Exploring the Impact of Model-Based Systems Engineering with Model-Based Collaboration

Traditional systems engineering frameworks, with their manual updates and siloed documents, often struggle to keep pace with evolving project requirements. The result? A complication of errors, inconsistencies, and inefficiencies that can derail a project. In fact, a study by the International Council on Systems Engineering (INCOSE) found that using Model-Based Systems Engineering (MBSE) can reduce system design errors by up to 55% and decrease project cycle time by 40%, making it a compelling alternative to traditional systems engineering frameworks”​ (INCOSE)​​

Model-Based Collaboration Applied to Systems Engineering

Systems engineering is crucial in industries such as aerospace, defense, automotive, and telecommunications, where the complexity and interdependencies of system components necessitate a structured and methodical approach to development and management. It encompasses the design, integration, and management of complex systems throughout their life cycles, ensuring that all aspects of a project or system are considered and integrated into a cohesive whole—from concept to design, production, operation, and eventual decommissioning. This approach employs tools and methodologies to analyze and optimize system components and their interactions, balancing performance, cost, schedule, and risk. Capturing systems-level requirements is especially important, as it provides a comprehensive view of what the entire system must achieve, ensuring that all components work harmoniously towards the project’s objectives.

Model-based collaboration leverages digital models to bring team wide visibility to complex multi-disciplinary projects. With proper access control, digital models ensure that all collaborators have real-time, direct access to the simulation models and their results, fostering a common understanding and shared context of simulation data. When applied to systems engineering, model-based collaboration becomes a powerful approach to ensuring that teams are able to innovate despite the increasing complexity of products. This integration enables seamless communication and coordination among team members, ensuring that every aspect of the project aligns with the overarching system requirements and objectives.

The Traditional Approach: A Bottleneck in System Development

In response to increasing global competition and the growing complexity of customer demands, in the 1960s and 1970s, businesses were searching for new approaches to ensure their products met market expectations. The House of Quality, a foundational framework within the Quality Function Deployment (QFD) methodology, revolutionized how businesses integrate customer needs into product design. This framework provided a structured matrix that translated customer requirements into specific engineering characteristics, ensuring that the final product aligned closely with market demands. Today, the principles of QFD continue to be integral to modern systems engineering, facilitating a comprehensive approach to requirements management and systems design.

However, traditional approaches to systems engineering didn’t account for shifts in market needs, customer preferences, and internal engineering requirements. QFD and House of Quality assumed a static set of requirements which had to be collected at the beginning of a project. They also assumed a waterfall relationship between several QFD matrices. In reality, both the customer requirements and the engineering specifications are constantly shifting requiring a more dynamic system. Historically, systems engineering processes were file-based and changes to the customer needs or product specifications were updated manually, making it difficult to track evolving requirements and meet product delivery deadlines.

Further, different engineering disciplines built their own test facilities to validate the products. For example, test benches for vibration testing and wind tunnels for aerodynamics. As testing became more computational, each discipline invested in its own methods and tools. Since the 1980s, different engineering disciplines have relied on specialized simulation software tailored to their specific needs. For example:

• Exterior Aerodynamics: Computational Fluid Dynamics (CFD) software is commonly used to analyze airflow around a vehicle in combination with test data from wind tunnel testing.
• Noise, Vibration, and Harshness (NVH) Analysis: Linear structural analysis tools are preferred to study the vehicle’s response to vibrations and acoustic characteristics in combination with bench and track testing.

While these tools are highly effective for their intended purposes, they often operate in isolation, creating silos that hinder collaboration and integration of insights across disciplines. Model-based collaboration provides us with the multi-disciplinary framework to update models in a complex system of systems in an automated fashion, thereby achieving the dynamic system of records needed to meet real life complexity.

Model-Based Collaboration Streamlines Meeting Engineering Requirements

Car development today involves many teams developing systems such as powertrain, structural components, and electronics. Design decisions for these systems often have related impacts on weight, rigidity/strength, and aerodynamics. Analyzing these performance measures is important to ensure the best trade-offs are made, optimizing the overall performance and efficiency of the vehicle.

System-level requirements serve as the foundational blueprint to enable optimal tradeoff decisions, detailing what the system must achieve and ensuring each component contributes to overall goals. These requirements are broken down into specific, actionable items and mapped to design elements, promoting coherence and traceability. This process enhances design accuracy and efficiency by aligning team efforts, preventing scope creep, and mitigating risks. Effective management of system-level requirements ensures the end product meets stakeholder needs and regulatory standards while balancing performance, cost, and schedule. This holistic approach is crucial for the successful execution of complex projects, managing interdependencies, and ensuring project success.

Step 1 – Requirements Capture: The process of identifying and documenting stakeholder needs.

Requirement capture starts with identifying and involving all stakeholders, such as customers, users, regulatory bodies, and internal teams. Techniques like user interviews, workshops, surveys, and direct observation are used to gather stakeholder needs and expectations. These requirements are then documented clearly and concisely in a standardized format to ensure they are understandable and actionable. This step is critical as it turns abstract ideas into concrete requirements, providing a roadmap for the design and development phases. Effective requirements capture ensures the final system meets stakeholder expectations and regulatory standards, reducing the risk of costly redesigns and delays.

