Exploring digital twins: The future of digital transformation

According to one recent forecast, the number of Internet of Things (IoT) devices worldwide will almost double from 15.1 billion in 2020 to more than 29 billion IoT devices in 2030.
 
Despite this rapid shift, some devices particularly in industries with large, sensitive, or complex machinery — remain incredibly difficult to digitalize.
 
Enter digital twins, or virtual models of physical objects. These allow you to monitor performance and troubleshoot all kinds of devices remotely. They can also bring huge benefits for people in industries that are more resistant to digitalization.
 
One of our Senior Product Managers, Lukas Bertram, has lots of experience working with digital twin technology as part of TeamViewer Frontline’s integration with Siemens Teamcenter. I spoke with him recently about digital twin technology, its current use cases and benefits, as well as its possible applications in the future.

What are digital twins?

At their simplest, digital twins are digital representations of physical objects. However, they also can be visual models of systems or processes. Digital twins are commonly used in aerospace, construction, and manufacturing but are becoming increasingly popular in other industries too.

Digital twins have been in use at least since the 60s, when NASA used them to test its spaceships. Famously, Apollo 13 experienced huge problems after take-off in 1970. The presence of a proto-digital twin allowed ground staff to test solutions and eventually get the crew safely home.

However, the term ‘digital twin’ is relatively new, dating from 2002. Over the last few years, as Lukas puts it, “more companies are realizing that they have to use this technology to be competitive going forwards.”

How digital twins work

Within industry, digital twins are virtual replicas of physical assets, processes, or systems. They can use real-time data and simulations to optimize performance, predict issues, and even improve decision-making.

In TeamViewer’s case, Frontline’s integration with Siemens Teamcenter, a product lifecycle management (PLM) platform, allows digital twin technology to be used for after-sales, i.e., once the product has been passed onto the customer.

As Lukas describes it, the PLM software hosted the 3D models of the product and could be used by anyone working on it. However, Siemens realized that the 3D models were only being used at the beginning of the product life cycle. “As soon as the product leaves the facility and is being sold,” Lukas points out, “that data doesn't do anything anymore.”

By integrating TeamViewer Frontline, the 3D data can be used after the sale of a product, which improves troubleshooting and reduces costs for the manufacturer. Instead of having to send out their own technicians to fix machines, they can use digital twin technologies to support their customers to address problems at their source.

Applications of digital twins

Digital twins can be used across almost every industry that creates products of any kind. But they’re particularly useful in low-margin industries that already rely on huge amounts of 3D data: fields like aerospace, transportation, large-scale manufacturing, and infrastructure.

Within these fields, digital twins are often used for training. This makes sense: As Lukas points out: “Usually, our customers have or build machines or objects that are fairly large, so that you cannot really give every technician the opportunity to work on the physical object to train them.”

By training workers on 3D models of huge and costly machines, which are often located on the other side of the world, processes can be sped up and optimized. All of which can mean huge cost savings.

Another application of digital twins is standardizing and optimizing workflows, which is being explored by Hymer, a German manufacturer of premium motorhomes (video below).

Using digital twin technology, Hymer is creating detailed 3D models and spatially pinned work instructions to visualize vehicle parts and workflows for production processes. Not only that, it’s also using the data for training with immersive 3D workflows.

Benefits of digital twins

Let’s stick with the training use-case. The key benefit here is that people can learn and work on a digital twin in a scenario where, as Lukas puts it, “nothing can break.”

“You can let people work on a digital object like it is physically in front of them because they see it in the room in augmented reality,” he says, “They can touch and move parts so they experience it as if it were there, but in an environment where they cannot fail.”

This is clearly of huge value in certain highly-specialized and/or costly industries — infrastructure, for example — where it’s simply not possible (or advisable) to allow trainees to develop their skills on real-world projects.

Digital twins also allow end-users to see things that are not visible in real-life. For example, an airplane engine can be taken apart and put back together in digital space. This supports a form of seeing that is impossible in day-to-day life and, with it, as Lukas says, “a better understanding of what you’re looking at.”

Another key benefit is the improved scalability that comes with digital twins. Lukas gives the example of an aircraft engine. Traditionally, it’s only possible to train people on this engine by actually putting them in front of it. That means only a handful of people at the same time. If that.

But by using a digital twin of the engine for training, Lukas says, “you can have hundreds of people around the globe working on the thing because they see it in front of them wherever they are.”

Which means cost reduction, of course, but also greater efficiency across the entire life cycle of the product.

Challenges and limitations of digital twins

While digital twins have broad usability across industry, Lukas points out two challenges that might impact the decision to implement them.

The first challenge is that there might be a separation between the 3D data and the real-world object. Let’s take the aircraft-engine example again. Say for instance the engine needs to be altered in some way. If the 3D data is not fully updated, then this change will mean a lot of manual work.

With the TeamViewer integration, however, any changes in the raw Siemens data are passed on automatically to the step-by-step processes, getting rid of the need to update everything as soon as anything changes.

Another challenge is data security. Simply put, with digital twins you are essentially translating your intellectual property into 3D models. Companies are understandably wary that this information could be leaked.

But there is a workaround, in that you can decide what level of information is available in the digital twin. Often, a small section or group of features is enough. As Lukas puts it, “in a training scenario, you probably don't need every single screw.”

Alongside clear security guidelines, reducing the information available to the software protects the company from the risk of theft.

The future of digital twins

As with most technology, AI and machine learning are expected to play more and more of a role in the creation of digital twins.

Because while it might seem counterintuitive, digital twins are still pretty manual. Usually, they are made operational with the help of step-by-step PDFs, which are created manually.

In the future, Lukas predicts that generative AI will do a lot of this legwork, probably “taking the PDF's input and creating step-by-step instructions and mapping that onto the right positions of the digital twin.”

He also predicts that AI will play a big role in correcting errors, and making sure that the right steps are followed when interacting with a digital twin. All of which could be a really positive force in reducing errors as well as downtime.

But Lukas points out that there is still work to be done on the hardware side of digital twins. The software is already there; what’s really needed is high-quality hardware — something like small, lightweight glasses — capable of generating more immersive experiences. This could drive digital twin adoption in industries still resistant to digitalization.

Case studies

Hymer is just one of the companies starting to use digital twins to improve their training and workflows, but they are being used widely across industries that are traditionally difficult to digitize.

One of the main industries Lukas works with is aerospace. He tells me about an aircraft-engine manufacturer that recently realized there was a problem with the efficiency of its engines. This meant that airlines needed to make changes to all their engines — all over the world.

Typically, this might be an easy fix. But in this case, it was a lot more complicated, involving hundreds of parts. It would take a team four days just to fix one.

Additionally, each plane has two engines, and only one can be fixed at a time. This means the plane stays grounded for at least eight days, causing huge losses for the airlines.

When every day of downtime costs millions, the airlines couldn’t afford to decipher PDF instructions at the airport. By using the TeamViewer solution, they were able to train their workers quickly on digital models — eliminating the need to fix the real thing and then restore it to its original ‘faulty’ form over and over again.

By working this way, the airlines could train many more people and roll out the engine changes much more quickly.

Having the 3D data available is also a huge benefit for any issues that arise in the future. Instead of waiting for the manufacturer to address the problem, they can simply consult the models from their own hangars and get right to work.

Getting started with digital twins