Part 4

A new era of value creation

More and more industries are using Digital Twins to enable better decision making.

Even 10 years ago, with the exception of spacecraft, airplanes and military equipment, relatively few assets merited the investment in time, money and IT required to develop a Digital Twin.

Now, however, the democratization of data and computing power, along with broader access to AI, has made Digital Twins a viable tool for a much wider range of sectors and businesses. Meanwhile, many “connected” products, from household appliances to assembly line machines and transport trucks, can generate and transmit to the cloud vast amounts of data that can then be used for Digital Twin development, machine learning and predictive modeling.

 

Those trends are creating new business opportunities as companies gain the ability to manage virtual constructs and their physical assets simultaneously and to predict outcomes. Yet while the potential of Digital Twin Technology is clear, it’s important to remember that it is still in its infancy. The effectiveness of a Digital Twin and its outputs relies on quality data—the old rule of “garbage in, garbage out” still applies. The technology may also elevate concerns over cybersecurity and intellectual property (IP). And finally, the predictive value of Digital Twins depends heavily on the utility and effectiveness of the algorithms used to project future outcomes.

 

Yet despite those realities, the technology is evolving rapidly. As advances in AI, machine learning and predictive modeling accelerate and become more widely adopted across sectors, Digital Twins are poised to take an ever-larger role in business decision-making. Here are three examples:

  • Manufacturing: As production equipment becomes more connected, more automated and at the same time more costly, efficient maintenance and continual process improvements have become critical to factory performance. Manufacturers are using Digital Twins of production lines, connected to the physical assets through sensors to monitor and model machine conditions in real time, predict machine failures and run multiple simulations to test lines under different conditions, workloads and maintenance and production schedules. Some manufacturers have taken this strategy one step further—and developed Digital Twins of entire factories.

 

  • Construction: Some builders are using Digital Twins to assist in planning and monitoring projects in real time. With Digital Twins, managers can derive a detailed view of an entire project, incorporating building design, site operations and other factors that can be difficult to get a big-picture view of from the ground. Through predictive modeling of the site and construction, Digital Twins can help reduce errors and cost overruns, make materials handling more efficient, and even enhance site safety.

 

  • Healthcare: With Digital Twin Technology, healthcare professionals can develop a dynamic mirror of a patient, incorporating historical data, sensor-derived real-time data from wearables and medical devices, and other factors such as demographics, genomics and lifestyle. As a result, Digital Twins can help achieve a far more personalized level of patient care and more accurate and actionable diagnoses, while enabling robust data-based decision-making and prognoses by modeling clinical outcomes.

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