This experience is not available in the language you selected. Proceed Digital Twin: Transforming How We Make Sense of Data Learn why digital twin is a strategic imperative for digital transformation What is a digital twin? A digital twin is a virtual representation of a physical product, process, person, or place that can understand and measure its physical counterparts. A digital twin has three components: a digital definition of its counterpart (generated from CAD, PLM, etc.), operational/experiential data of its counterpart (gathered from Internet of Things data, real-world telemetry, and beyond), and an information model (dashboards, HMIs, and more) that correlates and presents the data to drive decision-making. A digital twin is much more than a simulation, which is merely a data-driven prediction for how a physical environment/process/person/product will behave. A digital twin spans the full product lifecycle and has engineering, manufacturing, and service use cases. Why is a digital twin important? With continued advances in digital technology, digital twins are becoming more robust—and more important to enterprise companies. With digital twin technology, companies can use real-world product data to inform improvements to the next generation of product, identify bottlenecks in processes with more ease, or support service technicians in the field leading to faster repair. When looking at use cases for digital twins, consider the ROI and value they will bring to the business. Digital twins virtually duplicate the physical asset in a completely digital environment. Data, often captured through smart sensors and other IoT technology, is fed into a software system designed to consistently recreate the asset’s actions, characteristics, capabilities, and operational behaviors in real time. It is this continuous flow of data that separates a digital twin from a simulation. Simulations are virtual models of various assets; however, they do not require a constant dialogue with the physical world to create various readouts and potential operational paths for their asset in question. Given the sheer quantity of information involved in creating and maintaining a digital twin, it is common for machine learning to be deployed to help sort and prioritize the data. Machine learning is an AI application utilized to make algorithms and models that can perform tasks without constant, specific instructions from the user. It instead relies on pattern analysis and inference for future operations. What are some challenges the digital twin helps solve? Downtime reduction: Downtime, planned or unplanned, can cost a company significant money. With digital twin technology, businesses can be better prepared to solve issues faster, or avoid them altogether. Operational efficiency: Digital twins can expose previously undetectable issues and guide managers to make data-driven improvements. Product improvements : Product designers can use the insights from digital twins to improve the product in future iterations or uncover opportunities for new product lines or features based on product usage data. Improve customer experience : Digital twins can be used to deliver novel experiences and features to customers. Optimize service capabilities: Maximize customer satisfaction by optimizing the people, inventory, processes, and technologies of the service organization. Consistent product quality : Because digital twins have a physical counterpart, operators can see detailed data and insights, find patterns, and resolve quality or service issues proactively. What are the benefits of digital twin technology? Enhance supply chain agility and resilience Supply chain disruptions have put a spotlight on agility and resilience. A combination of emerging technologies and platforms have made it possible to pursue a digital twin of the physical end-to-end supply chain. With this type of digital twin, companies get visibility into their supply chain, such as lead times, and can make real-time adjustments internally and with their partners. Reduce product time to market With digital twins, companies receive continuous insights into how their products are performing in the field. With these insights, they can iterate and innovate products faster and with more efficiency. Enable new business models (i.e., product as a service) Digital twins sometimes have a secondary benefit if you’re able to think about the possibilities. With more data visibility into products, there could be opportunities for subscriptions and offerings that deliver enhanced service or support to customers. Increase customer satisfaction Digital twins can support improved customer satisfaction though use cases like predictive maintenance, but because they collect real-time data on the product, they can also enable smoother customer service and repair operations, while informing future product improvements. Improve product quality This benefit comes with time and data collection through digital twins. After initial investments have been made, generational improvements of a product—based on real-world operational data from many digital twins—can inform engineers and designers when developing a new product or version. Drive operational efficiency Digital twins offer the insights necessary to gain those operational efficiencies across the value chain. With process-based digital twins, for example, organizations can bring together different data sets to capture real-time information on asset and production performance. Not only can they see where there might be bottlenecks, but also how