high-level digital systems representations that allow for quick evaluation of different design options and scenarios while maintaining traceability throughout the development process.

The global MBSE market, valued at approximately $2.5 billion in 2023, is projected to reach $7.8 billion by 2032, reflecting the growing recognition of its importance in modern system development. This growth is especially evident in industries like automotive, aerospace, and defense, where manufacturers leverage MBSE not only to streamline product development and reduce time to market but also to enable continuous product feature updates and improvements.

Benefits of model-based systems engineering

MBSE is highly effective due to its structured, visual, and integrated approach to managing complex high-level definitions of systems throughout their entire lifecycle. This is because the use of system modeling languages like SysML allows these models to act as a connective tissue across all product design abstractions. SysML is a standardized, general-purpose modeling language designed to specify, analyze, design, and verify complex systems, including hardware, software, information, processes, personnel, and facilities. MBSE offers the following benefits:

• Formal definition of design intent, with interactive models that enable design teams and other stakeholders from across the organization to reference it with accuracy and consistency
• Efficient representation of complexity, with interactive diagrams and other user-friendly representations of system behavior
• Reduced design time, as it enables systems engineers to perform analyses in a fraction of the time it would take to do the same tasks manually
• Alignment with lifecycle process that modern PLM platforms can manage, including change management, revision control, access controls, and other PLM lifecycle services
• Higher performing designs and quality through integrated simulation data and harmonious system configuration
• Early detection of design issues through virtual prototyping and analysis, reducing costly late-stage changes
• Enhanced collaboration through the uniform definition of the design intent as the connective tissue between all design representations
• Improved risk management through early issue identification and proactive mitigation strategies
• Better requirements management through direct linking of requirements to system components

Main components of model-based systems engineering

MBSE methodology comprises several key components that work together to create a complete virtual definition of a system and facilitate interdisciplinary integration throughout product development.

Systems model

The system model serves as the foundation of MBSE, providing a formal, structured representation of a system that captures its functions, behavior, architecture, and interfaces. This high-level digital blueprint enables teams to:

• Create comprehensive system representations
• Analyze design alternatives
• Validate system performance
• Support collaborative development
• Maintain design consistency

System Modeling language

A formal modeling language provides the standardized syntax and semantics for creating and evolving system models. The most widely used languages include:

SysML (Systems Modeling Language)

Based on UML but extended specifically for systems engineering needs, SysML provides a standardized notation that enables consistent communication and understanding across engineering teams while supporting comprehensive system representation.

Domain-Specific Languages (DSLs)

These specialized languages are tailored to specific industries or applications, allowing teams to express complex domain concepts more efficiently while maintaining the necessary rigor for system modeling.

Requirements management

Requirements management functionality enables teams to capture, trace, and manage system requirements throughout the development lifecycle. Key capabilities include:

• Direct linking of requirements to system model elements
• Traceability from high-level needs to detailed design
• Real-time validation against requirements
• Change impact analysis
• Requirements verification tracking

Simulation and analysis (SAM)

MBSE models allow to extract simulation models that allow engineers to analyze system performance, optimize designs, and explore “what-if” scenarios before physical implementation, including:

• Behavioral Simulations: Test how the system responds to various inputs, states, or interactions.
• Parametric Analysis: Evaluate system performance by modeling key parameters (e.g., weight, cost, energy consumption) and their relationships.
• Tool Integration: MBSE integrates with various simulation tools, such as MATLAB/Simulink, to perform advanced analyses.

This comprehensive simulation environment allows teams to validate designs thoroughly before moving to physical implementation.

Lifecycle management

The lifecycle management component provides centralized control over the entire MBSE environment. It enables:

• Version control and configuration management
• Change tracking and governance
• Multi-user collaboration
• Model repository management
• Integration with PLM systems

Together, these components create an integrated environment that supports comprehensive systems engineering from concept through retirement. The digital thread established through these components ensures consistency and traceability across all aspects of system development.

