Let's start with the digital twin definition. Simply put, a digital twin is a virtual representation of a physical object, space, service or system. It's a copy made from code and driven by data from real-world sources, so it updates and changes as the original version would.
Digital twin technology can accelerate innovation, improve efficiency and support smarter decision making. That's invaluable if you want to grow your business, save on costs and even meet sustainability goals.
Here, we'll look at the definition in more detail, including the different types of digital twins, the industries using them and why the digital twin market is expected to be worth USD 110.1 billion by 2028.
Drawing on the powers of virtual reality (VR), mixed reality (MR) and other emerging technologies, digital twins are helping companies create products, rapidly scale capacity, train employees and bring teams together. They provide a virtual glimpse into the future, revealing outcomes before time, money and resources have gone into making them a physical reality.
Digital twins are more than a 3D visualisation of something in the real world. They rely on sensors placed in key areas of functionality of the physical objects and spaces they replicate. These provide real-time data readings to reflect what's happening in their physical counterparts.
With the data it receives, digital twin technology can create simulations, analyse performance and propose enhancements.
The technology working behind the scenes is extensive. Digital twins rely on:
Digital twin software has a wide range of use cases in many different industries:
Digital twin technology generally falls into one of four types:
The smallest possible digital twin. Component twins replicate a single part of a product or asset. Sometimes they're broken down into even smaller 'part twins'. They provide insight into how parts interact with the whole. Combined component digital twin studies give you a full analysis of predicted performance and where the weak links might be.
Also known as an asset twin, product twins bring together two or more component twins to help understand how they interact with each other,producing new insights.
The result of combining two or more product twins. By studying the relationship between products or assets, you can begin to understand whole systems of work – for example, an aircraft fuel system – and make more informed optimisations.
Digital twins at the highest level. Process twins bring together two or more systems to recreate entire warehouses or manufacturing lines. The large-scale replicas influence decisions at the highest level for maximum business impact.
At first glance, digital twins and simulations appear very similar. The differences lie in data.
A simulation is a 2D or 3D virtual model made using computer-aided design (CAD). Designers replicate a specific part or parts of an object or process to test their performance. If businesses want to make changes to a simulation, they need to do so manually, either at the start of the process or by adding them later.
Digital twins, on the other hand, evolve over time. They replicate what's actually happening to their physical counterparts using real-time data. Through machine learning, the models 'self-optimise' to help their physical counterparts work better. The matured digital twin then generates new data to help businesses monitor performance over a longer period.
Simulations are as accurate as they're designed to be. Perhaps they replicate a chemical process while using a set temperature, or a production line with conveyor belts set to a fixed speed. Both of these could make a simulation inaccurate compared to real-world performance.
Digital twins, on the other hand, use real-time data to replicate what's happening to a physical object or space. This gives businesses a comprehensive idea of how their systems are working – ultimately, allowing them to make better-informed decisions.
Mixed reality helps businesses to experience digital twins in the third dimension, but virtual twins and digital twins aren't necessarily the same thing.
For a twin to be virtual, it must be immersive: an environment that can be experienced in virtual reality; whether it's a static or an evolving model doesn't affect the definition.
For a twin to be digital, the model must evolve with real-time data. This could be presented both virtually or through conventional 2D screens. Digital twin technology doesn't tend to focus on immersing the user. It's data-driven, enabling organisations to engage with digital replicas to analyse, predict and gain insights.
Here are some of the reasons why organisations might want to consider investing in digital twin technology:
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