potential solutions could impact the overall process. Improve productivity The challenge of employee turnover and retention is nearly universal across industries. When a skilled employee leaves, they almost always take their knowledge with them, creating a barrier that slows productivity. With digital twins, organizations can mitigate some of these challenges through remote monitoring and assistance. Inform sustainability efforts There are opportunities across the value chain to identify sustainability opportunities with digital twins. It can mean swapping out product materials for more sustainable options, reducing carbon emissions or scrap in the manufacturing process, or decreasing the number of service truck rolls. Increase data visibility Digital twins can break down data silos across the enterprise and unlock value across the product (or process) lifecycle. Historical data and real-time data all live in one place. Types of digital twins Product Twins Also sometimes known as a unit digital twin, a product digital twin is the virtual representation of a product, either after conception or throughout its entire lifecycle. This level incorporates products of varying levels of complexity. Simple product digital twins are not much different from part or asset digital twins. Product digital twins can be used for a variety of purposes, including design improvements, manufacturing efficiency, and improved service rates. Process Twins Process digital twins are complex endeavors aiming to understand how various systems work with one another. Manufacturing brings together diverse hardware systems working toward a common goal. Process twins visualize these types of interactions and provide the user with actionable feedback to improve process speeds and quality standards. Part Twins Also known as a component digital twin, this is the technology on its most focused level. Part twins are only concerned with measuring data on basic parts or components of larger systems. This can be helpful to better understand certain component performance and identify larger problems before they occur. Asset Twins The next level up from part twins, asset digital twins focus on the specific interactions between two parts or components. They are not concerned with the interactive system as a whole—that is what process digital twins are for. People Twins People digital twins focus on trying to understand the role of the user in the environment they are in. A people twin could optimize user access across the factory floor or improve safety standards in dangerous situations. Place Twins A place digital twin is the largest scale of digital twin. A factory twin would be an example of a place digital twin. Sometimes conflated with process twins, place digital twins include spatial data, including climate, temperature, and context. What can digital twin do? Digital twins to improve service and uptime With PTC as a partner, Howden is using technologies like augmented reality and IoT to demonstrate the power of immersive experiences. One of Howden’s goals is to use digital twin technology to reduce business risk for their customers by improving the uptime of deployed products. Digital twin of place, process, and people The emerging technologies of spatial computing and analytics are enabling a digital twin of place. With both a bird’s eye and detailed view of a factory floor enabled by integrating multiple sets of data, spatial analysis offers visibility into movements within a space, and can make data-driven recommendations on how to improve processes and performance. See how it works in this video from the PTC Reality Lab. Real-time data integration Digital twins defining feature is their ability to utilize data from the real world in real time. Arguably, two factors control the effectiveness of a digital twin more than any other: timing and data. The more sources of information incorporated by the twin, the more complete the readout of its physical counterpart. Effective IoT deployment will help users capture the data necessary to power their digital twins. Many smart sensors are designed to facilitate this constant stream of data from the physical to the digital. Bidirectional communication While some organizations may classify a stand-alone simulation as a digital twin, PTC does not. The crucial difference is bidirectional communication. Simulations can be incredibly complex and intricate, as well as highly useful in many situations—but they are, by nature, stand-alone. There is no communication between the digital simulation and its real-world counterpart. Digital twins, by contrast, exist through bidirectional data communication. This means that every change in the physical world is dynamically recorded to the digital twin, allowing it to change and update itself with new information. Predictive modeling Using digital twin technology, especially in conjunction with machine learning AI technology, can allow for predictive modeling. Simply, not just understanding the physical twin now but in the future as well. Effective digital twin modeling measures what is happening, and AI can use these patterns to accurately predict future behaviors and operations—at least within the parameters of its capabilities. A digital twin of a machine, for instance, does not necessarily have spatial awareness of a heating leak in the nearby area. It will, however, inform the user of a temperature change in the machine. This is why PTC encourages comprehensive IoT layering—the more data feeding a digital twin, the more effective it becomes. Anytime monitoring and