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What Are Digital Twins?

“A Digital Twin is a computer program that takes real-world data about a physical object or system as inputs and produces as outputs predictions or simulations of how that physical object or system will be affected by those inputs.’

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 comes down, 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 35% that will take the Digital Twin market to $50 billion by 2027.

Major advances in Digital Twin technology are on the horizon. For example, researchers have been discussing cognitive digital twins that would have AI and machine learning capabilities that allow them to function as intelligent companions to their real-world counterparts.

What Is A Digital Twin

Digital Twins are real-time virtual representations of objects, processes, and systems that can help organizations monitor operations, perform predictive maintenance, and improve processes.

While Digital Twins can represent purely digital things, they most frequently serve as a bridge between the physical and digital domains. For example, a Digital Twin could provide a digital view of the operations of a factory, communications network, or the flow of packages through a logistics system.

The implementation of a Digital Twin is an encapsulated software object or model that mirrors a unique physical object, process, organization, person, or other abstraction. Data from multiple Digital Twins can be aggregated for a composite view across a number of real-world entities, such as a power plant or a city, and their related processes.

Benefits Of Digital Twins 

These virtual clones of physical operations can help organizations monitor operations, perform predictive maintenance, and provide insight for capital purchase decisions. They can also help organizations simulate scenarios that would be too time-consuming or expensive to test with physical assets, create long-range business plans, identify new inventions, and improve processes.

Digital Twins  offer five key benefits:

  1. Accelerated risk assessment and production time. Digital Twins   can help companies test and validate their products virtually before they exist in the real world. They can be used by engineers to identify process failures.
  2. Predictive maintenance. Organizations can use Digital Twins  to proactively monitor equipment and systems to schedule maintenance before they break down, improving production efficiency.
  3. Real-time remote monitoring. Users can monitor and control systems remotely.
  4. Better team collaboration. GlobalLogic notes that process automation and 24×7 access to system information lets technicians focus more of their time on collaboration.
  5. Better financial decision-making. By integrating financial data, organizations can use Digital Twins  to make better and faster decisions about adjustments.

How Does A Digital Twin Work

A Digital Twin is a virtual model designed to accurately reflect a physical object. The object being studied, for example, a wind turbine is outfitted with various sensors related to vital areas of functionality. These sensors produce data about different aspects of the physical object’s performance, such as energy output, temperature, weather conditions and more. This data is then relayed to a processing system and applied to the digital copy. 

Once informed with such data, the virtual model can be used to run simulations, study performance issues and generate possible improvements, all with the goal of generating valuable insights which can then be applied back to the original physical object.

Types Of Digital Twins

Several ways of categorizing digital twins exist, but the following four categories, organized in a hierarchy, are by far the most common:

  • Component twins (also referred to as part twins). The most basic level; it’s not for simple parts like screws but for things like mechanical subassemblies.
  • Asset twins (product). Two or more components whose interaction is represented in the digital twin.
  • System twins (unit). Assets assembled into a complete, functioning unit.
  • Process twins. Systems working together to serve a larger goal.

Challenges Of Digital Twins

Organizations looking to develop Digital Twins face other daunting hurdles. Here are six of the biggest Digital Twin challenges:

  • Data management. Data Cleansing is often needed to make data from a CAD model or IoT sensor usable in a digital twin. A data lake might need to be established to manage the digital twin data and perform analytics on it. Deciding who owns the data is another problem.
  • Data security. Digital Twin data is timely and mission critical, but it also travels through several networks and software applications, which makes securing it at every stage challenging.
  • IoT development. As the preferred data source for most of the real-time and historical data about an entity or process, IoT sensors are usually a basic requirement of digital twins. Implementing IoT presents big challenges in network infrastructure and storage capacity, device and data security, and device management.
  • System integration. Digital Twins often begin life in CAD software but get more use in PLM, where they’re used in post-sale services, such as performance monitoring and equipment maintenance. Numerous CAD and PLM software vendors have one-to-one integrations, but it isn’t always adequate and smaller vendors may have no built-in integration.
  • Supplier collaboration. The numerous participants in a supply chain must be willing to share information from their own production processes to ensure that the information in a digital twin is complete.
  • Complexity. The data collected in the different software applications used by a manufacturer and its suppliers is not only voluminous, it changes often. Last-minute design changes, for example, must make it into the final version of the twin so the customer and manufacturer have the most current information.

Use Cases And Examples Of Digital Twins

The initial deployments of Digital Twins have mostly been directed at the design, production and maintenance of extremely high-value, physically large equipment, such as airplanes, buildings, bridges and power-generation plants where 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:

Manufacturing. The industrial world is widely acknowledged as the pioneer in the use of 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.

Utilities and energy. Electric companies are investigating Digital Twins to design, monitor and maintain power plants, electric grids, transmission and consumption. The 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.

Healthcare. 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 real patients. Researchers are already running simulations on anonymized digital twin data to identify the best therapy options.

Urban planning and construction. Digital Twins are being used in the design of large buildings and offshore oil rigs. Some users are vastly expanding construction twins to encompass neighborhoods and cities, with a focus on infrastructure. The UK even has an initiative to develop a national Digital Twin. 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 to make transportation more efficient and conserve energy, among many goals.

Automotive. Digital Twins play their usual role in the vehicle product-design stage, but also in 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 reduce massive recalls by allowing each vehicle’s unique twin to be analyzed for the presence of a defect.

Retail and e-commerce. Retailers have begun to use Digital Twins to model product placement, the customer’s journey through a store and the impact 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.

Final Thoughts

Digital Twin technology combined with the latest ML and AI tools is helping companies across many industries reduce operational costs, increase productivity, improve performance, and change the way predictive maintenance is done. For product manufacturers in particular, Digital Twin technology is crucial to achieving more efficient production lines and faster time-to-market.

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