The MBSE workflow

The typical MBSE process includes the following steps:

Step 1. Establishing requirements
First, the engineering team develops high-level requirements based on the needs of all stakeholders, including users, business leaders, and regulatory authorities. Some teams leverage software to help them capture and manage all requirements.

Step 2: Designing the system
Next, the team begins to create a system model that meets the established requirements. They use MBSE to create visual models to account for all functions, behaviors, interfaces and architecture of the system. It also includes allocation of  the stakeholder requirements to a specific element or set of elements of the system model.

The team then uses the system model to explore design space including how the system will function and perform under various conditions, helping them understand how it will react to real-world use cases and specific implementation scenarios (mechanical, electronic, and software).

Step 3: Analyzing risks
Then, engineers run risk analyses against the system, including technical performance, costs, safety, etc. They respond to these risks with preventive measures, ensuring that the final product has minimal risks.

Step 4: Validating the models
Next, the engineering team checks the models. They run a series of tests to verify that they are accurate, reliable, and in accordance with the requirements.

Step 5: Building the system
After validating the models, the team can begin detailed designs in specific domains (mechanical, electronic, and software) ensuring it meets requirements and standards.

Step 6: Maintaining the system
The MBSE continues to serve as a central source of information including product manufacturing and deployment. It helps to interpret the real-world feedback, field issues, and other valuable information, giving the teams a collaborative space for continuously improving the product.

Use cases for model-based systems engineering

Several industries leverage MBSE to capture and communicate design intent throughout detailed product development and manufacturing. Here are a few examples of product-intensive businesses that rely on MBSE for business success:

• The aerospace and defense industry develops complex systems with detailed specifications and niche use cases. These teams use MBSE to precisely understand how the system will operate in various real-world scenarios.
• The automotive industry leverages MBSE to create in-depth models of complex modern cars, including both physical and software components.
• The engineering, procurement & construction (EPC) industries rely on MBSE to maintain traceability throughout the production of complex assets.
• High-tech industries that blend physical infrastructure and advanced software leverage MBSE to release innovative products and keep up with rapid change.
• Industrial manufacturing organizations turn to MBSE to build complex products at a fraction of the cost and time required with manual processes and disparate tools.

The medical industry leverages MBSE to create safe, compliant devices with minimal recalls.

Model-based systems engineering challenges

Organizations implementing MBSE strategy face several significant challenges that need careful consideration and strategic planning to overcome:

Primary implementation challenges

• Cultural Resistance and Change Management: Teams accustomed to traditional document-based methods and decentralized development of mechanical, electronic, and software domains often resist transitioning to model-based strategy. Overcoming such resistance requires comprehensive training programs, clear communication of benefits, and sustained leadership support to foster interdisciplinary collaboration and break down existing silos.
• Technical Integration Issues: Integrating various MBSE tools and ensuring compatibility with PLM platforms can be challenging. Organizations must carefully plan their technology stack and establish clear standards for tool integration and data exchange.
• Resource Investment: Organizations must commit substantial resources beyond initial software and infrastructure costs. This includes investing in comprehensive training programs, acquiring skilled personnel, maintaining systems, and optimizing processes while ensuring proper tool customization and integration across the enterprise.

Additional Considerations

• Data Management Complexity: Managing large volumes of interconnected model data authored in separate tools while maintaining consistency and version control across multiple teams and locations.
• Standards Adoption: Navigating the evolving landscape of MBSE standards and ensuring compliance while maintaining flexibility for organization-specific needs.
• Performance Scalability: Ensuring system performance and responsiveness as models grow in size and complexity, particularly in large-scale engineering projects.

While these challenges are significant, they can be effectively managed through proper planning, stakeholder engagement, and a phased implementation approach that allows for the gradual adoption and optimization of MBSE practices.

