The Power and Pitfalls of Digital Twins in Manufacturing
A decade from now, there will likely be a digital twin for consumer devices, industrial machines, the electrical grid, oil and gas infrastructure and even entire factories. But while digital twins are hot now, it is not always clear precisely what is meant by the term. While it is clear that they are data-based representations of physical objects, the resolution of current digital twin projects varies considerably. “There are digital twins, digital almost-twins, digital ‘look-alikes’ and digital ‘wish-we-were-related’-type degrees of twinness,” said Dan Miklovic, a research fellow at LNS Research. “We see lots of companies using very small ‘DNA strands’ of digital twinness today with the technology becoming far more important over the next few years.”
In early 2018, the digital twin is still an emerging technology, although its roots stretch back to 2003, when Michael Grieves, Ph.D. introduced the concept in an executive course on Product Lifecycle Management at the University of Michigan. Last year, Gartner pegged the technology halfway up the initial ascent of the “Innovation Trigger” stage of its hype cycle, which looks rather like a roller-coaster lift hill whose peak is dubbed the “Peak of Inflated Expectations
Because digital twins are a nascent technology, many of their promises remain fuzzy and intertwined with the marketing pitch for technologies such as IIoT, analytics, machine learning, artificial intelligence and cognitive computing. The basic idea is that the digital twin can bolster productivity and optimize physical assets, processes or systems. “The digital twin is the best way today for industrial companies to simulate and experiment without touching a production system. You can create models using precise real-time data. This is something extremely new,” said Poniewierski.
In addition, digital twins can simultaneously spur collaboration among workers who, after using digital twins to spot a problem, can pool their resources to address it. Proposed benefits of digital twins in the industrial space include the following: helping manufacturers improve the quality of finished goods, bringing predictive maintenance capabilities to manufacturing equipment, assisting manufacturers in the transition from selling industrial outcomes rather than discrete products while helping industrial organizations optimize their machinery, products, production lines or entire facilities. It so happens that all of those benefits have been directly linked to IIoT itself.
One of the chief benefits of digital twin technology is its potential to drive experimentation. “A manufacturer could build a digital twin for, say, a predictive maintenance application. They can then train the real behavior of the machine for their operators, machines and suppliers, in essence building a real-time test bed for improving the quality of production,” Poniewierski said. “They might realize that experimentation on live digital twins will cost, for example, one dollar, whereas running that same experiment in a development environment will cost $100.”
According to Gartner, a digital twin has four essential features, all of which are closely related to the “thing” in an Internet of Things project. First, there is a model of the “thing” it represents. Second, there is data about that thing such as its identity, status and context. Third, there is a twin’s uniqueness. In other words, each digital twin corresponds to a unique object. Finally, the digital twin monitors the status of an IoT “thing.” “You can ask the twin for information about the thing,” explained Nick Jones, vice president, distinguished analyst at Gartner at the November 2017 Gartner Symposium in Barcelona. Digital twins can also optionally be equipped with analytics for applications like predictive maintenance. There is also the option for digital twins to control physical objects. Lastly, some digital twins are capable of simulating the “thing” they are meant to replicate.
To be clear, the idea of using sophisticated models of physical products is not new. Organizations such as NASA have been using advanced simulations for spacecraft for many years. What sets digital twins apart is their connection to connected sensor data streaming from IoT-enabled objects, enabling them to discover insights and trigger actions based on sensors and smart machines and, in factories, manufacturing execution systems, eroding the barriers between the physical and the digital worlds. While the concept of bringing those physical and virtual models together is not new, it has been incomplete, as Grieves observed.
Manufacturers can also deploy digital twins that put AI/machine learning capabilities at the edge of a network to detect anomalies and spot data patterns next to a sensitive piece of equipment on the shop floor.
Specific use cases for digital twin technology have also been proposed and in some instances realized. For instance, digital twins have the potential to help industrial companies reduce the expense of product development and remote maintenance tasks. In addition, companies relying heavily on destructive testing during product development could perform a significant portion of such virtually. For instance, an automotive company could use digital twin technology to reduce the need for crash testing prototype vehicles. Similarly, companies relying heavily on building elaborate models and prototypes could save money by using digital twins during product development. Furthermore, organizations operating valuable assets in remote locations could use a digital twin of the equipment to simulate service life, helping curb the need to send a technician to that site to check its status. Digital twin technologies also could help manufacturers improve the level of sophistication and usability of future project generations while also helping speed time to market.