Registered users can unlock up to five pieces of premium content each month.
Up the Adoption Curve |
NEWS |
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.
The Four Classes of Digital Twins |
IMPACT |
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:
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.
Points of Entry |
RECOMMENDATIONS |
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:
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.