Digital Twin Technology: Where Are We Now?
Digital twin technology as it applies to IIoT is still in early stages, but the fiercely competitive nature of the industries most suited to reap its benefits continues to drive innovation, analysts said.
The high-stakes oil and gas, aerospace and defense, transportation and manufacturing sectors likewise can make the types of investments in digital modeling and analytics that are relatively inexpensive compared to the cost of developing a refinery or new jet engine.
“It’s imperative to know what’s going on, when something is going to break or is performing improperly,” said Ian Hughes, Senior Analyst, Internet of Things, for 451 Research. “Having more accuracy to then put it all together and do more interesting processes with the data, with machine learning and AI, to get a few extra percent improvement … it’s a no-brainer.”
The digital twin, which Gartner defines as “a digital representation of a real-world entity or system,” differs from a 3D model or a save-as version of a system architecture that you can sandbox. Conversely, a digital twin collects real- or near-time data from IoT sensors. And once it has that data, it analyzes the information to improve products and systems.
Whereas a 3D model captures an object’s geometry, a digital twin incorporates data from connected sensors and potentially physics engines as well. A digital twin can be used to demonstrate how discrete components in a system interact with one another.
As of the first quarter of 2019, according to Gartner research, 24% of organizations with IoT technologies in production or IoT projects in progress use digital twins. Another 42% plan to use digital twin technology within the next three years.
Their popularity, again according to Gartner, lies in the digital twin capabilities that “significantly decrease the complexity of IoT ecosystems while increasing efficiency.”
Beyond physical meters and dials by which manufacturing information is traditionally gleaned, the digital twin approach can “gather data from sensors, process the data using analytics, and use it for predictive maintenance and optimization,” said Scott Raynovich, principal analyst, Futuriom.
Among practical industry scenarios, the most common are wind turbines, energy rigs and aircraft engines. And as IoT technology proliferates, Raynovich said, the digital twin approach will become more common across industries to provide insight into existing operations and “things,” and envision new opportunities and model “new things.”
In principle — but in limited practice — digital twin technology can incorporate data from an array of systems, said Alexandra Rehak, practice leader, IoT, with Ovum Consulting.
“Digital twin is far from a mass market technology at this point,” she said. “It’s complex, not something off the shelf.” To be sure, Rehak added, the cost-benefit analysis of deploying a digital twin is an important consideration for enterprises.
Most early use cases are in automotive design and manufacturing, she said, where you have an intricate manufacturing process and a complex product that is constantly redesigned to include new features and technology.