Published on 28th November 2022
Also known as “process simulation technology,” digital twin technology has gained a lot of momentum in recent times with applications spanning the automotive industry, healthcare services, manufacturing operations, power generation equipment, urban planning and more. Digital twins (DTs) are an integral part of Industry 4.0 (4IR or the Fourth Industrial Revolution) and form the foundation for connected products and services.
A digital twin is a virtual representation of the real world, including a behavior, person, place, process, product, relationship, service, or system which involves pairing of the physical and virtual worlds.
A digital twin mirrors and measures its physical counterpart, and in doing so, covers the entire lifecycle of a physical asset.
Creating a dynamic software model/digital replica of a real-world entity/system allows us to analyze data and monitor systems to preempt problems, prevent downtime, explore new opportunities, and use simulations to plan for the future.
Digital twin initiatives are becoming crucial in unlocking the value of data across enterprises and large-scale products or projects.
Read on to learn more about digital twin technology and why it is becoming increasingly relevant in a data-driven world.
History of the Digital Twin Concept
The first account of the digital twin technology is attributed to David Gelernter’s 1991 book “Mirror Worlds,” which explores the impact of computational technology on the future world.
The “digital twin” concept and model were first introduced by Michael Grieves in 2002 at the University of Michigan, wherein he proposed the digital twin concept as a study model for product lifecycle management (PLM) in the white paper titled “Digital Twin: Manufacturing Excellence through Virtual Factory Replication.”
Furthermore, John Vickers of NASA is credited with formally inventing the term “digital twins” in a “2010 Roadmap Report.”
Why do we need digital twins? The need primarily stems from the requirement of instant access to a single source of trusted, verified data for timely, effective decision-making.
Digital twins, including 3D digital twins, are visually immersive and accurate models that provide tangible benefits in terms of increased revenue growth and planning efficiency, optimal asset utilization, and reduction in expenses.
So, owner-operators can expect to see an increase in fill rates and a decrease in duplication of efforts and manual intervention in addition to improved inventory reliability and visibility.
A digital twin mirrors a unique object, organization, person, or process. Architects, urban designers, urban planners and others can aggregate data from multiple digital twins for a composite view across several real-world entities, such as a building, city, factory or a power plant, and their related processes.
A virtual world replica of a physical asset or the built environment relies on data gathered by an extensive network of IoT sensors to provide an accurate reflection of the asset.
Typically, applied mathematics and data science experts study a physical object or system being replicated and use that data to develop a mathematical model that simulates its real-word counterpart in digital space.
The constructed twin is capable of providing insights into performance and potential problems of the physical object and can be as complicated or as simple depending on the digital twin application.
As IoT devices evolve, there would be digital-twin scenarios with less complex and smaller objects, which would generate more benefits for companies.
The digital twin framework typically consists of three components:
Unlike a virtual prototype, a digital twin is continually updated with data about the performance, maintenance, and health status throughout a physical asset or system’s lifecycle.
In the context of digital twin technology, a digital thread creates a closed loop between the digital and physical worlds and fosters bi-directional data flows (from the physical to the virtual object and vice versa), which minimize the time wasted on sourcing and checking data.
Let us look at how a digital twin works to solve business challenges:
Data capture and integration—Digital twins are transforming how organizations capture and model data, analyze information, integrate networks, and streamline workflows.
Real-time information and visualization—Discovering new insights, making better-informed and transparent decisions, and unlocking the potential of data are easy with dashboards and reporting, advanced visualization, and real-time IoT integration.
Sharing and collaboration—It is possible to eliminate data silos, maintain dynamic visual communication, increase internal and external engagement, and provide pervasive data access while improving information sharing across communities and organizations.
Analysis and prediction—Automation methods (such as AI – artificial intelligence/DL – deep learning/ML – machine learning) and simulation and scenario modeling are highly beneficial when it comes to advanced analysis and automation of future predictions.
Digital twins are being adopted more and more across global operations and supply chains to understand the present and predict the future. Who defines requirements for new digital twin standards and drives the best practices for digital twin usage?
An example is the Digital Twin Consortium® (DTC)—a global ecosystem of users comprising academia, government, and industry. The Consortium is a collaborative partnership that drives the awareness, adoption, interoperability, and development of digital twin technology.
In addition, as a topology- and location-sensitive form of distributed computing, edge computing can help more companies access digital twins for their operations, ensure data integrity and real-time decision-making capabilities.
