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How AI Could Solve Supply Chain Shortages and Save Christmas


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A wind turbine (left) and its digital twin (right).

Digital twins seek to solve breakages in the supply chain by anticipating them before they happen and then using artificial intelligence to figure out a workaround.

Credit: twi-global.com

With the supply-chain disruptions of the past two years showing no signs of easing anytime soon, businesses are turning to a new generation of AI-powered simulations called digital twins to help them get goods and services to customers on time. These tools not only predict disruptions down the line, but suggest what to do about it. Desperate companies struggling with the collapse of just-in-time shipping are using them to find a crucial balance between efficiency and resilience. 

The list of things that have been hard to get hold of at one time or another in the last few months is as varied as it is long: new cars, new phones, contact lenses, cleaning products, fresh produce, garden furniture, books, the color blue. "It's not like when everyone ran out of toilet paper in March 2020," says Chris Nicholson, founder of Pathmind, a company that applies AI to logistics problems. "This time the missing items feel personalized."

Covid-19 has shined a spotlight on many of the world's networks, from the internet to international air travel. But the supply chains that crisscross the world—the ships and trucks and trains that link factories to ports and warehouses, bringing almost everything we buy many thousands of miles from where it's produced to where it's consumed—are facing more scrutiny than they ever have.

"It's fair to say that whatever you're selling, you've got a problem right now," says Jason Boyce, founder and CEO of Avenue7Media, a consulting firm that advises top Amazon sellers. Boyce says he has clients who would be turning over tens of millions of dollars a year if they could stay in stock. "We're having talks with clients every day where they're just crying," he says. "For months, they haven't been fully in stock for one 30-day period in a row."

From MIT Technology Review
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