Food’s Flying Off the Shelves: Focal Systems Brings AI to Grocery Stores
We’ve all chosen the self-checkout stand over the human cashier, thinking it’ll take less time.
But somehow, things take a terrible turn. The barcodes aren’t scanning, there’s a pop-up scolding you for not placing the product in the bagging area (though you did, of course), and an employee is coming over to fix the chaos.
It would’ve taken less time to go to the cashier.
Focal Systems is applying deep learning and computer vision to automate portions of retail stores to streamline operations and get customers in and out more efficiently, without the pitfalls of the traditional self-checkout.
CEO Francois Chaubard sat down with AI Podcast host Noah Kravitz to talk about how the company is changing retailers.
As labor costs increase, the traditional solution is twofold: automation and human staff reduction. But Chaubard explains that self-checkout systems don’t actually compensate for fewer employees. Instead, “you get more out-of-stocks, because you’ve got less people,” he says.
Focal Systems started by applying AI to a different area of the store: shelves. Chaubard notes that, for store employees, one of the first tasks every day is checking what items are out of stock, and “knowing that answer takes about four hours a day.”
To prevent this, Focal Systems installs small, inexpensive cameras throughout the store, with a focus on high-moving areas like the soda aisle. The cameras “take an image once every half hour” and produce a chart that notes either “in” or “out.”
“Every single hour that you don’t have a product on the shelf is lost sales,” Chaubard emphasizes. This aspect of Focal Systems alerts employees that they need to restock, and helps identify common “out-of-stock hours” so that stores can recognize the pattern and avoid it altogether.
This shelf camera system is already in 11 major retailers across the world.
The other component to Focal Systems is the Focal Scan. While barcode scanning takes three seconds an item, on average, Focal Systems installs a camera on top of the conveyor belt. “You’re just using deep learning and computer vision to detect amongst a hundred thousand different SKUs in 0.1 seconds with 99.9 percent precision recall,” Chaubard explains.
The cashier can just focus on bagging, reducing the total time of the transaction by 60 percent.
Chaubard thinks that the future holds even more automation, but only where it would be cheaper than human labor. “People are hard to beat in certain tasks,” he laughs.
Visit Focal Systems’ website for more information and to find videos of their technology in action.
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