This blog post is the second in a three-part series on digital twins. The first post explained how generative AI and digital twins make a powerful pairing. The third post in the series will explore an in-depth case study on how generative AI and digital twins could be used to enhance customer experience.

When most people consider digital twins, they think of the visualization and simulation tools used to develop innovative new products. Offshore oil platforms, racing sailboats, luxury automobile engines, hotels, and train stations are just some of the things that have been designed and refined using advanced digital simulations.

These uses represent a fraction of what digital twins can accomplish. It is helpful to think of digital twins less as a tool for designers, engineers, and manufacturers and more as a laboratory in which nearly any organization can optimize its most precious resource—information—to continually push the boundaries of what it can accomplish. By using data to mirror real-world situations, digital twins can be deployed to create, fine-tune, or entirely reimagine nearly any complex process or system, including supply chains, public transit systems, and assembly lines.

For example, a global retailer recently set out to rethink its supply chain with an eye toward cutting costs, optimizing service, and boosting sustainability. It was a complex problem that involved optimizing an array of key levers, such as inventory positioning, product flow optimization, supply planning, and carbon emissions. Drawing on the organization’s vast quantities of real-time data, a team created a digital twin of its global supply chain operations—a sprawling system of manufacturing facilities, freight and cargo operations, third-party contractors, and distribution centers. The digital replica allowed the retailer to test more than 50 scenarios a day, examining potential outcomes for various large and small choices along the supply chain, all without any real-life disruptions. An optimization engine embedded within the digital twin provided users with informed recommendations in the meantime. Ultimately, the company made a series of optimized decisions that sparked a 7 percent reduction in carbon emissions and a 5 percent improvement in customer orders received on time.