What is a digital twin? A real-time, virtual representation

Digital twin definition

Digital twins are real-time, virtual representations of objects, processes, and systems. 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,” according to Gartner. “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, according to digital product engineering company GlobalLogic:

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.

Digital twin technology

Digital twins consist of three primary elements, according to systems integrator, SL Controls:

1. Historical data. Data on the past performance of machines, processes, and systems.
2. Real-time data. Digital twins receive continual updates from equipment sensors and outputs from platforms and systems, like manufacturing equipment, customer service, and purchasing.
3. Future data. This includes machine learning and inputs from engineers.

The various types of digital twins in use today are differentiated largely by their area of application, according to IBM:

• Component twins/parts twins. These are the basic unit of digital twins. Here the twin represents the physical, mechanical, and/or electrical characteristics of a component or part.
• Asset twins. Assets are two or more components working together. These twins represent the interaction of components to create performance data.
• System or unit twins. These twins reflect the interaction of assets that form a system or unit.
• Process twins. These twins represent how systems work together. For example, a process twin might represent an entire production facility.

Digital twins vs. simulations

Digital twins can be seen as simulations of physical objects and processes, but what sets digital twins apart is real-time updates.

Systems integrator, SL Controls, says that simulations allow engineers to run tests and conduct assessments of a physical asset, but the simulation is static. It depends on the engineer to input new parameters. Digital twins receive real-time updates from the physical asset, process, or system, allowing engineers to perform tests, assessments, and analysis work using real-world conditions.

Digital twin examples

Organizations are already using digital twins to improve their businesses.

Rolls-Royce improves jet engine efficiency: Multinational aerospace and defense company Rolls-Royce has deployed digital twin technology to monitor the engines it produces. The company can monitor how each engine flies, the conditions in which it’s flying, and how the pilot uses it. Rolls-Royce uses the digital twin to offer service plans tailored to specific engines.

Mars optimizes its supply chain: Confectionary, pet care, and food company Mars has created a digital twin of its manufacturing supply chain to support its businesses. The company is using the technology to improve capacity and process controls, including boosting the uptime of machines via predictive maintenance and reducing waste associated with machines packaging inconsistent product quantities.

TIAA reduces client service complexity: The Teachers Insurance and Annuity Association of America-College Retirement Equities Fund (TIAA) is using a digital twin to reduce the complexity of onboarding new institutional clients. TIAA’s twin uses product metadata, operational flows, overlapping interdependencies, and business rules to validate plan operators’ service plan selections.

Bayer Crop Science reshapes strategy with virtual factories: Bayer Crop Science has created “virtual factories” for each of its nine corn seed manufacturing sites in North America. The virtual factories are dynamic digital representations of the equipment, process and product flow characteristics, bill of materials, and operating rules for each of the nine sites. In addition to providing a real-time view of operations, the virtual factories allow Bayer to perform “what-if” analyses that the company uses to analyze new strategies, make capital purchase decisions, create long-range business plans, and improve processes.

Digital twin software

Digital twin software platforms incorporate IoT sensor data and other data to monitor asset performance and run simulations. Consultant Ian Skerrett says scalable digital twin platforms have six key features:

1. Manage the digital twin lifecycle. Skerrett says digital twins are the instantiations of “digital threads” or “digital masters,” i.e., the requirements, parts, and control systems that make up a physical asset. For instance, Skerrett says a windmill’s digital master consists of the engineering diagrams, bill of material, software versions, and other artifacts used to create a windmill. A digital twin platform for windmills needs to leverage that digital master and provide tools to test, deploy and manage the digital twin based on the digital master. These tools need to be able to scale to hundreds of digital masters and thousands of digital twins.
2. Single source of truth. Platforms need to be able to update and provide the exact state for each digital twin. For example, routine maintenance might lead one asset to have a different part or firmware version than another asset. A platform must be able to update the exact state for each object and asset as soon as it changes.
3. Open API. As the interface and integration point for an industrial IoT solution, the platform must provide an open API that allows any system to interact with it.
4. Visualization and analysis. The platform must allow the organization to create visualizations, dashboards, and in-depth analyses of live data from the digital twin. The live data should be linked to the digital master.
5. Event and process management. The platform must allow users to create events and business processes that can be executed based on the platform’s data.
6. Customer and user perspective. The platform needs to enable collaboration between the stakeholders of a digital twin. It should reflect what organization owns or operates each one and what users are allowed to access the data.

Digital twin companies

Many organizations are building their own digital twins, but there are some popular platform vendors. Some of the more popular offerings include:

• aPriori Digital Manufacturing Simulation Software
• Autodesk Digital Twin
• Ayla IoT Platform
• AWS IoT
• Enterprise Process Center (EPC) from Interfacing Technologies
• Oracle IoT Production Monitoring Cloud
• Predix Platform from GE
• SAP Leonardo Internet of Things

Digital twin market

The digital twin market is growing at a rapid clip. Research firm, MarketsandMarkets, says the global digital twin market was $3.1 billion in 2020 and is expected to reach $63.5 billion by 2027. It considers digital twin a key component of the fourth industrial revolution (or “Industry 4.0”).

“In Industry 4.0, the digital twin is considered to integrate the manufacturing techniques with advanced technology like IoT that helps in developing interconnected manufacturing systems,” MarketsandMarkets says in its report. “Thus, digital twin technology seems to be an ideal solution that facilitates the companies in realizing the Industry 4.0 standards.”

The firm believes the automotive and transportation, energy and power, and aerospace and defense verticals are the key end-users of digital twin technology.