By bringing computing power and data storage closer to modeled physical entities, edge computing helps avoid extra cost and time delays often associated with large cloud computing.
Digital twins exist in varying complexities and forms. The four major types of digital twins are:
Asset twins: Asset twins virtually represent how two or more components interact and work together as part of a more comprehensive system. An asset twin’s performance data can be analyzed to make informed decisions.
Component twins: A part twin simulates a functioning component and is tied to a part of a bigger system. Also known as “part twins,” component twins represent a single piece of an entire system. Usually, this is an essential part of asset operation, such as a wind turbine’s motor.
Process twins: Process twins provide insights into how a digital environment’s assets, components, and units work together. For example, a process digital twin can digitally reproduce an entire manufacturing facility’s operations and production processes, including all its components.
System twins: A system twin or a unit twin reflects historic and current state and predicts future state of a whole system or a particular part of the system. For example, system twins simulate an entire production line.
Other significant types of digital twins relate to product (captures the entire product lifecycle), person (delivers task information and capture data), and place (spatial) (virtualizes a place to learn more about the complex workings within the environment).
The primary advantages of a digital twin are:
Boost operational efficiency: Digital twins offer relevant insights to gather different data sets and capture real-time information (including real-time IoT data) on asset and production performance and thereby boost operational efficiency.
Strengthen sustainability efforts: Applying plant-wide digital twin models enables companies to use the data, visibility, and visualization for identifying potential areas for efficiency and discovering improvement opportunities across multiple dimensions.
For example, a company could reduce scrap in the manufacturing process or swap out product materials for more sustainable options.
Reduce equipment downtime: Live access to data ensures that equipment is up and running with timely inspection checks, monitoring of operating conditions and power consumption levels, automatic alerts and more.
A digital twin’s preventive and predictive maintenance capabilities increase equipment uptime, prevent lost revenue, and also improve public safety.
Improve supply chain agility and resilience: A digital twin of the physical end-to-end supply chain provides better visibility into lead times and other key factors.
Consequently, companies can make real-time adjustments internally and externally (with their partners) and become agile and resilient in the event of supply chain disruptions.
Reduce time-to-market: Continuous insights derived from digital twins promote faster iteration and innovation.
Moreover, using digital models and simulation tools to validate product performance before physical prototyping avoids late-stage redesign, reduces time-to-market, and results in substantial cost and time savings.
Enhance customer satisfaction: Understanding customer needs and making future product improvements are imperative to delivering customer satisfaction.
A digital twin enables businesses to expedite repair operations and provide smoother customer service, thereby, becoming a competitive differentiator in an increasingly customer-centric marketplace.
Other benefits of using digital twins include improved product design (aircraft engine, offshore oil platform, train, turbine, etc.) and product development, increased first-time-fix-rates, new business models, and remote monitoring and assistance.
Does creating a virtual twin have challenges or limitations? Yes. In practice, a digital twin is not a simple install and deploy solution—it takes consistent efforts, data standardization and more to develop a reliable and readable digital twin.
Here are some major challenges of a digital twin:
Inaccurate representation of a physical object: A virtual clone does carry risks. Inaccurate digital twin replicas do pose certain dangers. Misrepresenting an object or system to be replicated may lead to difficulties if enough information is not available regarding the physical counterpart.
Depending on the complexity of the physical item, it may not be easy to ensure the accuracy of the simulation without open, real-time, and quality data.
Affordability for small businesses: Creating a digital twin is an expensive, resource-driven process. Furthermore, every object is not complex enough to warrant the use of a digital twin.
Lack of data standards: It is essential for data to be made available in a usable format so that it can be applied and scaled across regions. Maintaining data standards is of utmost relevance as data interoperability and data integration are critical in the context of a digital twin.
Cooperation at all levels: A Digital Urban Twin especially needs buy-in and support from the local administration and other members from the mobility, urban planning, environmental and/or other departments to make it work locally and develop into a useful public administration tool.
Unlike 3D modeling, a digital twin provides real-time data along with a view of an object and also explores various environmental conditions that impact the object over time. In addition, having a digital equivalent is useful for creating varied “what-if” scenarios and optimizing IoT deployments for maximum efficiency.
Digital models have proven successful for a variety of applications, including:
Digital twins can help streamline urban planning by using data analytics to virtually test and enact policies and design real-world construction methods. Such digital representation provides actionable insights into designing, operating, maintaining, and sustaining smart cities to improve the quality of life of the citizens.
