How AI could solve the supply chain shortage and save Christmas

Covid-19 shed the spotlight on many of the world ‘s networks, from The Internet for international air travel. But the supply chains that cross the world – ships, trucks and trains that connect factories to ports and warehouses, transporting almost everything we buy, many thousands of miles from where it is produced to where it is consumed – are upright. facing greater control than ever.

“It’s fair to say that whatever you’re selling, you’re having a problem right now,” said Jason Boyce, founder and CEO of Avenue7Media, a consulting firm that advises Amazon’s best sellers. Boyce says there are customers who would spin tens of millions of dollars a year if they could stay in stock. “Every day we talk to customers when they’re just crying,” he says. “For months, they haven’t been fully in stock for another 30 days.”

Digital twins seek to resolve supply chain outages by anticipating them before they occur, and then use AI to find a solution. The name captures the key idea of ​​simulating a complex system in a computer, creating a kind of twin that reflects real-world objects – from ports to products – and the processes of which they are part. Simulations have been part of industry decision-making for several years, helping people research different product designs or streamline warehouse layout. But the availability of large amounts of real-time data and computing power means that more complex processes can be simulated for the first time, including the chaos in global supply chains, which often rely on multiple suppliers and transport networks.

This type of technology has given Amazon, which already has the advantage of controlling its own trucks and warehouses, an added advantage for years. Now others accept it. Google is developing digital twins in the supply chain, which carmaker Renault said it began using in September. International shipping giants such as FedEx and DHL are building their own simulation software. And artificial intelligence companies like Pathmind are creating custom tools for anyone who can pay for them. Still, not everyone will benefit. In fact, powerful new technology can increase the growing digital divide in the global economy.

The time of the storm

It is easy to blame the pandemic for the current supply chain problems. Closing factories and labor shortage destroyed the centers of production and delivery at the same time as the leap in online shopping and comfortable shopping caused the demand for home supplies to increase.

But in reality, the pandemic only exacerbated the bad situation. “There are global forces driving this, all combined in a perfect storm,” said D’Maris Coffman, an economist at University College London who is studying the effects of the pandemic on supply chains.

Extinguishing this storm will require sinking trillions of dollars in global infrastructure, expanding ports and fleets to supply and invest in better governance, better working conditions and better trade deals. “Technology will not solve these problems. That won’t allow ships to carry more containers, “said David Simchi-Levy, who runs the data science lab at the Massachusetts Institute of Technology and has helped build digital twins for several large companies. But AI can help companies overcome the worst. “Digital twins allow us to identify problems before they happen,” he said.

According to Hans Talbauer, managing director of supply and logistics chains at Google, the biggest problem businesses face is the inability to predict upstream events. “It doesn’t matter which company you talk to,” he says. “Everyone in the supply chain world will tell you that they don’t have the visibility they need to make decisions.”

This is the visibility of the supply chain, which allows Amazon, for example, to predict when an item will appear on your doorstep. For each item that Amazon delivers on its own – and that includes the millions of items it delivers on behalf of third-party vendors such as Boise and its customers – it gives an accurate estimate of when it will arrive. It may not seem like much, Boyce says, but if Amazon messes up those predictions, it will start losing customers – especially around the holiday season, when people buy gifts at the last minute and trust Amazon to deliver them. “It takes a lot of computing power just to show this simple little delivery day,” he says. “But people go crazy when they don’t get their things on time.”

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

Not surprisingly, others want their own crystal ball. Supply chains are almost dead just in time. Disruptions in the last two years have flooded many companies, which have pursued over-efficiency to the extreme. Warehouse space is expensive, and paying to store inventory that you may not need for a week can seem extravagant in times of abundance. But when next week’s shares don’t show up, you have nothing to sell.

“Before the pandemic, most companies focused on cutting costs,” said Simchi-Levy. Now they are willing to pay for sustainability, but focusing only on sustainability is also a mistake: you need to find the right balance between the two. This is the real power of simulations. “We are seeing a growing number of companies starting to test their supply chains using digital twins,” he said.

What if?

By exploring different possible scenarios, companies can identify the balance between efficiency and sustainability that works best for them. Add in-depth reinforced training that allows AI to learn by trial and error what to do in different situations, and digital twins become what-if machines. What if there is a drought in Taiwan and water shortages stop the production of microchips? A digital twin can anticipate the risk of this happening, track the impact it would have on your supply chain, and – using reinforcement learning – suggest what actions to take to minimize harm.

If you are a car manufacturer in the Midwestern United States, a digital twin may offer to purchase additional components from a West Coast distributor who still has a surplus. But put multiple scenarios together and things will soon become extremely complicated. For example, according to Simchi-Levy, Ford maintains more than 50 plants worldwide that use 35 billion parts to produce 6 million cars and trucks each year. There are about 1,400 suppliers distributed in 4,400 production sites with which it interacts directly, and a pile of suppliers and suppliers of up to 10 layers deep between Ford and the raw materials that go into its vehicles. Each of these connections can break and a good stress test will need to examine each of them.

Source link

Leave a Reply

Your email address will not be published.