Digital twin technology has moved beyond manufacturing and into the merging worlds of the internet of things, artificial intelligence and data analytics.
As more complex “things” become connected with the ability to produce data, having a digital equivalent gives data scientists and other IT professionals the ability to optimize deployments for peak efficiency and create other what-if scenarios.
Digital twin applications
Digital twins could be used in manufacturing, energy, transportation and construction. Large, complex items such as aircraft engines, trains, offshore platforms and turbines could be designed and tested digitally before being physically produced. These digital twins could also be used to help with maintenance operations. For example, technicians could use a digital twin to test that a proposed fix for a piece of equipment works before applying the fix the physical twin.
Digital twins and IoT
With the explosion of IoT sensors, digital-twin scenarios can include smaller and less complex objects, giving additional benefits to companies.
In a February 2018 article for Network World, Dave McCarthy cites “5 reasons digital twins matter to your IoT deployment," which include the ability to use digital twins to predict different outcomes based on variable data. This is similar to the “run the simulation” scenario often seen in science-fiction films, where a possible scenario is proven within the digital environment. With additional software and data analytics, digital twins can often optimize an IoT deployment for maximum efficiency, as well as help designers figure out where things should go or how they operate before they are physically deployed.
The more that a digital twin can duplicate the physical object, the more likely that efficiencies and other benefits can be found. In this earlier article, contributor Dean Hamilton says digital-twin technology will change the face of manufacturing. “The more highly instrumented a device is, the more accurately its digital twin will represent its actual historical performance, leading to better analysis and simulation of its future performance,” Hamilton writes.
Digital twin skills
Creating digital twins with this much data, however, can require additional skill sets such as expertise in machine learning, artificial intelligence, predictive analytics and other data-science capabilities. Gartner warns that digital twins aren’t always called for, and can unnecessarily increase complexity. “[Digital twins] could be technology overkill for a particular business problem. There are also concerns about cost, security, privacy, and integration.”
Major companies such as Microsoft, Oracle and GE have digital twin technology offerings for companies looking to create and deploy digital twins within their own IoT environments.
Digital twin vs. predictive twin
In November, Network World contributor Deepak Puri outlined an example of an Oracle digital-twin tool that provides users with two options – a digital twin and a predictive twin.
The digital twin “can include a description of the devices, a 3D rendering and details on all the sensors in the device. It continuously generates sensor readings that simulate real life options.”
The predictive twin “models the future state and behavior of the device,” Puri writes. “This is based on historical data from other devices, which can simulate breakdowns and other situations that need attention.”
Microsoft is taking the digital twin concept and applying it to processes in addition to physical products. In this whitepaper, Microsoft proposes the idea of the digital process twin:
“The Process Digital Twin is the next level of digital transformation, compounding Product Digital Twin benefits throughout the factory and supply chain,” Microsoft states. The associated whitepaper highlights some advanced manufacturing scenarios that product digital twins don’t support, but that process digital twins would.
Digital twin history
Several articles (from sources such as GE, Gartner, and Innovation Enterprise) have cited NASA as an example of using the basic twinning idea during the space program of the 1960s – only it would create duplicate physical systems on the ground that matched the systems in the spaceships. Eventually those duplicate physical systems became computer simulations.
In 2016, the term really took off after Gartner named digital twins as one of its “Top 10 Strategic Technology Trends for 2017” saying that within three to five years, “billions of things will be represented by digital twins, a dynamic software model of a physical thing or system.” The rise of IoT-styled sensors that could provide data within the physical world of how that object operated and reacted to environmental factors is also driving digital-twin usage. The Gartner report stated that “a digital twin can be used to analyze and simulate real world conditions, respond to changes, improve operations and add value.”
A year later, Gartner once again named digital twins as a top trend for 2018, saying that “with an estimated 21 billion connected sensors and endpoints by 2020, digital twins will exist for billions of things in the near future.”