https://www.techtarget.com/searcherp/definition/digital-twin
A digital twin is a virtual representation of a real-world entity or process. It's composed of the following three elements:
A digital twin functions as a proxy for the physical entity it represents.
While many digital twins have a two-dimensional or 3D computer-aided design (CAD) image associated with them, visual representation isn't a prerequisite. For example, the digital representation, or digital model, could consist of a database, a set of equations or a spreadsheet.
The Digital Twin Consortium, an industry association working to build the market and recommend standards, adds an essential phrase to the basic definition of a digital twin: "synchronized at a specified frequency and fidelity."
This definition refers to the following three aspects of digital twin technology:
Digital twins are created using the same CAD and modeling software that designers and engineers use in the early stages of product development. The difference with a digital twin is that the model is retained and updated for later stages of the product's lifecycle, such as inspection and maintenance, and is often managed within product lifecycle management (PLM) software.
The physical connection points between the physical entity and its digital twin are often internet of things (IoT) sensors.
Finally, analytics, often bolstered by AI and machine learning (ML), is essential in processing and analyzing digital twin data.
Digital twins demand herculean efforts to capture and digitally mirror artifacts in the real world. This process is called reality capture. It is usually done with a laser scanner that directs a laser beam at a physical object and the surrounding space to capture measurements. Laser scan files are then combined into a point cloud, a set of points in 3D space that outline the geometry of the scanned object. Color is often added, and the point cloud is imported into CAD software for further enhancement.
Reality capture technology has expanded beyond laser scanners to include smartphone software and drones, which should help expand its use in digital twin development. In addition, advances in AI promise innovation in how 3D representations are created and stored. For example, neural radiance fields, or NeRFs, use advanced ML to generate 3D representations from 2D images and compress them to a fraction of the size of traditional images. A related approach called Gaussian splatting promises higher-quality, more interactive images and could accelerate the building of the industrial metaverse in which digital twins play an essential role.
Digital twins are one of the building blocks of the metaverse, a broad concept that incorporates technologies such as virtual reality (VR) that enable immersive and highly interactive digital worlds. Proponents are working to re-create common real-world elements in the metaverse. For example, in an enterprise metaverse, digital twins of various aspects of an organization are integrated to simulate operations and improve decision-making.
Augmented reality, another foundational technology of the metaverse, can overlay a digital twin on the object it represents to provide field technicians with more detailed maintenance data. Digital twins could also offer some of the data for VR images.
The ability to process data in real time differentiates digital twins from similar technologies such as computer simulations. A computer simulation can simulate one process at a time. However, because a digital twin has real-time access to data that can flow bi-directionally between the twin's sensors and processor, it can simulate multiple processes simultaneously. This enables users to experiment with the digital twin in a closer approximation of its physical counterpart's actual environment and actions. The multi-process capability that real-time data provides lets users investigate the state of an object or process in a virtual environment, query live data, and observe the resulting actions of the virtual model in a realistic setting. Conversely, computer simulations lack strong network connections to the represented entity and a data model that organizes the information.
Marketing hype often confuses computer simulations with digital twins, but the differences are straightforward. Simulations are usually done in CAD software during the design phase of a product or process. Engineers create 2D or 3D models, introduce variables and observe how they affect outcomes.
In contrast, digital twins exist throughout the entire product lifecycle. Over time, they acquire substantial quantities of real-time data that allow them to evolve and represent the current state of their physical counterparts more accurately. The simulations done on digital twins are more active and involve many variables, while those done in CAD are static and typically use a small number of variables. CAD simulations are theoretical, while digital twin simulations are connected to the real world.
Several ways of categorizing digital twins exist, but the following four categories are the most common:
Some observers employ less abstract categories for digital twins and name them by the type of physical object they represent. Examples are infrastructure twins for highways and buildings, and network twins for processes such as supply chains that involve entities operating in a network.
To be useful, digital twins must work in various enterprise applications. While they usually start in CAD and PLM, some digital twins are also managed in enterprise resource planning (ERP) and material requirements planning (MRP) software.
ERP and MRP store the bill of materials (BOM), a comprehensive inventory of the materials and parts needed to make a product and typically a major contributor of digital twin data. They also contain product engineering structures that illustrate where each part or subassembly of a BOM fits within the product. ERP and MRP run many of the supply chain and production processes that go into manufacturing a product; along the way, they collect much of the data that goes into the digital twin.
Another common source of digital twin data is the manufacturing execution system (MES) that many companies use to monitor, control and optimize production systems on the factory floor. Digital system and process twins can significantly affect an MES.
Enterprise asset management software, which is increasingly the preferred system companies use to manage the purchasing, monitoring and maintenance of their most valuable equipment, must also be integrated with digital twins.
In addition, public cloud providers that offer digital twin services, such as Amazon Web Services and Microsoft Azure, integrate with enterprise applications. It's even possible for digital twins that reside in different levels of the hierarchy -- asset twins and process twins, for example -- to be spread across different cloud services.