Additionally, creating a digital twin before constructing a building is a key factor in determining the suitability of the construction materials and preventing the risk of low-quality construction.
Moreover, civic authorities can use digital model data to audit the structural stability of old buildings and avoid potential issues like physical damage.
Digital twins also prove useful for improving a building’s self-sufficiency and reducing the urban heat island effect by providing energy consumption and other related data. Such data is vital to visualize a digital twin at city-scale and support smart city initiatives.
That’s not all. Digital twin city data supports the efforts of transport planners and urban planners in identifying road expansion opportunities and traffic congestion possibilities in the physical city, as well as testing the potency of MRTS (Mass Rapid Transit System) for enhancing public transportation.
A digital twin is not just a virtualized copy of a physical asset—it behaves like the modeled asset and is open to adjustments for reproducing a real-world object’s smallest details.
Tesla creates a digital simulation of its cars using data collected from vehicle sensors and uploaded to the cloud. These digital twins aid AI algorithms in detecting likely breakdowns and faults and minimizing the need for maintenance and repairs—consequently reducing the cost to the company of servicing cars under warranty while improving user experience.
End-to-end oversight of engineering data is the key to ensuring a quick review of relevant asset data (from a high-level overview of an asset down to granular detail) and achieving asset excellence by breaking down data silos and empowering collaboration.
Examples of engineering data include documents, drawings, laser scans, line lists, spreadsheets, and text files. Deploying digital twin technology eliminates disparate information processes, which cost time, lead to error, and reduce efficiency.
In the context of climate change and global warming, a digital twin is useful for mapping the future environment and its impact on cities.
For example, data related to the rise in sea levels and tectonic plate movement in the virtual city can be used to assess the risk in a physical city and prepare better to deal with natural disasters, such as cyclones, earthquakes, and floods.
As digital twin technology allows process changes to be modeled and explored in digital twin software, enterprises can leverage the technology for industrial and plant digitalization and production optimization without risking critical infrastructure on experimentation.
Instead of laborious data entry of production records, it is now easier to automate and update data in real time.
Moreover, technicians keep the digital twin up to date with operating data about the real-world functioning of a physical asset while sensors monitor the digitally twinned asset or process.
Digital twin software can both model hypothetical changes based on the relationships between components and actual past performances based on historical data to make either a simulated prediction or find a predicted outcome in the case of multiple variables.
A digital twin has the distinct advantage of accurately modeling industrial equipment and processes and revealing ways in which an asset may be used more efficiently. Besides close monitoring of an asset or process, plant operators and process engineers get a chance to run experiments in simulation and adjust the digital twin conditions without impacting a physical asset or causing costly production downtime.
According to John Vickers, NASA’s leading manufacturing expert and manager of NASA’s National Center for Advanced Manufacturing, “The ultimate vision for the digital twin is to create, test, and build our equipment in a virtual environment.”
NASA was the first to use pairing technology to develop mirrored systems (full-scale mockups of early space capsules) for space exploration missions. Today, NASA uses digital twins for next-generation aircraft and vehicles and new recommendations and roadmaps.
Digital twin technology is instrumental to establishing smart, sustainable, and people-centric cities. The Digital Urban Twin is a hub for the integration of real-time data and visualizations, which are usable for city planners and policymakers.
However, certain challenges need to be overcome for making digital twin adoption possible at a large scale.
Here are some other examples of digital twin applications across industries:
Healthcare: Band-aid sized sensors are helpful for sending health information to a digital twin that monitors and predicts a patient’s well-being.
Integration with big data: The integration of big data and digital twin technology has a wide range of applications, such as equipment manufacturing, fault diagnosis, product R&D, and supply chain management.
Integration with IIoT: The integration of digital twin technology and the industrial internet of things (IIoT) captures the complex industrial environment and enables the use of scalable and resilient digital twins for industrial automation, predictive analytics and more.
Port of Rotterdam: A digital twin of Europe’s busiest port tracks more than 80 ships a day and 460 million tons of cargo annually, generating 3% of Dutch GDP.
Digital twin use cases are so widespread across the world that it is now possible to:
Cities like Helsinki and Singapore are investing in digital twin technology to achieve a range of goals from sustainability to virtual tourism.
Here are some noteworthy digital twin use case examples:
Amsterdam: The world’s first 3D-printed pedestrian bridge spanning the Oudezijds Achterburgwal canal in central Amsterdam has its own digital twin. As part of the Turing Institute’s project, a network of sensors placed across the structure gathers data used by the digital twin to analyze the structure’s performance as it comes under stress during daily use.
