The birth of 30 million digital twins: Transforming the engineering lifecycle and manufacturing feedback loops

August 08, 2017

Digital twins are combining with product line engineering software to transform manufacturing. But the process for mapping these technologies to the engineering lifecycle is still in its infancy.

The “digital twin” was first introduced to mainstream technology by General Electric back in 2015, and has been assimilating into Industrial Internet of Things (IoT) hierarchies ever since.

Digital twins are virtual representations of physical products that, when combined with connectivity, enable device makers to interact with their equipment long after it has been deployed in the field. From an engineering perspective, digital twins can be used to monitor the impact of operational or environmental wear and tear on system components, as well as simulate failures before they occur. From a business standpoint, on the other hand, digital twins allow companies to track customer behavior and usage patterns in an effort to provide improved products, features, and services.

Although the digital twin concept is still in its relative infancy, large-scale manufacturing has been quick to adopt the technology. In the automotive market, for example, digital twins are being integrated with product line engineering (PLE) platforms to help manufacturers manage the immense variation in makes, models, and features of vehicles across their fleets. Today this helps ease the burden of events like recalls, as digital twins based on PLE data can automatically indicate whether or not a particular unit has been equipped with an affected feature. Tomorrow’s objective, however, is a true digital twin for every vehicle that leaves the dealership.

Using digital twins, it is possible for manufacturers to extract detailed diagnostic data and match it against the precise options of a specific vehicle to determine if maintenance is required or how an over-the-air (OTA) firmware upgrade will influence performance based on a given feature set. Dr. Charles Krueger, founder and CEO of PLE company BigLever Software, Inc., foreshadows this as “the birth of 30 million digital twins a year.” 

“As we get more complex systems out in the world, it’s not like the old days where you put together a mechanical thing, sold the car to the consumer, and the cord was cut at the dealership,” Krueger says. “With the complexity of systems and the way they interact, companies are trying to keep track of their products as they are out in the world. They’d like products to ‘phone home’ and provide data through the IoT so that the manufacturer can still remain connected to the consumer and the product.

“In order to do that there’s a physical product in the world that the manufacturer is trying to keep a sufficient representation of in the office,” he continues. “As the product evolves and has maintenance issues, upgraded features, etc., [the digital twin] mirrors the real product as it exists in the digital world. It should provide sufficient representation in a digital form to stay connected to a product, understand its current state, and communicate with the owner of that product to provide the best user experience based on that continuing knowledge.

“The end game is that the manufacturers want a true digital twin for every product,” Krueger continues. “If you’re getting close to an oil change or there is some diagnostic information coming back from your car, they want that level of detail.”

Feedback loops and evolutionary design

To fully appreciate the transformational qualities of a digital twin, it's first important to understand how they fit within a product lifecycle. To understand how they fit within a product lifecycle, it's important to understand how large-scale manufacturers like car companies are able to efficiently manufacture fleets with massive amounts of variation.

Figure 1 shows the standard systems engineering V model, spanning requirements, design, and test, to user documentation. The upper tiers of the V represent the modeling and design phases where engineers build out various features based on product requirements, which waterfall down to subsequent phases of the development and manufacturing lifecycles.

Using software such as BigLever’s Gears PLE Lifecycle Framework, individual features of a given product are represented as pieces of source code that can be "turned" on and off, allowing assets to be generated based on a particular set of features. This accumulation of source code-based assets provides the foundation for creating physical products, but can also be leveraged simultaneously to create a corresponding digital twin. The physical product and digital twin can be bound using component serial numbers once the physical device is manufactured, and remain in contact throughout the product’s life to ensure that the twin is updated accordingly.

Figure 1. The Gears PLE Lifecycle Framework maps with the systems engineering V model of large-scale manufacturing to enable the simultaneous creation of physical products and their digital twins.

While digital twins have thus far been positioned as a reactionary monitoring mechanisms, their integration with PLE technology places them early enough in the development lifecycle that they can be used as part of a feedback loop to inform ongoing product engineering decisions. Data from digital twins can be leveraged proactively to show how assets based on a certain feature set perform under different conditions, and then be mapped back into earlier stages of the engineering lifecycle to enable dynamic, evolving system designs. Organizations can model and simulate these progressive systems before creating physical prototypes, or even create digital twins for systems that are no longer being produced.

Standardizing the PLE-driven digital twin: Paying it forward

Despite their success in corners of the industry, PLE and digital twins still face challenges on the road to widespread adoption. Both technologies recently emerged from the world of academia, and as a result suffer from the uniform implementation and widespread adoption that drive down costs and instill confidence in users.

In response, a working group at the International Council on Systems Engineering (INCOSE) has been established to create an ISO standard around PLE that defines technology, process, and workflow best practices for implementing PLE in broad manufacturing environments. The working group currently consists of members from General Motors, Air Bus, Boeing, General Dynamics, Raytheon, and BigLever Software, along with others who have a vested interest in reducing manufacturing and design complexity through the use of PLE and digital twins.

“The ISO standard is based on those companies who have been successful,” Krueger says. “We’re trying to create a clear and concise set of terminology and concepts to help organizations go down a lower risk path.”

With the birth of 30 million digital twins on the horizon, the pressure to do so is on.