improved control Using a digital twin means gaining a comprehensive overview of how the physical counterpart is performing. As long as the bidirectional flow of data is preserved, the user will have an accurate, up-to-date readout—regardless of the time of day. This increased oversight opens new opportunities for improved control, which can lead to efficiency improvements and ultimately increased productivity. Industry applications of digital twins Applications with digital twins are still emerging. With the technology comes the capability for real-time feedback—and even predictive monitoring and insights. This has the potential to expose new revenue opportunities such as enhanced service delivery. The outcomes of these use cases are increased customer satisfaction and loyalty, driven by improved asset uptime and faster time to resolution. Digital twins will make a difference across three areas: engineering, manufacturing, and service. In engineering, digital twin technology provides a product lens that enables teams to better understand how products are being used in the field, and then use that data to build better products. Through this closed-loop design process, engineering organizations optimize product form, fit, and function, as well as quality far beyond what can be achieved when relying on static specification documents. Leveraging a digital model and simulation tools, digital twins can validate performance well before—and in some cases in lieu of—physical prototyp.. To support maintenance and service teams, digital twins are being used to enhance service delivery and offerings that improve customer satisfaction through increased uptime and faster time to resolution. Teams are leveraging it for service parts identification and fulfillment, visual procedure guidance/verification for frontline workers, remote monitoring, and predictive service and maintenance. New technologies are emerging that will enable high-fidelity digital twins, as well as connections to transformative manufacturing processes, from generative design to additive manufacturing. Digital twins, when combined with artificial intelligence capabilities, such as machine learning and deep learning algorithms, will derive new operational insights. Increased transparency and security will be made possible with blockchain as well. As digital twins become more prevalent across the industry, there is great potential in orchestrating digital twin networks and enabling communications between two distinct digital twins. Digital twin frequently asked questions What is the history of digital twin technology? Digital twin technology has been a concept since the 1960s, pioneered by NASA, who physically duplicated systems on Earth to match the systems in space. The NASA version of digital twin technology enabled the team to get the Apollo 13 crew home safely. A new era of digital twins was ushered in by Michael Grieves, a faculty member at the University of Michigan, in 2002. He proposed a digital twin must have a connection between the physical and digital version. This definition persists today with IoT technology increasing the relevance, fidelity, and cost-effectiveness of digital twins. Are digital twins considered AI? Yes and no. Technically, a digital twin can be made simply by attaching a sensor to a physical object and recording its data. That said, many digital twins use machine learning (an application of AI) to process and analyze this information in a way that makes it actionable to the user. What are digital twins in the metaverse? Digital twins function in the metaverse (or spatial computing) much the same way they function in traditional computing. The industrial metaverse often makes use of spatial twins, which are location-based digital twins that provide real-world 3D context to workflow and operational efficiency. Where are digital twins used? Digital twins are becoming more common across multiple industries, including manufacturing, healthcare, construction, urban planning, oil and gas, aerospace and defense, automotive, and more. They’re used in various roles and use cases across the product lifecycle from engineering to manufacturing to service. What’s the difference between a digital thread and a digital twin? A digital thread is the term used to describe universal access to data. It’s the connection synchronizing related upstream and downstream information from multiple sources and systems. A digital thread enables a more complete, real-time representation of a product, process, or person across enterprise functions. This data unification of a digital thread is a prerequisite to building a robust digital twin. How are digital twins different from simulations? A simulation mirrors a physical process, place, person, or product—it never once measures its counterpart. It is an unanchored digital representation of a physical location but without the constant measuring and reflecting that goes on with a digital twin. Boiled down, digital twins can only exist if they have a physical counterpart while simulations do not necessarily need any real-world counterpart. Stay up to date on digital transformation Curated for industrial leaders, the monthly Digital Transformation Newsletter highlights the latest insights on business and technology strategy from respected research analysts presented in easy-to-digest executive summaries. Stay up to date on digital transformation Please wait while your request is being submitted.. Hi {name}, welcome back. Swaziland Sweden Switzerland Taiwan Tajikistan Venezuela Vietnam Sign Up Support Portal