The Power and Pitfalls of Digital Twins in Manufacturing

Digital twins are also at the heart of a whole suite of proposed applications in which technicians donning AR visors can gain a virtual view of what is happening inside a piece of production equipment. Digital twin technology could also help guide an inexperienced technician equipped with an AR headset to spot defects and learn to perform routine repairs thanks to step-by-step instructions that break the tasks into discrete steps that show, for instance, which screws or bolts to remove, the proper orientation of a new component and so forth.

While an array of benefits have been proposed for the technology, one of the impressive aspects of digital twin technology is its potential to scale. A first-generation digital twin could, for instance, help the owner of a wind farm optimize an individual wind turbine. A second-generation turbine could help optimize the entire wind farm, while subsequent generations would, thanks to increasing resolution, enable further gains while also allowing the turbine manufacturer to refine its latest product line using production data.

While it may seem that mainstream digital twin adoption is at least several years out, many organizations are already deploying them. While Gartner estimated last year that mainstream digital twin adoption was another five to 10 years out, 48 percent of enterprise companies implementing IoT technologies are “already using or planning to use digital twins in the next 12 months,” Jones explained.

In 2018, LNS Research expects digital twin initiatives to focus on replicating industrial processes rather than just the physics-based aspects.

Digital twins could also be a boon to artificial intelligence research. One of the reasons AI research stalled in decades past, according to AI pioneer Marvin Minsky, is because of computers’ limited sensory input and ability to interact with the physical world. Because digital twins align machine learning and AI with sensor data, that barrier could be steadily overcome in the next several years as digital twins become mainstream. Digital twins can help create a sort of digital feedback loop between connected products and machines and their environment and user base.

Ultimately, digital twins promise to be a powerful technology that, when used in conjunction with IIoT, can help transform the industrial landscape. Yet both IIoT and especially digital twin technologies remain at an early phase of adoption, and, for many organizations will require considerable customization work to create bespoke digital twins for their unique operational needs. That, in turn, will likely require the expertise of experts in fields like machine learning and artificial intelligence, who command salaries in the six- and even seven-figure range. Even recently graduated machine learning doctorates can command starting salaries in the $300,000 range. On the flipside, a growing number of vendors are beginning to launch digital-twin related services or products, which could contain costs of the technology to some extent, and their potential to curb physical testing while driving efficiency gains could quickly offset the initial capital outlay needed to deploy the technology.

High-resolution digital twins could also become a valuable asset to whoever possesses them — whether it is the industrial company or a cybercriminal who gains access to the technology. “Now, with IIoT and digital twins, you integrate both knowledge and experience,” Poniewierski said. “It is a killer mix.”