Requirement capture can be broken down into three essential steps:

1. Stakeholder Identification: Identify all stakeholders, including customers, users, regulatory bodies, and internal teams.
2. Requirement Elicitation: User interviews, workshops, surveys, and observation to gather requirements.
3. Documentation: Document requirements clearly, concisely, and unambiguously using a standardized format.

Step 2 – Requirements Analysis: Categorizing gathered requirements to ensure they are feasible, consistent, and complete.

This process includes classifying requirements into functional, performance, regulatory, and non-functional categories. Each requirement is assessed for technical and economic feasibility and checked for any inconsistencies or gaps. By meticulously analyzing requirements, organizations can identify potential issues early, ensure all necessary requirements are captured, and maintain alignment with stakeholder expectations, ultimately enhancing the accuracy and efficiency of the system development process.

1. Requirements Classification: Categorize requirements into functional, performance, regulatory requirements and non-functional.
   • Functional: For example, the cooling system of the electric vehicle battery pack must be capable of maintaining the battery temperature between 20°C and 35°C under all operating conditions, including rapid charging and high ambient temperatures, to ensure optimal performance and longevity of the battery cells.
   • Performance: For example, the cooling system of the electric vehicle battery pack must be able to dissipate at least 5 kW of heat and reduce the battery temperature from 40°C to 30°C within 10 minutes during peak load conditions to prevent overheating and ensure efficient battery operation.
   • Regulatory: For example, the cooling system of the electric vehicle battery pack must comply with UNECE Regulation No. 100, ensuring that all components and fluids used in the system are non-toxic, non-flammable, and environmentally safe, to meet international safety and environmental standards for electric vehicle battery safety.
   • Non Functional: For example, the cooling system’s control interface must be user-friendly, allowing technicians to easily monitor and adjust cooling parameters with a response time of less than 2 seconds for any user input or status update.
2. Feasibility Analysis: Evaluate the technical and economic feasibility of each requirement.
3. Consistency and Completeness Check: Ensure no conflicting requirements and all necessary requirements are captured.

Step 3 – Requirements Modeling: The creation of executable models and diagrams to represent and analyze requirements

1. Modeling Techniques: Use SysML or other modeling languages to create requirement diagrams, use case diagrams, and activity diagrams.
2. Traceability: Establish traceability links between requirements and other model elements.
3. Behavioral Models: Develop models that describe the behavior of the system in response to different requirements.

Step 4 – Requirements Traceability: The ability to link requirements to design elements, implementation, and verification to ensure completeness and manage changes

Requirements traceability in Model-Based Systems Engineering (MBSE) ensures that every requirement is linked to its corresponding design elements, implementation, and verification processes. This involves creating a traceability matrix that maps requirements to design components, test cases, and verification methods. Traceability facilitates impact analysis, allowing teams to understand the effects of requirement changes on the system. By maintaining clear traceability, organizations can manage changes effectively, ensure all requirements are addressed, and verify that the final system meets stakeholder needs and compliance standards.

1. Traceability Matrix: Create a traceability matrix to map requirements to design elements, test cases, and verification methods.
2. Change Management: Implement a process for managing changes to requirements and updating traceability links.
3. Impact Analysis: Perform impact analysis to understand the consequences of requirement changes on the system.

Step 5- Requirements Verification and Validation: The process of checking that the system meets specified requirements and stakeholder needs through testing and reviews

1. Verification: Conduct reviews, inspections, and tests to verify that the system meets the specified requirements.
2. Validation: Validate the system against stakeholder needs and requirements through simulations, prototypes, and user testing.
3. Documentation: Document verification and validation results, including any discrepancies and corrective actions.

Effective requirements management in MBSE is essential for developing systems that meet stakeholder needs and perform reliably. By capturing, analyzing, modeling, tracing, and verifying requirements systematically, organizations can ensure the success of their systems engineering projects. Implementing best practices further enhances the efficiency and accuracy of the requirements management process.

Best Practices for Model-Based Collaboration

1. Continuous Stakeholder Involvement: Engage stakeholders throughout the requirements management process to ensure their needs are accurately captured and addressed.
2. Iterative Approach: Use an iterative approach to refine requirements and models continuously.
3. Automated Approach: Leverage metadata automation to update models, results and reports so that the most up to date insights are visible to all collaborators.
4. Training and Competency Development: Provide training to team members on MBSE and requirements management tools and techniques.

Model-Based Collaboration on Rescale

Rescale’s platform enhances model-based collaboration by streamlining and securing simulation processes, allowing multidisciplinary teams to work together efficiently. It automates the capture and management of simulation metadata, ensuring critical parameters and performance indicators are systematically recorded and categorized. This standardization enhances data searchability and organization, enabling quicker retrieval and informed decision-making. Engineers and scientists can share simulation jobs and results seamlessly within and across organizations, ensuring all stakeholders have access to consistent, accurate data. Supported by Rescale’s high-performance computing capabilities, this collaborative environment enables rapid simulation and analysis, reducing the time and cost associated with product development. Visualization tools present trends and insights from the data, facilitating better analysis and strategic choices. Additionally, integrating AI and machine learning enhances predictive analytics, providing deeper insights and more accurate predictions based on historical data.