Beyond manufacturing, digital twin technology has merged the realms of the Internet of Things, artificial intelligence, and data analytics.
As more complicated “things” connect and provide data, having a digital equivalent allows data scientists and other IT workers to optimize deployments for maximum efficiency and build various what-if scenarios.
What is Digital Twin?
A digital twin technology is a computer-generated digital representation of a physical object or system. The technology underlying digital twins has developed to incorporate buildings, industries, and even cities, with some arguing that even individuals and processes can have digital twins, further broadening the idea.
The concept of a digital twin originated at NASA, where full-scale models of early space capsules were used on the ground to reflect and diagnose faults in orbit, eventually giving way to totally digital simulations.
But the term truly gained off after Gartner named digital twins one of its top ten key technology trends for 2017, predicting that “billions of objects would be represented by digital twins, a dynamic software representation of a physical thing or system,” within three to five years “. A year later, Gartner named digital twins a top trend once more, claiming that “with an anticipated 21 billion linked sensors and endpoints by 2020, digital twins will exist for billions of things in the near future.””
In essence, a digital twin is a computer program that accepts real-world data about a physical thing or system as inputs and delivers predictions or simulations of how those inputs will impact that physical object or system as outputs.
How Does Digital Twin Work?
A digital twin is created by IT consulting companies, who are often experts in data science or applied mathematics. These programmers investigate the physics behind the physical object or system being emulated and use that information to create a mathematical model that simulates the real-world original in digital space.
The twin is designed to receive input from sensors collecting data from a real-world counterpart. This enables the virtual twin to imitate the physical thing in real time, providing insights into performance and potential faults. The twin could also be constructed based on a physical counterpart’s prototype, in which case the twin can provide input while the product is refined; a twin could even act as a prototype before any physical version is built.
Use Cases for Digital Twins
The digital-twin examples we mentioned before – the automobile and the cargo vessel – give an idea of potential use cases. Aircraft engines, railways, offshore oil platforms, and turbines can all be developed and tested digitally before being manufactured physically. These digital twins could also aid in maintenance operations. Technicians, for example, could utilize a digital twin to ensure that a planned fix for a piece of equipment works before implementing the fix.
Digital-twin business applications can be found in a variety of industries:
Manufacturing is perhaps the most advanced in terms of digital twin adoption, with companies already adopting digital twins to model their operations, as demonstrated by this Deloitte case study.
Automotive digital twins are achievable since automobiles already have telemetry sensors, but developing the technology will become increasingly critical as more self-driving vehicles reach the road.
People’s digital twins could be created in the healthcare industry. Band-aid-sized sensors might transmit health data to a digital twin, which could be used to monitor and anticipate a patient’s well-being.
What other sorts of digital twins are there?
IBM provides a classification scheme based on the intricacy of what is being twinned rather than specific sectors. This is a great method to think about the needs in certain use scenarios and provides an overview of what digital twins may do:
Component or part twins are the smallest example of a working component.
Asset twins replicate two or more components functioning together and allow you to investigate their interactions.
System or unit twins allow you to see how numerous system assets interact with one another, such as replicating a full production line.
Process twins provide an incredibly high-level picture of how systems interact, allowing you to imagine how an entire factory might operate.
It’s important to note that adding more components to the mix increases complexity. Mixing and combining components from different manufacturers, in particular, can be challenging because you’d need everyone’s intellectual property to play nice in the environment of your digital twin.