Implementing Digital Twin Standards: A Response to AIAA’s Digital Twin Position Paper

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Digital Twin

Implementing Digital Twin Standards: A Response to AIAA’s Digital Twin Position Paper

In February 2021, the American Institute of Aeronautics and Astronautics (AIAA) and the Aerospace Industries Association(AIA) released a joint position paper on the definition and value behind the digital twin. With industry experts across academia, industry, and government on the committee for this publication (including the likes of Jayendra Ganguli of Raytheon, Don Kinard of Lockheed Martin, and Olivia Pinon Fischer of Aerospace Systems Design Laboratory), the paper is a triumph in providing a coherent view on a digital twin definition, values, and direction in order to take full advantage of its capabilities.

This paper clearly aligns with what Vertex is seeing in the marketplace regarding digital twin needs and benefits across manufacturing. Most significantly, the digital twin can clearly provide significant value anywhere along the product lifecycle, as seen in some key metrics and results from the paper:

  • Up to a 75% reduction in design cycle time
  • Up to 8x reduction in testing
  • 40% reduction in maintenance costs and $11M in cost avoidance through detection and avoidance of three failures

With these benefits across the lifecycle and conversation around digital twin increasing, digital twin implementation projects will only continue to accelerate. But as defined in the position paper, the manufacturing industry must address one activity in parallel: standardization. By setting up standardization and taxonomy before implementation, the industry can ensure application and usage is more meaningful. Through standardization, every digital twin will be defined and built in a similar way, which gives industry, academia, and government entities a common framework and “language” to speak from.

The Importance & Challenges of Digital Twin Standardization

At my previous company, I saw firsthand both the importance and challenges of implementing standardization. Back in 2008, the world of accounting didn’t have a standard framework or taxonomy for their reported financial data. Even if two organizations were using the exact same rhetoric in describing a financial concept, the meaning behind the terms wasn’t always the same. (This “same words, different meaning” approach is very common in the manufacturing world, as seen in countless blogs from thought leaders across our industry.) To solve the problem, the standards body FASB released XBRL, a taxonomy and standardization allowing companies to essentially “tag” their financial concepts from the approved taxonomy.

XBRL is meant to provide transparency. But when it first launched, it also created a lot of confusion and administrative work to meet the requirements. The accounting world changed overnight, and companies were left to figure out an approach to achieving compliance in a relatively short time. The company I worked for at the time developed innovative software to smooth out the transition and give companies the support they needed in the time of transition.

What are the takeaways from this story? First, standardization is extremely important, and I don’t think anyone would argue that. But the education and ease with which the industry makes standardization is just as critical if we’re going to see widespread adoption and usage of such a valuable tool like the digital twin. Remembering how challenging the XBRL standard was for the accounting industry, I predict that the challenges for a digital twin standard and taxonomy will be tenfold.

Simplifying Digital Twin Standardization with 3D Visualization

Vertex is poised to help with this challenge by providing 3D visualization to the industry as a way to ensure clarity and context in digital twins. Taxonomies and standards come with definitions, clauses, compliances, and a whole host of other challenges to be aware of. At Vertex, we know that a picture is worth a thousand words, and a 3D model is worth a million. By looking at a model, users can know precisely the information they need to make smart, timely business decisions.

In the position paper, there are several digital twin applications listed. Among them, I see elements of how Vertex is currently supporting these use cases with our current customers by using 3D visualization of the digital twin. To name a few examples:

  1. Performance Monitoring. A digital twin helps companies virtually validate product performance while mirroring how they work in the field to understand performance across fleets, assets, and operators. 3D models provide a perfect context to overlay performance data and set up conditional triggers to highlight performance.
  2. As-Built Configuration. As-built configurations are rarely associated with engineering configurations, such as serial numbers, supplier disclosures, and added inspections. Vertex connects to enterprise tools such as PLM and MES to allow different BOMs (such as the eBOM and the mBOM) to link to different parts and show 3D visualization sessions. In this way, each entity can maintain their BOMs without having to understand another team’s BOM while still having full understanding and context of the product.
  3. Factory Simulation. Large-scale digital twins of an entire factory can help companies understand layouts, materials flow, tooling, and bottlenecks. Our cloud platform’s scalability is the only 3D digital twin platform that can render full factories including products moving through the factory in real-time. Other 3D systems cannot load or render these model sizes.

As was the case with XBRL, innovative software plays a critical role in supporting these use cases. With digital twins becoming more common and upcoming standardization, manufacturers need to take software architectures into account. Legacy systems rarely support digital twin projects. Not only are they slow and unwieldy, but they don’t enable scalability without significant investment. Cloud-native architecture, like with the Vertex platform, operates at high speeds to deliver digital twins information in the end user’s context. With the cloud, manufacturers can deliver better, usable, purpose-built applications.

Preparing the Workforce for Digital Twin Standardization

My colleague Matt Heying has written in length about the importance of organizations preparing for the next gen engineer; but it’s also critical that we as an industry prepare the next gen engineer. One way the industry can take advantage of the incoming workforce is to ensure they are trained and taught early so they come in with a foundation of digital twin standards. The industry has seen a lot of success incorporating Industry 4.0 concepts into curricula, as we’ve seen with things like model-based definition at Purdue University. These are great first steps towards true adoption of the digital twin.

Although AIAA’s paper is specific to the aerospace industry, I believe everyone in the manufacturing space would benefit from the positioning in this paper. The ability to quickly get digital twins to anyone in the product lifecycle gives companies significant value regardless of industry. Vertex is helping customers across the industry to build and deploy purpose-built 3D applications—from idea to application—in under 30 days with our low-code platform for 3D visualization. To learn how we can support your digital twin initiatives, visit


About the author

Mike SellbergThis article was written by Mike Sellberg and originally it was published here. Mike has over 25 years of experience in software development, product strategy, product marketing, and operations management. As Vertex VP and Chief of Staff, Mike manages product strategy, marketing, customer success, and solutions architect initiatives, ensuring an aligned vision and execution of the Vertex platform. Mike spent most of his career in the PLM industry starting with Engineering Animation, Inc. (EAI) where he was Divisional General Manager. After EAI was acquired by Unigraphics Solutions (now part of Siemens PLM), Mike led the Teamcenter product management team.