To put it simply, a digital twin is a virtual model of a physical object, space or process. A copy made from code and driven by data.

Digital twinning is one of the key ways companies are working in the metaverse to create products, train employees and bring teams together. 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 $110.1 billion by 2028.

Digital twins are more than a 3D visualization of something in the real world. They use data to simulate the performance of objects, spaces and systems, to create virtual twins that look and behave like the real thing.

By using sensors on the physical objects and spaces they replicate, digital twins receive real-time data to reflect what’s actually happening to their physical counterparts. Through simulations and machine learning, the models evolve to work more efficiently.

Digital twin technology is particularly useful when managing large, complex projects. The virtual replicas are commonly found in:

• Automotive: Digital twins help manufacturers to develop and test vehicles, and even predict maintenance tasks to save costs. The technology generated $2.17 billion in 2022 and is expected to generate $34.58 billion by 2032.

• Construction: Contractors use digital twins to plan buildings and recreate workflows to identify hazards and minimize safety risks. The sector’s digital twin market is expected to hit $48.2 billion by 2026.

• Energy: Usage data from energy meters is fed to digital twins to help providers better manage demand. The companies can use the technology to predict usage surges and prepare properly. The industry’s digital twin market is expected to grow at 32.5% compound annual growth rate from 2022 to 2027.

• Healthcare: A wide range of digital twins are used in the healthcare sector, from replicas of patients and organs, to hospitals and the equipment inside, even drugs for testing. The technology generated $1.6 billion in 2023, a figure that’s expected to rise to $21.1 billion by 2028.

• Manufacturing: According to the Manufacturing Leadership Council, 58% of manufacturers either already have extensive digital twins or will within two years. Because the industry is filled with early adopters it’s likely to achieve a relatively lower compound annual growth rate of 16.5% from 2021 to 2030.

Digital twins generally fall into one of four types:

Component digital twins: The smallest possible digital twin. Component twins replicate a single part of a product or asset and are the starting point for bringing real world objects into the digital world. Sometimes the twins are broken down into smaller ‘part twins’.

Product digital twins: Also known as an asset twin, product twins bring together two or more component twins to help understand how they interact with each other and produce new insights.

System digital twins: What you get when you combine two or more product twins. By studying the relationship between products or assets, you can begin to understand whole systems of work and make more informed optimizations.

Process digital twins: 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 it manually, either at the start of the process or by adding them later on.

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-optimize’ to help their physical counterparts work better. The matured digital twin then generates new data to help businesses monitor performance over a longer period of time.

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 conveyors set to a fixed speed. Both of which could make a simulation inaccurate compared to real-world performance.

Digital twins, on the other hand, use real-time data to replicate what’s actually happening to a physical object or space. This gives businesses a comprehensive idea about how their systems are working – ultimately, allowing them to make better informed decisions.

Here are some of the reasons organizations might want to consider investing in digital twin technology:

• Greater efficiency: Digital twins simplify complex information and processes to help decision makers identify bottlenecks and develop solutions. The more data sources businesses have in their digital twin, the more processes can be fine tuned.

• Better team collaboration: Workplace systems are often managed by siloed teams. By mapping out their role in entire processes, businesses can identify areas to bring teams together. Whether it’s preventing the doubling up of work, or supplementing one system with another, digital twins help operations teams to make the most of their resources.

• Risk-free training: Digital twinning is being used in several industries to train workers using simulated scenarios. Employees can get hands-on experience of different situations and different machinery without any risk to their safety. If the machinery or process changes, the digital twin can be modified to reflect those changes.

• Improved processes: Real-time data allows businesses to optimize the processes behind their products and services. A wind farm in California recently reported that their digital twin allowed engineers to mix and match configurations and increase energy production by up to 20%. That adds up to $100 million in extra revenue over the wind turbine’s life.

• Better products: Companies that already use digital twins report an improvement in product quality of up to 25%. Because they’re powered by data, the virtual replicas allow engineers and designers to make better informed decisions during the design stage.

• Greater customer satisfaction: Through digital twins, businesses can carry out predictive maintenance to minimize disruptions to their products and services. They can also collect real-time data on customer behavior to track product use and offer more efficient customer service and repairs.

• Faster to market: According to McKinsey, digital twins are accelerating the time it takes to get a product to market by 50%. Speedy production times are made possible as businesses rely less on physical prototypes and the time and resources needed to make them.

• Earlier risk assessment: Digital twins allow businesses to test for potential risks during the earlier phases of a process. Whether it’s identifying gaps in a supply chain before a demanding period or unearthing weaknesses in a building’s design before shovels are in the ground.

• Cost saving: Despite the initial outlay, digital twins have proven to save businesses money in a short period of time. Back in 2018, General Electric shared that the technology had saved their customers $1.05B. Who knows what savings they’ve achieved since?

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