System Modeling: Real-World System-to-System

The simplicity of early integrated circuits can seem quaint today as engineers grapple with increasingly complex systems for high-stakes fields like defense, transportation, and healthcare. To be completely accurate, though, such designs aren't single systems but collections of interrelated subsystems. Their interactions provide the overall system with functionality and purpose, enabling it to do its job.

That makes it sound easy, but engineers cannot simply "daisy chain” the systems together, flip a switch, and expect perfection. Interactions between various subsystems can produce unexpected and unwanted results. Interdisciplinary engineering teams must anticipate and address these issues to prevent costly and shocking failures.

The stakes are even higher in mission-critical government projects. Program officers, acquisition specialists, and end users must manage various risks, particularly financial ones. With certain acquisition programs valued in the hundreds of billions, the consequences of failure are enormous.

That presents a challenge: how do you model these complex systems, predict their interactions, and optimize for efficiency, affordability, and sustainability without committing time, money, and resources to full-scale development that may ultimately go awry?

The solution lies in collaboration. Multidisciplinary teams can apply their expertise in software, electrical, and mechanical systems to identify and solve problems, integrating individual components into a functioning whole through complex system modeling.

Choosing the Right Methodologies for Complex System Modeling

There is no one-size-fits-all approach to complex system modeling. Each project has its own challenges of scale and complexity and therefore, its own unique solutions. Engineers might use several different methodologies to build confidence that their systems are coming together.

Historically, modeling complex systems has been a manual process. Engineers comb requirements documents and translate them onto paper for tracking, which requires math and trial-and-error. Spreadsheets have helped digitize modeling processes, but work largely continues piecemeal, one subsystem at a time.

The digital age has supported the development of new techniques that provide more visibility into predicting performance and finding paths to optimization. Generally, these new techniques all have a similar goal: to break down large-scale problems into smaller pieces to isolate, study, and forecast system interactions. After all, you can’t build a house unless you know that the foundation is composed of appropriate materials and is strong enough to hold up the rest of the structure.

Here’s a rundown of three common system modeling methodologies for large-scale projects:

• The verification and validation model. Also commonly referred to as the “V Model” for testing and validating system performance, it breaks the development of complex systems into phases. These fit into a V-shaped diagram, with verification steps on the left and validation on the right. Verification starts with requirement gathering and includes system analysis and module design. Testing activities populate the validation leg of the V. Coding sits at the apex, supporting the legs. The idea is to link phases of development with a corresponding testing task to ensure quality and reliability.
• Agile/Spiral development. Complex systems can be prone to “drift into failure,” so Agile and Spiral development tends to be effective for maintaining and verifying progress without going “all in” on an initial concept. Agile and Spiral break down larger development cycles into shorter “sprints” focused on frequent testing, adaptability, and continuous engagement across the development teams.
• Concurrent engineering. As the name suggests, system modeling can be handled through concurrent engineering. This method highlights the power of breaking big challenges into smaller pieces. Concurrent engineering leans heavily on cross-functional collaboration, roughly analogous to how pit stop crews work together during a race. Design and manufacturing stages happen in parallel rather than sequentially to avoid enabling any errors in system performance to cascade to other design elements.

These and other methods can be applied differently, depending on the circumstances of individual projects. Project managers may use a combination of techniques depending on desired solutions. Complex modeling involves a series of tradeoffs to enable engineers to make educated projections of what might happen, recognizing that system interactions are not always linear.

While there's no one-size-fits-all approach, one particular methodology is gaining traction: model-based systems engineering (MBSE). This approach provides a framework for managing complexity and improving communication across disciplines throughout the design process through enhanced visibility via an integrated visual model.

Using MBSE for Complex Systems

Many organizations, particularly resource-challenged government agencies, are recognizing the power of MBSE in forecasting system performance. It provides a framework for unifying different project phases—requirements, design, analysis, verification, and validation—using modeling special software designed for this purpose. As its name states, it unifies these stages through a single centralized model.

MBSE aims to provide a “single source of truth,” enabling stakeholders to work across a shared operational picture. This way, professionals can collect data and understand cause-and-effect relationships in near real time.

Here’s a practical example, according to the Software Engineering Institute at Carnegie Mellon University. Engineers historically have added cybersecurity protocols later in the development process to address vulnerabilities once systems are closer to completion. MBSE, however, enables systems to be “secure by design,” a critical benefit for mission-critical government systems in defense and intelligence. The ability to provide information assurance when lives are on the line represents a significant advantage.

MBSE improves the modeling of complex systems, particularly in government, by fostering closer collaboration between agency personnel and contractors, enabling real-time decision-making and enhancing project management.

MBSE in Complex System Modeling: Case Studies

MBSE is increasingly proving its value across various applications, both in government and commercial markets. Here are a few examples of how engineers are using—or are considering using—this growing discipline to provide improved coordination and collaboration in working with complex systems:

• Defense technology: The Department of Defense has encouraged program offices and vendors to move toward a comprehensive digital modeling environment for several years. According to FedTech Magazine, the US Navy adopted MBSE in designing a “5G smart warehouse” at its Naval Base Coronado in San Diego. Teams used MBSE to streamline the risk management process, including accreditation processes.
• Aerospace: NASA has been a leader in embracing the adoption of MBSE for the federal government, as illustrated by working groups formed to study the methodology and assess challenges. The NASA assessment team specifically called out the need for MBSE as missions, such as plans to send astronauts to Mars, get more complicated.
• Healthcare: MBSE also has applications in addressing complex processes. For example, a 2023 research paper looked at the use of MBSE to model disaster response protocols at healthcare centers. Of note was the importance of computer simulations in designing processes and assessing resource allocations.
• Commercial applications: Facing continuing pressure to produce results with fewer resources, many professions are exploring MBSE to help streamline. According to Deloitte, auto manufacturing is tailor-made for the approach: “Instead of siloed teams working on specific components of a product—such as the interior, engine, and display of a car—integrated cross-functional teams are established, eliminating boundaries. This can enable teams to complete designs faster and be interconnected in a new way.”

MBSE is not a panacea, of course. It requires an enormous investment in time and upfront costs for software installations, training, and transitioning legacy systems, and some industries are further ahead in its adoption than others. In the future, MBSE will provide significant advantages as artificial intelligence will play an increasing role in modeling complex engineered systems. Ultimately, the value of integrated systems modeling is paying off.

The Future of Complex Systems Modeling

The increasing sophistication of digital technology is enabling engineers to think bigger than ever before in conceiving what’s possible for military, intelligence, healthcare, and other sensitive and complex systems in government and commercial markets.

Particularly as it pertains to the government, agencies and their leaders would be wise in considering vendors with demonstrated expertise in MBSE, such as Sumaria Systems.

Sumaria Systems, a reliable small business, has been a trusted industry partner to the US government for over forty years. As a CMMI Level 3, ISO 9001, and ISO 27001 certified contractor with current registrations in SAM.gov, Sumaria has a clean record of never being on the debarred or suspended list. Its leadership, composed of former defense and intelligence community leaders, has steered the business to current and past contract performances for which it consistently receives exceptional ratings. Contact Sumaria to help solicit, negotiate, enter into, and manage contracts between the government and potential contractors.