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Covid-19 shed light on many networks of the world, Internet to international air travel. But supply chains around the world—ships, trucks, and trains that connect factories to ports and warehouses, bringing nearly everything we buy thousands of miles from where it’s produced to where it’s consumed—are facing more scrutiny than ever before.

“Whatever you’re selling, it’s fair to say you have a problem right now,” says Jason Boyce, founder and CEO of Avenue7Media, a consulting firm that advises Amazon’s top sellers. Boyce says they have customers who would roll over tens of millions of dollars a year if they could stay in stock. “We have conversations with clients every day where they just cry,” he says. “For months, they weren’t exactly out of stock for a 30-day period in a row.”

Digital twins try to solve supply chain breaks by predicting them before they happen and then using artificial intelligence to find a workaround. The name captures the main idea of ​​simulating a complex system on a computer, creating a kind of twin that reflects real-world objects, from ports to products, and the processes they are part of. Simulations have been a part of decision making in the industry for several years, helping people explore different product designs or organize a warehouse layout. But the availability of massive amounts of real-time data and computing power means that more complex processes can be simulated for the first time, including the chaos of global supply chains that often rely on multiple vendors and transportation networks.

This type of technology has given Amazon an extra edge for years, which already has the advantage of controlling its own trucks and warehouses. Now others are adopting it as well. Google is developing its supply chain digital twins, which automaker Renault announced in September. International shipping giants like FedEx and DHL create their own simulation software. And AI firms like Pathmind are creating bespoke tools for anyone who can pay for them. However, not everyone will benefit. In fact, powerful new technology could widen a growing digital divide in the global economy.

storm weather

It’s easy to blame the pandemic for current supply chain problems. Factory closures and labor shortage A leap in online shopping and convenience purchasing has rapidly increased demand for home delivery, while also knocking down manufacturing and distribution centers.

But in reality, the epidemic only made a bad situation worse. “There are global forces driving this, it all comes together into a perfect storm,” says D’Maris Coffman, an economist at University College London who studies the impact of the pandemic on supply chains.

Taming this storm will require sinking trillions of dollars in global infrastructure, expanding ports and delivery fleets, and investing in better management, better working conditions and better trade deals. “Technology is not going to solve these problems. It won’t allow ships to carry more containers,” said David Simchi-Levi, who runs the data science lab at the Massachusetts Institute of Technology and has helped create digital twins for several large companies. But AI can help companies survive the worst. “Digital twins allow us to identify problems before they arise,” she says.

According to Hans Thalbauer, general manager of Google’s supply chains and logistics team, the biggest challenge businesses face is the inability to predict events in the chain. “It doesn’t matter which company you talk to,” he says. “Anyone in the supply chain world will tell you they don’t have the visibility they need to make decisions”

For example, it’s supply chain visibility that allows Amazon to predict when a product will arrive at your doorstep. It gives an accurate estimate of when it will arrive for each item Amazon self-delivers—and that includes the millions it delivers on behalf of third-party vendors like Boyce and its customers. It may not seem like much, Boyce says, but if Amazon got those estimates wrong, it would start losing customers, especially during the holiday season when people buy last-minute gifts and rely on Amazon to deliver them. “It takes enormous computing power to show this simple little delivery day,” he says. “But people go crazy when they don’t get their stuff on time.”

According to Deliverr, a US company that manages delivery logistics for multiple e-commerce firms including Amazon, Walmart, eBay, and Shopify, an estimated delivery time of two days versus seven to 10 days drives sales by 40%; an estimated delivery time of one day increases sales by 70%.

It’s no surprise that others want a crystal ball of their own. Just-in-time supply chains are almost dead. Disruptions over the past two years have wrecked many businesses chasing hyper-efficiency to the extreme. Warehouse space is expensive, and paying to store inventory you won’t need for a week may seem overkill in times of plenty. But when next week’s stock doesn’t arrive, you have nothing to sell.

“Before the pandemic, most companies were focused on cutting costs,” says Simchi-Levi. They are now willing to pay for stamina, but focusing solely on stamina is also a mistake: you have to find the right balance between the two. This is the true power of simulations. “We are seeing an increasing number of companies starting to stress test their supply chains using digital twins,” he says.

Suppose?

By exploring different possible scenarios, companies can determine the balance between efficiency and flexibility that works best for them. Add in deep reinforcement learning, which allows an AI to learn through trial and error what actions to take in different situations, and digital twins become machines for exploring probabilistic questions. What if there is a drought in Taiwan and water shortages stop microchip production? The digital twin can predict the risk of this happening, monitor the impact on your supply chain, and recommend what actions should be taken to minimize harm using reinforcement learning.

If you’re a car manufacturer in the US Midwest, a digital twin may recommend purchasing extra components from a West Coast distributor that’s still more than enough. But put multiple scenarios together and things soon get very complicated. For example, according to Simchi-Levi, Ford has more than 50 factories worldwide that use 35 billion parts to produce 6 million cars and trucks each year. There are approximately 1,400 suppliers spread across 4,400 manufacturing sites with which it interacts directly, and a range of suppliers and supplier suppliers up to 10 times deep between Ford and the raw materials that go into their vehicles. Any of these links can break, and a good stress tester needs to examine each one.