Model Based Systems Engineering: Value of Moving to Data-Centric Solutions

Across industries, more than 85% of enterprises are undertaking initiatives to improve their model based systems engineering capabilities (MBSE), especially in aerospace and defense. There’s a significant shift in how organizations conceive, design, and manage complex systems in commercial businesses.

However, government agencies are falling behind. For example, the Department of Defense reports that significant improvement is needed within the next year to better model data-centricity, data access, and interoperability. Moving from document-centric to data-centric is essential.

For many organizations, the current decision-making process is simply inefficient. Excessive reliance on “thick text documentation” has led to delays and misinterpretations, making it no longer a safe or effective method. Paper, spreadsheets, and digital documentation are insufficient to manage the complexity of today’s systems.

Complexity in Engineering Systems

Modern engineering systems are increasingly complex. Keeping all the various documents connected and in sync is nearly impossible, even with adequate underlying infrastructure and connectivity. This opens the door for serious challenges, including:

• Difficulty maintaining consistency in documentation
• Lack of a single source of truth
• More threats and vulnerabilities

In turn, these challenges produce:

• Inefficiency, increasing costs of labor, time, and quality concerns
• Difficulties in collaboration and document sharing
• Increased risk of errors and oversights

A Data-Centric Model Based Systems Engineering Approach

MBSE relies on a coordinated data-centric approach to produce a single source of truth. It leverages data with these core concepts:

• Model-Centric: Utilizes models as primary communication and documentation tools throughout the system lifecycle
• Integrated: Promotes collaboration and unity across engineering disciplines and stakeholders
• Iterative: Supports incremental development and refinement of models throughout the lifecycle
• Hierarchical: Organizes complex systems into manageable subsystems and components
• Analyzable: Enables analysis and simulation to evaluate system behavior and performance
• Traceable: Maintains clear documentation of relationships and dependencies between system elements
• Consistent: Enforces standardized modeling languages and practices for clarity and reliability
• Reusable: Facilitates reuse of models, components, and patterns across different projects
• Automatable: Supports automation of model based processes to improve efficiency and accuracy

In MBSE, the system architecture model (SAM) serves as the single source of truth. Separately, engineering simulation software verifies that what is in the SAM matches system requirements. The centralized computation center performs functions and stores results. These components work together to ensure that when updates are made to any one model, they are updated across all other models in the system to keep things in sync.

NASA uses this approach to create virtual models of systems in the design and planning phase. This model then becomes a single point of reference for every phase of the project. NASA’s Safety and Mission Assurance (SMA) team uses the model to perform assurance analysis throughout the project lifecycle and run simulations and tests in real time, confident that the data and models are always up to date.

Benefits of Model Based Systems Engineering: Data-Centric MBSE

Government agencies modernizing operations using a data-centric MBSE approach provides a range of benefits.

Improves Quality and Accuracy

Model based systems engineering significantly enhances the quality and accuracy of projects and operations. Providing a single source of truth reduces the errors and inconsistencies that often plague complex government systems.

Increases Efficiency and Speed

The threat landscape globally continues to evolve. Government agencies must be able to respond quickly. This requires more rapid iteration and automation of processes to achieve an effective response. By centralizing data and enabling quick model updates, agencies can respond more swiftly to changing requirements or emerging threats.

While there have been improvements in many areas of government, a report from the General Accounting Office (GAO) shows that more improvement is needed. For example, the GAO has stated that current US Navy shipbuilding efforts still lag behind leading commercial companies. Streamlining the design and decision-making process, coupled with a more data-centric approach, is necessary to accelerate production.

Simplifies Complexity

This data-centric engineering approach simplifies complexity by pooling resources and knowledge into a unified model. This approach allows for a better understanding of system interactions and more effective resource allocation.

Validating system architecture is essential to validate the underlying models. Once this has been done, testing enabled by model based systems engineering can streamline production and improve safety. The Federal Aviation Administration encourages contractors and manufacturers to incorporate this data-centric MBSE process into design and testing.

Enables Scalability and Flexibility

MBSE works across a wide range of applications and product development lifecycles. It is easily scalable and can adapt to changing conditions while keeping data aligned. This prevents the need to update various models and project software; everything is always in sync with the underlying source.

Enhances Traceability and Transparency

You get improvements in traceability and transparency in government operations, which is crucial for accountability. Every decision, change, and dependency is documented and traceable within the model, providing a clear audit trail and supporting evidence.

Early applications of MBSE principles in the design of military ground vehicles created an audit trail for decisions and changes, substantially improving the transparency and traceability from requirements to implementation.

Reduces Costs and Optimizes Resource Allocation

Implementing data-centric MBSE can lead to significant cost savings for government agencies, enabling more efficient resource allocation.

Data interoperability has long been a stumbling block for government agencies. The National Institute of Standards and Technology estimated that it cost $15.8 billion annually in 2004. While there have been improvements since then, MBSE's data-centric approach significantly enhances interoperability, potentially saving billions across various government sectors.

By identifying potential issues earlier in the development process, organizations can also avoid delays or rework. The ability to virtually prototype and test systems before implementation can also reduce costs.

Creating a Data-Centric Approach

A data-centric approach to model based systems engineering produces the infrastructure needed to design, test, and validate outcomes. With the pace of development today, this approach is crucial to accelerate time to deployment and keep costs under control.

You can eliminate the complexity and streamline your processes by getting expert guidance from Sumaria, driving a strategic focus on data-centricity.

Sumaria Systems is a reliable and trusted industry partner that uses a series of services, including advisory, assistance, and advanced analytics AI, to convert documents into integrated and interconnected digital models. With over forty years of experience, numerous ISO and CMMI Level 3 certifications, and a clean compliance record with UTD registrations in SAM.gov, Sumaria is a trusted option for government contract awards, with no history of suspension or debarment. Contact us during your next program to get support for the nation's vital missions with the highest degree of responsiveness, effectiveness, and efficiency.