Getting started with MBSE

Establishing an MBSE strategy within your organization requires the right management commitment, technologies and processes. Many teams take an agile approach to strategically roll out organizational and technology elements that support it. This way, they can gradually release tooling, adequately train team members on using the new features, and test how the MBSE system works over time. As with any software development project, the team should measure key performance indicators, ensuring that the implemented technologies and processes meet their goals.

PLM platform like Aras Innovator® is a great starting point for establishing a centralized MBSE process. It offers several features to help teams implement a custom approach to MBSE that is specifically designed for their unique product and business goals. Connected product data and models across every stage of the lifecycle creates a PLM-managed digital thread that stakeholders can use in real time.

Aras Innovator offers several features to help teams implement a custom approach to MBSE designed explicitly for their unique product and business goals. For example, Aras DevOps is included for all Aras SaaS customers, allowing teams to customize their Aras Innovator instance. It also offers advanced digital twins for building user-friendly models of any asset or system of assets.

Common MBSE FAQs

Here are answers to frequently asked questions about Model-Based Systems Engineering to help clarify some common points of confusion in the industry.

What is the difference between Agile and MBSE?

While MBSE is a methodology that uses digital models to define and analyze system functions, Agile is an iterative approach to project management and development. The two can work together effectively — Agile MBSE combines the flexibility and iterative nature of Agile with the model-based approach of MBSE. Recent implementations have shown that using Agile with MBSE can reduce labor hours by up to 71% compared to traditional document-based approaches.

Is MBSE a framework?

No, MBSE is not a framework but rather a strategy and methodology that uses high-level system models to provide design intent across the entire lifecycle of a system, from conception through development to decommissioning. It represents a paradigm shift from document-centric to model-centric engineering, using digital modeling and simulation to design systems. MBSE can be adapted to projects of different sizes and complexities.

What is the difference between PLM and MBSE?

Product Lifecycle Management (PLM) and MBSE have evolved from different requirements and serve different purposes. PLM focuses on managing all information around a manufactured product throughout its lifecycle, while MBSE emphasizes creating and using models to define and analyze high-level system functions, behaviors, architecture, and interfaces.

While PLM systems handle documents and metadata, MBSE provides semantic models with meaning and focuses on system behavior and architecture. Modern implementations often integrate both approaches to create a comprehensive product development environment.

How can I choose an MBSE tool?

The selection of an MBSE system modeling tool should start with defining your organization’s specific requirements and use cases. Key considerations include the tool’s modeling language support (like SysML or UML), user interface intuitiveness, collaboration features, and integration capabilities with existing systems.

“When the use of a system modeling language (e.g., SysML and its diagrams) does not consider how the model will be used by others, it results in unnecessarily complex system models,” warns Paweł Z. Chądzyński, Senior Director of Strategic Research at Aras. “As a result, the primary goals of MBSE are defeated: to better manage system complexities and minimize risks by enabling cross-discipline collaboration across engineering domains,” he adds.

In turn, the chosen MBSE tool should be evaluated for its scalability, performance with large models, and the level of vendor support and available training.

How can I properly apply MBSE?

Successful MBSE implementation requires a strategic approach, starting with clear objectives and a phased implementation plan. Organizations should begin with pilot projects that create real value rather than just didactic exercises. It’s essential to provide management support, comprehensive training to all engineers involved, ensure adequate budget and time allocation for the adoption process, and establish proper documentation of new MBSE processes.

The implementation should focus on creating a collaborative environment where engineers from different disciplines can work effectively while maintaining a single source of truth for system development.

What is MBSE in engineering?

MBSE is the formalized application of modeling to support system requirements, design, analysis, verification, and validation activities throughout a system’s lifecycle. It represents a shift from document-centric to model-centric engineering, using digital modeling and simulation to design systems.
This methodology enables better communication between stakeholders, reduces errors through early detection, and provides a more efficient way to manage complex systems. MBSE contributes to the a digital thread strategy that ensures consistency and traceability across all aspects of system development, from conception through retirement.