Helsinki: Virtual tourism allows people to experience the world from the comfort of their homes and promotes a sustainable way of living. For instance, Zoan created Virtual Helsinki—a digital twin of the Helsinki City center created in high-quality 3D for VR that facilitates virtual visits and activities.
Singapore: Singapore has created a live test case for its virtual look-alike—a city-state model that comprises thousands of street-level and aerial images with more than a billion data points plotted in 3D and 14 core datasets covering land use, tree cover, and underground utilities.
The virtual city will help policymakers identify infrastructural pitfalls and study the feasibility of new building developments apart from planned future application of scenario planning for autonomous vehicles and robots.
In addition, aerial and street mapping tools will continuously map the entire island to keep the digital twin on par with Singapore’s evolution.
EODA: Lockheed Martin and NVIDIA announced a collaboration to build an Earth Observations Digital Twin (EODA) for the National Oceanic and Atmospheric Administration (NOAA).
The collaboration is aimed at monitoring global environmental conditions with accurate and timely observations through a combination of Lockheed Martin’s AI technology with NVIDIA Omniverse.
Let us briefly look at some leading digital twin innovations:
Amazon’s AWS IoT TwinMaker enables developers to create digital twins of real-world systems like buildings, factories, and production lines to improve equipment performance, increase production, and optimize building operations.
Here’s how AWS IoT TwinMaker works to create a holistic view of operations faster and with less effort:
GE’s (General Electric) “digital wind farm” involves configuring each wind turbine prior to construction, with the goal of improving efficiency by analyzing the data fed to the virtual equivalent of each wind turbine.
Such a virtual copy running in the cloud is believed to “get richer with every second of operational data” and contribute to productivity improvements.
The IBM® Digital Twin Exchange is a resource for asset-intensive industries, which supports a wide range of downloadable digital twin types, such as 3D CAD files, building information models, and engineering manuals.
The Digital Twin Exchange makes it easy for content providers to list and set pricing for their digital twins, manage inventory, and reach a large asset management audience. On the other hand, digital twin customers can search category menus by industry, manufacturer, and price, and enjoy a one-click download for digital twins.
Microsoft Corporation’s Azure Digital Twins use IoT spatial intelligence to create comprehensive digital models of physical environments. The Azure Digital Twins platform provides a live execution environment that historizes twin changes over time and assists in creating unique customer experiences and optimizing costs and operations.
Furthermore, it is easy to model and create custom domain models (such as energy networks, farms, factories, model buildings, stadiums, or even entire cities) of any connected environment using the Digital Twins Definition Language. Another notable feature is the integration with the Azure IoT Hub to build enterprise-grade IoT connected solutions.
Other prominent players include AVEVA, Cisco Systems, NVIDIA, PTC Inc. (formerly Parametric Technology Corporation), and Unity among digital twin companies.
From physically large projects (bridges, buildings, and other complex structures) to mechanically complex projects (such as aircraft, automobiles, and jet turbines), digital twin solutions help improve efficiency, increase asset value and ROI, leverage collaborative data, and reduce risks and loss of productivity.
These digital proxies of the physical world present new collaboration opportunities between physical product experts and data scientists to better understand customer needs, enhance existing products/operations/services, and drive business innovation.
Using a digital twin allows for asset simulation to model and predict how an asset will respond to various operating conditions. Virtual models of valuable assets play a key role in optimizing production and preparing engineering, maintenance, and operations teams for extraordinary situations with better predictability and stability.
When we gain greater insight with enriched data, we find certainty in uncertain times and an opportunity to improve decision readiness and quickly identify required strategies to deliver sustainable performance improvements.
Furthermore, companies investing in digital twin technology enjoy a significant improvement in cycle times of critical processes and experience a transformational shift in designing, manufacturing, using, and maintaining products. After all, digital twins equip software engineering and other teams with advanced analytical, monitoring, and predictive capabilities at their fingertips.
Unsurprisingly, digital twin technology is becoming a business imperative and an achievable goal worth investing as part of a larger digital transformation (DX) strategy. Companies that do not respond to this strategic accelerator or deploy their very own digital twins stand to lose a competitive advantage.
The global digital twin industry is projected to reach more than USD 125 billion by 2030, with the demand for digital twins continuing to grow across many industries.
To conclude, we can safely say that digital twin technology is worth the hype (and investment) and is here to take digital transformation to the next level!