What is Model-Based Systems Engineering (MBSE)?

Model-Based Systems Engineering (MBSE) refers to the use of 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 to managing the increasing complexity of modern systems.

Benefits of model-based systems engineering

MBSE is highly effective due to its structured, visual, and integrated approach to managing complex systems throughout their entire lifecycle. This is because of the use of system modeling languages like SysML. 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:

  • Centralized definition of design intent, with interactive models that enable design teams and other stakeholders from across the organization to reference 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 with digital models that modern PLM platforms, including change management, revision control, access controls, and other PLM lifecycle services, can manage

Main components of model-based systems engineering

MBSE methodology includes several key components. Together, they create a complete, virtual definition of a system and facilitate interdisciplinary integration between stages of product development.

Systems model

A formal, structured representation of a system using various representations and abstractions that capture its components, behavior, relationships, and interactions in a way that can be used to analyze, design, and understand the system. It serves as a blueprint for how the system is constructed and operates, providing a shared visual reference for engineers, stakeholders, and developers throughout the system’s lifecycle.

Modeling language

A formal modeling language is used to create and evolve system models.

Requirements management

Functionality that captures and traces system requirements throughout the entire development process, where requirements are linked directly to the system model elements, ensuring that design decisions align with the specified requirements.

Simulation and analysis (SAM)

MBSE incorporates 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 tools for simulation, such as MATLAB/Simulink, to perform advanced analyses.

Lifecycle management

The centralized computation center provides a consolidated location for the SAM, all simulations, and any other functions involved in the MBSE process. It enables team members from across departments to access the same real-time information and make changes immediately visible to other teams.

The model-based systems engineering workflow

The typical MBSE process includes the following steps:

Step 1. Establishing requirements
First, the engineering team develops 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 design a system that meets the established requirements. They use MBSE to create visual models of each component within the system. It also includes functionality that allows them to map each part of the system back to the original requirements.

Then the team models how the system will perform under various conditions, helping them understand how the system will react to use cases in the real world.

Step 3: Analyzing risks
The engineers then run risk analyses against the system, including technical performance, costs, safety, etc. Then, 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 manufacturing the system, ensuring it meets requirements and standards.

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

Use cases for model-based systems engineering

Several industries leverage MBSE to centralize system development and production. Here are a few examples of product-intensive businesses that rely on MBSE for business success:

  • The aerospace and defense industries develop 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 it would take with manual processes and disparate tools.
  • The medical industry leverages MBSE to create safe, compliant devices with minimal recalls.

Model-based systems engineering challenges

Teams setting up and using MBSE to build complex products face a few common challenges. They include:

Cultural resistance and organizational change

Shifting from document-based systems engineering to MBSE often requires a cultural change within the organization. Team members who are accustomed to traditional methods may resist adopting new tools and processes. This includes training and skill development on the use of modeling tools and the resulting models. It also requires greater collaboration between different engineering disciplines (software, hardware, mechanical, etc.) where the teams may be siloed, and fostering interdisciplinary collaboration may require significant effort.

Tool integration, compatibility, and lack of standardization

Since MBSE methodology relies on a variety of tools, it must work together seamlessly. However, this is not always easy to accomplish. To avoid complications related to integration, teams need to collaborate when building out the MBSE methodology and making decisions about their tech stacks.

Initial investment costs

The cost of acquiring and maintaining tools can be high, particularly for smaller organizations. In addition to tool costs, organizations must invest in training their engineers and other staff to use MBSE tools and practices effectively. This requires management commitment, time, and financial investment, which can slow the adoption process.

Getting started with model-based systems engineering

Establishing MBSE within your organization requires the right technologies and processes. Many teams take an agile approach to strategically roll out features that support MBSE. 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.

A technology 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. Centralizing product data and models from 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 specifically designed 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. We also offer advanced digital twins for building user-friendly models of any asset or system of assets.