The concept of digital twins is a trending topic today. But digital twins aren’t really anything new. What is new are the more sophisticated applications that are becoming possible – across more industries – with the adoption of new technology. Two of the technology advances making these new avenues of applications possible are the Internet of Things (IoT) and cloud computing. And today’s applications are just the beginning of what will become possible in the future.
In essence, a digital twin is a virtual model of a physical product or a process. Using a digital twin allows businesses to analyze their physical assets to troubleshoot in real time, predict future problems, minimize downtime, and even perform simulations to create new business opportunities.
Some of the earliest examples of digital twins were with computer-aided design software, or CAD. Engineers were able to create digital representations of structures before actually building them. NASA employed digital twin technology for pairing its Apollo missions. Today’s technology opens a much wider set of products and processes to digital twins thanks to rich data feeds that allow digital twins to be used throughout a product’s lifecycle.
We recently spoke with Monica Schnitger, president and principal analyst with the Schnitger Corporation, a market analysis firm that specializes in engineering software, about how digital twins are being used today and the exciting new possibilities that lie ahead.
Using Digital Twins to Change the Business Model
Digital twins are being used across a variety of industries with more use cases popping up regularly. One application is in aerospace. Aircraft engine manufacturers like General Electric and Rolls Royce can now lease and maintain the airlines’ engines and charge the airlines for “power by the hour”; that is, the number of hours they fly.
Schnitger explains, “What that means is that the makers of those engines have to have really good, solid information about the efficiency of the engine. They need to be able to predict when and how maintenance will be carried out, so that the makers can maximize the revenue that they get from that engine. They have to ask, ‘What do I need to know? Then, how do I get that data and then, how do I analyze that data? Finally, what decisions can I draw from it and what does that mean in terms of where I put people on the ground to do the maintenance jobs?’”
Another example is air conditioners. Rather than sell a unit to a customer, an air conditioning manufacturer can instead sell “cooling degrees” but maintain ownership of the physical air conditioners. Using sensors, the company is able to go beyond monitoring into predictive analytics. By analyzing weather patterns, the air conditioner maker can predict how customers are likely to use their air conditioners and plan ahead for its power needs to prevent power surges – and, therefore, downtime.
Digital twins can reduce risk by being employed in settings that pose physical danger to workers, such as wind farms and oil rigs.
Virtual and augmented reality (VR and AR) can also be deployed for digital twin technology, Schnitger says. For example, a car mechanic might use an AR headset to help identify the full maintenance history of a car, which appears as an overlay when looking at a vehicle.
Massive Effect on IT Infrastructure
To make digital twin applications possible – and effective – all the data related to a product has to be integrated and managed over its lifecycle. For example, if a product is producing real-time sensor data for the purposes of predictive maintenance, that data needs to be gathered and analyzed. Cloud and edge computing enable enterprises to turn data into insights.
“There is a cost implication as well as a bandwidth implication,” Schnitger says. “If you can figure out what sensor data is important to send somewhere else and what’s not important, then you’re not paying to transmit lots of useless data.”
The cloud is a cost-effective place for data storage. And many of the applications that run the technologies can live there as well. But for some purposes, edge computing might be the better solution because it exists away from centralized cloud computing and close to the sources of data, such as manufacturing equipment or sensors.
The Challenges Are Real
As promising as digital twins are, enterprises shouldn’t discount the number of potential roadblocks along the way to implementation. It’s important to be clear-eyed about these challenges:
Expense. A digital twin program isn’t free. It relies on sophisticated software, data storage, and sensors. Without a clear business case and identification of the issue needing to be solved, enterprises might write off digital twins as too costly to explore. You can bring down the cost of a digital twin strategy by employing a cloud-based solution, such as Oracle Engineered Systems that use cloud equivalents like Oracle Exadata Cloud@Customer and Oracle Exadata Cloud Service. They offer flexibility, scalability, agility, and cost savings to make a digital twin strategy a reality.
Data overload. At its heart, digital twins rely on large amounts of data to gain insights. Unfortunately, not all the data is relevant. “The vast majority of the data is not going to help us with anything,” Schnitger acknowledges. “It’s that one tiny piece in the middle of an overwhelming stream that’s going to tell us something critical.” Enterprises can take advantage of fully built-out big data infrastructure like Oracle Big Data Appliance to wade through all the data. That allows businesses to leverage insights immediately without having to spend the time to develop a custom big data solution in-house.
Security. Even though much of the data from digital twins can be relayed through the cloud or over a public Internet, there are security concerns. Oracle Cloud@Customer allows enterprises to take advantage of the public cloud in their own data center behind their own firewall. It provides the flexibility and security needed for a digital twin strategy.
Ready to Adopt Digital Twins? Start Small.
If you’re planning to test a digital twin project, Schnitger recommends picking one small thing that you want to try to understand it and focus on what you need to do for it. Finding, gathering the data, and sanitizing it is a huge issue, so stay tightly focused. Another challenge is the business case.
Schnitger emphasizes, “This isn’t necessarily easy or cheap to create, and so if you’re not clear on what it is that you’re trying to solve, you're not going to succeed at this. And one of the things that works really, really well in doing this whole digital twin exercise is creating some sort of a pilot or sandbox where someone who cares about that data is responsible for answering the question of how I can make this better, or cheaper, or whatever the particular question is. Prove the success and the business case and then get bigger.”
A good place to start is maintenance because that’s the low hanging fruit. “It’s a good way of both proving that you can do this, because that’s a big hurdle, and then you can say there’s a benefit to do it and, therefore, it should be scaled,” she adds.
Oracle Engineered Systems and cloud-ready solutions help enterprises address today’s infrastructure complexity and maintenance issues while preparing for tomorrow’s shifting market demands.
What Does the Future Hold?
Finally, we had Schnitger give us a glimpse of the transformational change that digital twins can bring: “We start having all of these opportunities for people to change the structure of the way that their industries currently work, and that’s really exciting because it means, ultimately, we will wind up with a much more efficient ecosystem in whatever industry we’re in.”
Monica Schnitger is Founder, President, and Principal Analyst of the Schnitger Group. She has developed industry forecasts, market models, and market statistics for the CAD/CAM, CAE, PLM, GIS, infrastructure and architectural/engineering/construction and plant design software market since 1999. She holds a B.S. in Naval Architecture and Marine Engineering from MIT and an honors MBA from the F.W. Olin School of Management at Babson College.