Because they're virtual, digital twins can reduce the cost and risk of having to work on the physical things they represent. Additional benefits include the following:
Organizations looking to develop digital twins face some hurdles. The following examines seven of the most prominent digital twin challenges:
The initial deployments of digital twins were mainly directed at the design, production and maintenance of extremely high-value, physically large equipment, such as airplanes, buildings, bridges and power-generation plants. In these cases, mechanical failure can be life-threatening or cause financial losses that exceed the significant expense and effort of developing a digital twin.
The following industries are seeing the most activity in planning or deploying digital twins:
The industrial world is widely acknowledged as the pioneer in using digital twins and has seen the broadest deployment. For several years, manufacturers have been making digital twins of parts, products and systems and are beginning to deploy process twins that model production processes and sometimes entire factories.
Electric companies use digital twins to design, monitor and maintain power plants, electric grids, transmission and consumption. This technology could also help improve the efficiency of renewable energy systems, such as solar installations and wind farms, whose production is less predictable than fossil fuel-burning plants. Process digital twins could someday mirror entire electric grids.
Digital twins built on electronic health records, medical images, genome sequencing and other medical information could make it easier for providers to diagnose illnesses and recommend treatments by comparing a patient's digital twin to that of other patients with similar profiles. Medical testing could be done more efficiently by avoiding the risks of using actual patients. Researchers are already running simulations on anonymized digital twin data to identify the best therapy options.
Digital twins are being used to design large buildings and offshore oil rigs. Some users are expanding construction twins to encompass neighborhoods and cities, focusing on infrastructure. The U.K. even tried to develop a national digital twin but ended the five-year initiative in 2022. Digital twins also have a role in smart city initiatives, which aim to digitally connect infrastructure, often through IoT, and apply AI and analytics to the data, making transportation more efficient and conserving energy, among many goals.
Digital twins play their usual role in the vehicle product-design stage and later stages of the vehicle lifecycle, such as service. Automakers are also using digital twins to make assembly plants more efficient. Digital twins are expected to reduce massive recalls by allowing each vehicle's unique twin to be analyzed for defects. Digital twins of customers enable virtual test drives of vehicles.
Retailers use digital twins to model product placement, the customer's journey through a store and the effect of new store layouts. Some companies have started using the technology to build online twins of their stores to boost interest in their e-commerce sites. Digital twins are also helping to improve the realism of 3D product images.
McKinsey & Company advises organizations to get their data house in order before deploying digital twins. That means building and maintaining high-quality data infrastructure that provides reliable data.
According to McKinsey, the three main steps in developing and scaling digital twins are:
Organizations can build on the initial, reusable data set and digital twins to continue expanding use cases, with the twins evolving based on real-world feedback and acquiring more predictive capabilities. Connecting the twins to form process twins makes simulating the relationship between entities possible. For example, integrating the digital twins of customers with the twins of retail stores, e-commerce sites and supply chain infrastructure enables a company to analyze and simulate business strategies and eventually build an enterprise metaverse.
Many experts trace the beginning of the digital twin concept -- though not the name -- to 1969 when the National Aeronautics and Space Administration (NASA) augmented training simulators with telemetry data from the damaged Apollo 13 spacecraft to diagnose and solve problems. NASA consultant Michael Grieves, an influential academic in PLM, expounded upon the concept in 2002 and significantly expanded on it in subsequent years. In 2010, Grieves collaborator John Vickers, manager of advanced manufacturing at NASA, began applying the term digital twin to the concept.
In the ensuing two decades, digital twins have expanded to other industries besides manufacturing, becoming a standard feature of CAD and PLM software. They have risen to the top of vendor and market-research hype cycles and are taking root in the popular imagination.
In the near term, developers are likely to expand the use of digital twins to a broader range of entities, from body parts to people, from smart cities to global supply chains.
As the price of digital twin technology decreases, companies will no longer limit its use to their most expensive, mission-critical equipment. The continued growth of digital twin development options, IoT infrastructure and related concepts like the metaverse should help popularize digital twins and make them easier to deploy.
Most market researchers expect growth to skyrocket. For example, Global Market Insights expects a compound annual growth rate of 25% that will take the digital twin market to $125 billion by 2032.
Significant advances in digital twin technology are also on the horizon. For example, researchers are discussing cognitive digital twins with generative AI and ML capabilities that enable them to function as intelligent companions to their real-world counterparts. Software developers are working on alternative database schemas, including semantic networks, that can unify the myriad data sources that feed into digital twins and make them more suitable for training generative AI.
Digital twin optimists imagine a day when intelligent digital twins become ubiquitous at every level of complexity, from household objects to the global environment. Whether this level of digital transformation is realistic, let alone desirable, is open to debate, but few would dispute that digital twins have secured a role in that future.
Various industries, including manufacturing and healthcare, use digital twins. Recent advances in AI and machine learning have also created use cases for digital twins in unified communications.
09 Apr 2025