“Digital twin technology is a bridge between the digital world and the physical world.” – Yuval Lirov, vice president of marketing, Ansys

The concept of “digital twin” is not new; it has been around for some time as an idea to describe a digital replica of a living or non-living physical entity.

As described by Gartner, Inc., “A digital twin is a dynamic software model of a physical thing or system that relies on sensor data to understand its state and respond to changes, feeding back into how the physical world develops.”

The idea behind this concept is to have a mirror image of the real-world entity in the digital space that can be used for various purposes including monitoring, controlling, and prediction among others. The idea was first coined by Dr. Michael Grieves at the University of Michigan. In 2002 GE adopted the term “digital twin” to describe the virtual representation of industrial assets such as jet engines and wind turbines. And in recent years, manufacturing companies are using digital twin for simulation, visualization, and analysis purposes.

The “digital twin” is a virtual model of a process, product or service. It relies on sensor data to understand how the physical parts of a system are performing in order to make predictions about what will happen in the future.

A digital twin technology is running at the shop floor level, which helps to make it possible for factory workers to actually see what’s going on and to interact with it. That takes the IT department out of it.

The digital twin holds enormous potential, but we are still at the beginning of this journey.

Digital Twin is a digital replica of a living or non-living physical entity. Digital Twin uses sensor-based data and IoT to understand the state of the physical entity and make predictions on its future state. It provides an overview of how the product works, what it has gone through, and how it is likely to perform in the future.

Digital Twins are not just limited to sophisticated pieces of machinery, such as aircraft engines. They can be applied to anything, ranging from factories to buildings, products, and people.

As of now, most industries use Digital Twins for:

Monitoring: The primary purpose of Digital Twins is to monitor how a product is performing in real time. This gives businesses a chance to take corrective actions before their product fails. For example, manufacturers can use Digital Twins to track the performance of various components in their equipment in real time and find out if they need maintenance or replacement. If a product is going off-track, they can take corrective actions quickly before it causes a problem.

Digital twin is a tool for understanding complex systems. It’s a digital replica of physical assets, processes and systems that can be used for various purposes. The digital twin is also defined as the virtual representation of a real-world entity or system.

The digital twin has been described as the “single source of truth” that brings together data from physical sensors with other information sources, such as computer-aided design (CAD) files, operational data and engineering calculations.

Digital twins are not new. NASA has been using them to simulate space shuttle missions since the early 2000s. But it was only in 2016 when Gartner declared digital twins to be the next big thing in their hype cycle report. Since then digital twins have been rapidly adopted across many industries, including manufacturing, healthcare, insurance, retail and others.

The development of the technology has been largely driven by IoT and related advancements in sensors and actuators that allow us to gather rich data about our physical environments and interact with them in highly sophisticated ways.

The concept of digital twin technology has been around for a while, but with the rapid growth of machine learning and the Internet of Things (IoT), digital twins are becoming increasingly sophisticated. A digital twin is a virtual representation of a physical object or system. The idea is to create a virtual copy of something in the physical world so that it can be used to model, simulate and analyze data from the real world.

The relationship between the digital twin and its physical counterpart is created through a combination of sensors and IoT technologies, which gather data on the characteristics, behaviors, and usage patterns of the physical object. This data can then be fed into the virtual model to create an exact replica that looks and acts like its real-world counterpart.

By combining virtual models with real-world data, digital twins help businesses anticipate problems and optimize outcomes in a wide range of sectors, including manufacturing, healthcare, supply chain management and more.

The concept of a digital twin – a virtual version of an asset or process that is used for testing and simulation – is gaining traction in the manufacturing space. It’s not a new idea, but with the rise of Industrie 4.0, the practice of simulating processes to understand performance and improve outcomes is becoming more mainstream.

The concept of ‘digital twins’ was first introduced by NASA in 2002, as they created digital models to replicate the performance of physical assets during the design and test process. The idea was then adopted by General Electric (GE) in 2003. GE used digital twins as part of their ‘Brilliant Factory’ initiative in its aviation arm, with the goal of creating a digital replica of all its assets, allowing them to continuously monitor and understand them.

The first iterations of digital twins were based on designing simulations for testing purposes; however, today the term has evolved to refer to the broader practice where information about an object or system is collected from sensors and other sources, then continually updated within a model so it can be analysed for optimisation purposes. While many companies are still using digital twins for testing purposes, most are now also using them to make better decisions based on real-time data collection.

The physical world is currently evolving rapidly to include more digital components. While the first car was only built in 1886 and the first airplane in 1903, it took us less than 20 years to connect them to the Internet. The same goes for other smart devices, such as thermostats, refrigerators or security systems. Cars, airplanes and many other things are becoming digital twins.

A digital twin is a virtual representation of a real thing or system. It provides an interface between virtual and physical worlds allowing us to understand the real world better and make better decisions. Digital twins can be used for both physical objects (a piece of equipment, a building or a vehicle) or organizational structures (a business unit, an entire company or even a city).

Digital twins help predict failure and optimize performance. They are already being used in factories to optimize processes and prevent machines from breaking down; in cities to reduce traffic jams and improve road safety. A digital twin is also a powerful tool that can help us understand how we impact the environment.