Does your factory have a digital twin?

The market for digital twin software, which repliclates all aspects of an entity digitally, grew 71% between 2020 and 2022, according to a recently published report.

The Digital Twin Market Report 2023–2027 highlights the six main digital twin applications today: system prediction, system simulation, asset interoperability, maintenance, system visualisation, and product simulation.

The study, conducted by IoT Analytics, a global provider of market insights and strategic business intelligence for the Internet of Things (IoT), AI, Cloud, Edge, and Industry 4.0, analysed 100 digital twin projects.

It reveals that 29% of manufacturing companies globally have fully implemented or are implementing a digital twin strategy for a portion of their operational assets, and a further 63% are currently developing or have already developed a digital twin strategy.

“The digital twin market is yet to reach its full potential,” says Knud Lasse Lueth, CEO of IoT Analytics. “We expect future growth to be more aggressively driven by manufacturers in East Asia and Pacific region.”

The roots of digital twins go back to NASA’s Apollo program in 1970. However, the concept of creating digital replicas of physical assets and visualising, simulating or predicting in a virtual world is suitable for companies that are trying to make Industry 4.0 a reality or are aiming toward future industrial metaverse projects.

While the definition of a digital twin may be straightforward, its applications are numerous, says IoT Analytics.

“In 2020, we published our first market research on the topic and showcased that there may, in fact, be 200 or more different types of digital twins.

“The feedback we received … was that classification helps to ensure apple-to-apple digital twin comparisons, but questions remain about the hotspots of activity. Therefore, as part of our new 233-page Digital Twin Market Report 2023-2027, we classified 100 real digital twin projects along the three dimensions and found six main areas of activity. These six digital twin application hotspots cover two thirds of all digital twin projects we analysed.

Classifying Digital Twins
IoT Analytics defines a digital twin as a virtual model replicating the behavior of an existing or a potential real-world asset, system, or multiple systems.

Digital twin classification
There are three main dimensions describing the concept of digital twins. Each axis of the cuboid represents one dimension of the digital twin:

• Lifycycle phase: The X-axis represents the six life cycle phases a digital twin is used for, from design to decommissioning.
• Hierarchical levels: The Y-axis represents the five hierarchical levels a digital twin represents, from information to multi-system.
• Use/purpose of implementation: The Z-axis represents the seven most common uses for digital twins, such as simulation and prediction.

There are 210 potential different digital twin combinations (5 x 6 x 7 = 210), although our research indicates that many digital twin initiatives cater to more than one combination.

The six most common Digital Twin applications
As part of the research, IoT Analytics looked at 100 digital twin case studies and classified each project into the digital twin cuboid. The result was that six clusters of digital twin activity stand out. We call them digital twin applications:

Digital twin application Description % of projects

1. Twins for system prediction A digital twin geared toward predicting complex systems 30%
2. Twins for system simulation A digital twin geared toward simulating complex system behavior 28%
3. Twins for asset interoperability A digital twin geared toward common data formats and streamlined data extraction in complex systems 24%
4. Twins for maintenance A digital twin geared toward assisting with maintenance-related use cases 21%
5. Twins for system visualisation A digital twin geared toward visualizing a complex system (e.g., in 3D) 20%
6. Twins for product simulation A digital twin geared toward simulating the behavior of (future) products (mostly during the design phase) 9%

For more information, visit www.iot-analytics.com/research-blog