The Four Types of Digital Twins

Digital twins are no longer a niche concept but rather becoming mainstream with the help of Industrial Internet of Things dashboards and near real-time reporting. Driving factors include the pandemic response alongside new requirements like increased factory and shop floor automation, greater data transparency, and better worker augmentation. By 2026, more than US$17.8 billion (38% compound annual growth rate) will be spent on digital twins, supporting more than 10 million frontline workers, up from 1.3 million today.

There are multiple types of digital twins, ranging from those that encompass basic metadata about a respective entity/asset with a means to monitor it in real-time, to advanced high fidelity analytic models that enable prediction and simulation for comparison of expected versus real behavior. To better understand some of the nuance, digital twins can be grouped into four main classes:

• Basic twin: A basic digital twin requires a near real-time representation of a physical asset. It stores data and insights from monitoring to allow engineers and other employees or partners to extract insights. This is more than a simple system for alerting because of the data storage capabilities coupled with near real-time asset visibility and, importantly, context to derive insights from parts or processes that cannot be easily observed.
• Intelligent twin: Intelligent twins take the next step beyond basic storage and visibility to perform and provide actionable analytics. This level of twin includes pattern recognition and can enable higher-value use cases, such as twin-based predictive and prescriptive maintenance. The use of machine learning on collected data and more advanced analytics on single or fleets of endpoints are common traits of this level of maturity.
• Simulation twin: Simulation twins are characterized by physical or physics-based simulations that take collections of sensor data and run simulations on monitored components to determine the real-world impact on factors like durability and wear, process and performance, or overall design.
• Executable twin: The general premise and key attribute of this most advanced stage of digital twin is that twin models include environmental context that spans not only the particular asset twin, but also the environment in which it operates. Ultimately, this means instituting closed-loop quality, and likely 3D and spatial mapping, which can be achieved with fixed or mobile sensor data (e.g., video). The ability to confidently perform or implement commands on a physical model because of digital simulation is another notable characteristic.

Specific nodes in any class of digital twin are often referred to by object type—for example, an asset twin, computer numerical control (CNC) twin, or automated guided vehicles (AGV) twin.

Digital twins are not a single technology, but a composition of solutions aimed at bridging the physical and digital worlds, from design and simulation through manufacturing, assembly, and after sales service and support. Consequently, manufacturers need a range of capabilities to successfully deploy digital twins, including computer-aided design (CAD) modelling, connectivity, cloud computing, IIoT software platforms, remote monitoring, hardware for shop-floor workers (tablets, AR glasses), physics-based simulation, machine learning, and systems integration.

Many vendors provide a few of these core products and services very well, but few provide a customizable end-to-end solution. Some companies that provide the most complete solutions include Dassault Systèmes, Hitachi Vantara, PTC, and Siemens. Other companies with a prominent position are Ansys, Autodesk, GE Digital, and Microsoft, due to their work on standards through organizations like the Digital Twin Consortium (DTC).

At a high level, there are five main ways to play in this market:

1. Selling digitalization as an approach for business and positioning digital twins as the solution.
2. Digital twins for a product, system, or solution portfolio.
3. Infrastructure for digital twins (IoT hardware, software, platform, industrial communication).
4. Software to design, host, operate, and maintain digital twins.
5. Consulting and support for digital twins.

The biggest changes in the next 12 to 24 months will be seen in the development of data and model-based standards; integration of real-time data from new sources such as virtual sensors, video cameras, and front-line workers; emergence of new business models including power-by-the-hour; and the use of simulation alongside metrics like overall equipment effectiveness (OEE). Digital twin solutions providers must determine which of the above subsegments to address and can learn more in the recent ABI Research report, Industrial Digital Twins: What’s New and What’s Next.