You’ve Heard of IoT and AI, but What is Digital Twin Technology?

You've Heard of IoT and AI

Topics like artificial intelligence (AI), the internet of things (IoT), and machine learning are getting lots of hype, but digital twin technology might just be the real game-changer. Digital twin software uses aspects of all the trending tech mentioned (AI, IoT, ML) in a unique way that's changing the way businesses optimize production and investment, and the big boys are already heavily invested.

A digital twin is a highly advanced simulation that's used in computer-aided engineering (CAE). It's a digital duplicate that represents a physical object or process, but it is not intended to replace a physical object; it is merely to inform its optimization. Other terms used to refer to digital twin technology include virtual prototyping, hybrid twin technology, and digital asset management, but digital twin is quickly winning out as the most popular name.

Both NASA and the United States Air Force are planning on using digital twin technology to create future generations of lightweight vehicles that are sturdy and able to haul more than their current counterparts. Goldman Sachs recently examined digital twin technology in their series "The Outsiders," which seeks to identify "emerging ecosystems on the edge of today's investable universe." IBM is already on the digital twin train, combining AR with digital twin optimization and visualization. And SAP recently launched SAP Predictive Engineering Insights, a software product that enables organizations to use digital twin technology for digital asset management.

Independent research companies are steadfast in their belief in digital twins, too. Gartner predicts that by 2021, "50 percent of large industrial companies will use digital twins, resulting in those organizations gaining a 10 percent improvement in effectiveness." So, while digital twin technology isn't getting the same level of media attention as 3D printers or voice assistants, there is little doubt that it will be a game-changer and become an integral part of how all businesses of the future (not just manufacturing) will optimize processes, products, and communication.

Some industry experts argue that a true digital twin must have a physical counterpart in the real world. Indeed, for most of the (admittedly short) history of digital twin technology, this has been the case. Such twins (that have physical counterparts) are sometimes referred to as data-driven digital twins, because they rely on connected devices that make up IoT technology (often in the form of sensors and integration with other tracking systems and databases) to collect past and current data, test new configurations and processes, and inform future decisions.

There's also a crop of digital twin technology that's relatively more affordable, which uses digital simulation in a predictive way without a physical counterpart. This secondary type of digital twin is sometimes called a model-driven digital twin, but it can also go by the standard digital twin moniker.

The reason the second type of digital twin is less expensive is twofold: First, there are no IoT sensors or setup, since all the testing and tracking is happening digitally. Second, businesses can try out different processes and view outcomes prior to investing in actual infrastructure, technology or resources.

Imagine a factory that produces automobile tires. Within that factory, there are several pieces of highly expensive machinery, each one responsible for a different task related to making tires. Now imagine you own that factory, and you want to figure out how to optimize the production process.

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