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As the digital era flourished, it seemed almost inevitable that some of what existed in the physical world would take on life in the digital realm, and that this replication would eventually have business value.
The consumer universe is increasingly digitized -- e-books and digital music are everywhere -- and 3D printing has made digitization a two-way street. Enter digital twin technology.
Several definitions of digital twins
The face-value definition of the term digital twin is a digital instantiation of an actual physical asset. By this definition, a 3D printing process qualifies: A digital instantiation of an object is used to actually create that object. Using older terminology, a digital twin might be thought of as a simulation of something real.
In a GE Global Research Center presentation, Colin Parris, vice president of software research at GE, defines a digital twin as "a living model of something that delivers a business outcome," operationalizing the concept in practice.
Gartner defines a digital twin as "a dynamic software model of a physical thing or system" -- the key words being dynamic and system. Digital twin technology is becoming so important that it landed a spot on "Gartner's top 10 Strategic Technology Trends for 2017."
The origin of digital twin technology
Digital twins aren't new -- they've been around for more than 15 years -- and they aren't specifically an innovation of business.
In fact, the impetus for creating digital representations of physical objects was NASA's need to model extraterrestrial environments to build hardware. Since a robot on Mars can't be monitored in real time, it became important for NASA to model the environment as a digital simulation while designing and building the robot to ensure they got it right the first time.
This bridging of the physical and digital realms makes possible the modeling of the behavior and lifecycles of objects in physical space, and in the era of the internet of things (IoT) and the cloud, to constantly update those digital models with real-world data from the physical world. The physical object (or process) and its digital twin essentially make each other better over time. And the applications of that concept are numerous.
A business imperative for digital twins
In a Forbes article, Thomas Kaiser, SAP's senior vice president of IoT, said that the digital twin is a critical foundation of connected products and services.
"Companies that fail to respond will be left behind. Those that embrace digital twins have the opportunity to better understand customer needs, continuously improve their products and services, and even identify new business models that give them competitive advantage," he told the writer.
Indeed, IoT has brought digital twin technology to the forefront. The ability of real-world objects to gather information from themselves and their environments and hand it off via the internet in real time elevates the digital twin as an essential component in predictive analytics for any products and processes using IoT technology. In other words, the internet of things and digital twins were meant for each other.
The myriad uses for digital twins
As IoT spreads within and across industries, digital twins will likewise spread -- and their use will proliferate as IoT's uses have. Here's just a sampling.
Data collection and analytics across industries. The state of the art in product and service evolution prior to digital twins was reactive, not proactive.
"When an asset failed or [was] about to fail, most industrial companies collect data without really knowing what data to gather," Parris said in his presentation. "This is called unstructured data. The company will then reconfigure the data collection and analysis model to make sense of the data [and] refine the process before having to change it again when more problems creep up months down the road. It is a static read-and-react model."
Digital twins eliminate this static model, replacing it with a dynamic model -- a living model, according to Parris. The guesswork is taken out of data collection, and what is gathered is utilized for prevention rather than post-failure diagnostics.
Prototyping. Digital twin technology enables virtual prototyping -- the digital creation of a product or service before it is physically instantiated -- as well as the simulation of its intended environment and operation. In addition to being faster and cheaper, this style of prototyping is ultimately more precise, resulting in a higher quality product or service.
In field service. Combining IoT and digital twins can yield predictive maintenance, which is the ability to know that a component is going to fail before it actually does. Field service then becomes a matter of showing up to replace a part before the failure, rather than a downtime trip to not only replace the failed part, but to fix whatever else was broken as a result of the failure.
In industrial IoT. Industrial IoT is opening up new possibilities in complexity laden products and processes.
Airplane engines, factory robots and other complicated machines are notoriously difficult to model because they combine individual component behaviors and processes in ways that cause them to interact unexpectedly. It is difficult to anticipate those interactions in the design phase.
By using industrial IoT (wherein the airplane engine or factory robot contains many sensors in many places) in combination with digital twins, interactions between components and processes become far easier to pinpoint – and, subsequently, plan for. And that data not only makes for superior design, but also feeds into the predictive maintenance process mentioned above.
Digital twins are certainly here to stay. The Gartner technology trends report predicts that they will number in the billions within three to five years.
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