Digital twins—virtual representations of real-world objects—can transform fields like construction, logistics, health care and engineering. Effective digital twins require a large amount of information for effective simulation and modeling of the object, place, or system they represent. However, gathering this data and ensuring its accuracy can be challenging. Information from Internet of Things (IoT) sensors, monitors, and tracking devices can provide the foundation for a digital twin. Some businesses are already using them to create digital twins that utilize real-time IoT data.
Digital twins—high-detail virtual replicas of physical devices, places, or systems—allow information technology (IT) and data specialists to simulate the function of an object before it exists or monitor an extant object in real-time. The twin also makes it easier to visualize data and how it relates to the item being modeled.
The technology can be used to represent a wide variety of objects—in theory, almost anything that exists in the real world. End-users already leverage digital twins to model items as small as an individual subcomponent or as large as an entire city or supply chain. In practice, digital twins are primarily used to effectively test component designs, simulate machinery or buildings like factories, and visualize the flow of goods through the supply chain.
Effective digital twins require a large amount of information. Businesses wanting to use digital twins for real-time monitoring also need a data source relevant to their interests and needs. Collecting the amount of data needed for a digital twin can be challenging, especially for operations without a dedicated IT team. Real-time monitoring solutions may not provide the flexibility or adaptability required by a company populating a digital twin with relevant data.
IoT sensors can help solve this problem. IoT technology is increasingly popular for purposes like monitoring, remote access, and asset tracking. Data collected by IoT devices is delivered to the cloud automatically, where it can be used by various systems—including digital twins.
For example, supply chain managers are using IoT devices more often to improve supply chain visibility. These same devices can help lay the groundwork for a supply chain digital twin. Devices like asset trackers attached to cargo containers can continuously monitor and report their current location, as well as information on local shipping and environmental conditions. Once available on the cloud, this data can be imported into a digital twin, where it will help a business observe the movement of goods through the supply chain.
A business that maintains a digital twin of a building may use a similar strategy to capture and represent data on air quality, lighting, or foot traffic. A manufacturer could use IoT monitors and a digital twin to create a preventive maintenance tool or a real-time model of machine health.
Many modern digital twin platforms, like Microsoft’s Azure system, are built with IoT data sources in mind. These platforms allow businesses to better integrate their digital and virtual processes. A company’s fleet of IoT devices provides the data needed for a digital twin, which offers a representation that makes that information easier for people and analysts to understand.
Digital twins can enable businesses to simulate objects, monitor facilities, and predict problems before they occur. They can also be powerful data visualization tools, making stored information on an object more accessible and easier to understand.
For example, imagine a digital twin of a building that looks like a sophisticated 3D model. Stakeholders can use it to quickly get a sense of the building’s structure and how systems like heating, ventilation, and air conditioning (HVAC) and plumbing are integrated into walls or distributed throughout the facility. Another twin could provide a manager with a bird’s-eye view of how goods flow through a business’s supply chain or the aerodynamics of a new vehicle design.
The same digital twins may also be powerful simulation tools. They make it possible to simulate potential changes to layout or upgrades to a building system, then observe how they will work in practice and impact other aspects of the building. A supply chain digital twin could allow a business to simulate process, equipment, and workflow changes to better understand the overall impact these changes may have on the flow of goods.
Some early adopters of the technology are beginning to publish case studies on what they’ve learned and the potential benefits of digital twin design. For example, one manufacturer that faced significant quality issues in the field turned to digital twin technology to improve quality and reduce warranty liability. The business modeled the machines it manufactured using data from a digital bill of materials and information captured during the manufacturing process. This dataset included procured parts and assembly details for each manufactured item.
Combining this data in one place allowed the business’s design team to better analyze its production process and link quality issues with various manufacturing variables and parameters. As a result, the team improved the assembly process and reduced rework by 15 to 20%. The company’s sales team is now preparing to apply the same process to items that have already been deployed in the field, allowing them to better understand how operating conditions and device maintenance may affect performance.
Digital twins may soon help the business improve asset availability management, implement predictive maintenance, and leverage spare parts inventory optimization.
Digital twins can provide significant benefits for a range of industries, but they require a large amount of information to work well. IoT devices can generate and transmit vast quantities of data from real-world objects, like buildings and heavy machinery, that businesses can use to build their digital twins. Some companies are already using these twins to improve quality assurance and better understand their manufacturing process. IoT devices can make digital twins even more useful, offering real-time data that can accurately represent a modeled object.