By Poornima Apte
Wouldn’t it be nice if you could measure twice and cut once, especially when millions of enterprise dollars are on the line? This is precisely what a digital twin enables.
A digital twin is an exact virtual copy of an object, product, piece of equipment, person, process, supply chain, or even a complete business ecosystem. Using a digital twin, enterprises can test drive the effects of various variables on each other before the model hits the real world.
The integration of information technology (IT) and operational technology (OT) in asset-driven industries empowers the digital twin. Through Internet of Things (IoT) sensors, machines can “talk” and we can incorporate that data in real time into the digital twin. Simulations and modeling of complex systems is not new. Digital twins take these a step further by forecasting in real time and by allowing engineers to monitor infinitely more complex systems, even entire cities.
And digital twins overlay complex systems —HVAC on top of plumbing for example—and feed on a variety of interrelated inputs so users can see how one input affects others. Cloud-connected data facilitates both structural and operational views of what happens to the object in real-time, allowing engineers to monitor systems and model systems dynamics. Enterprises can adjust any parameter on the digital twin to see how the system would change in real life before making any changes to the original. They can make more informed decisions and even simulate how a decision today might affect parameters tomorrow.
As more companies embrace IoT, digital twin technology is becoming more popular across a variety of sectors. Valued at $6.5 billion in 2021, the global market for digital twins is set to grow at an impressive 39.48 percent compounded annual growth rate until 2030.
How can enterprises take advantage of this technology? Here are ten ways:
As manufacturing becomes increasingly digitized, companies can create digital twins of production lines to test process efficiencies and find and fix issues before changing physical components. Such digital twins can incorporate information about maintenance time, cost of parts, maintenance, and more. Production managers can also plug in information about work schedules and production plans to see what bottlenecks, if any, these might create on operations. Digital twins are also useful in being able to calculate the sustainability impacts of various processes before going into full operation mode.
While enterprises need not set up an elaborate digital twin to access predictive maintenance, it is a pleasant side effect of leveraging digital twins already in place. Digital twins collect information about humidity, vibration, motion, and more from IoT-connected devices. It can become easier to see outliers or spikes in usage with the digital twin and the technology can predict degradation of equipment via regular monitoring.
Digital twins can help understand changes in supply and demand at each link in the supply chain, from manufacturers to distributors and retail stores. In addition to factoring in demand and supply data, digital twins can include external environmental factors, such as weather or labor strikes, to influence stock recommendations and avoid under- or overflow. Digital twins can also model scenarios such as shifts in production lines or availability of workers.
Businesses can use a digital twin of a warehouse to optimize production by changing factors such as floor plans and automation. Digital twins can also simulate special conditions such as holiday demand spikes, customer demand changes, or equipment downtime, to see how warehouse systems react to these changes. These simulations help businesses assess average warehouse performance and pinpoint how operations could be improved.
As the clamor for sustainable agriculture to feed more people grows louder, digital twins can help analyze and optimize resource use on a farm. Integration of data points, such as satellite images of crops, soil samples, and farm equipment data, into a digital twin has the potential to better maximize agricultural outcomes.
Smart cities generate data from a variety of sources, including IoT sensors in traffic lights at street corners, HVAC systems, even trash collectors. This data can be combined into a digital twin that can help urban planners build scenarios in the digital realm before implementing them in the real world. Traffic flows can be analyzed to improve pedestrian safety, optimize traffic flow, and encourage different forms of transportation. Digital twins can also analyze the impact of urban development in specific areas of a city.
Digital twins can be used to locate and address areas of risk in a nuclear power plant using modeling and real-time data. One digital twin successfully predicted and autonomously corrected the temperature of a heat pipe in a power plant, adjusting the heat pipe temperature to avoid future complications. Autonomous control of the digital twin saves money and keeps workers safe.
Digital twins help retailers optimize key factors in stores, such as layout and product freshness. Kroger used digital twins to optimize the freshness of meat and produce, schedules for meat-cutting, and layouts of physical product. Digital twins can include IoT sensor data from physical points in a store as well as data from customer activities. They can also be used to reduce waste across the supply chain by identifying traffic and weather conditions, supply shortages, bottlenecks, and other shortages in advance.
Digital twins can assist with understanding and optimizing complex systems at scale. They can be used to optimize hospital planning, including factors such as bed shortages, spread of pathogens, staff schedules, and operating room capacity. They can optimize hospital performance, efficiency, and safety. As a result, digital twins deliver positive outcomes on patient care and resource use.
The utilities sector is vulnerable in multiple ways: Old infrastructure, natural disasters, and cybersecurity issues all put utilities at risk. Digital twins’ real-time visualizations of physical assets can help utility companies improve efficiency of operations and stress-test how current systems would fare during a natural disaster or other risky scenario. Water utilities can also use digital twins to optimize future performance and improve quality and leak detection.
Poornima Apte is an independent technology writer in Boston, Mass.