GPU manufacturer Nvidia is expanding its enterprise software offering with three new AI workflows for retailers it hopes will also drive sales of its hardware accelerators.
The workflows are built on Nvidia’s existing AI technology platform. One tracks shoppers and objects across multiple camera views as a building block for cashierless store systems; one aims to prevent ticket-switching fraud at self-service checkouts; and one is for building analytics dashboards from surveillance camera video.
Nvidia isn’t packaging these workflows as off-the-shelf applications, however. Instead, it will make them available for enterprises to integrate themselves, or to buy as part of larger systems developed by startups or third-party systems integrators.
“There are several of them out there, globally, that have successfully developed these kinds of solutions, but we’re making it easier for more software companies and also system integrators to build these kinds of solutions,” said Azita Martin, Nvidia’s VP of retail.
She expects that demand for the software will drive sales of edge computing products containing Nvidia’s accelerator chips, as latency issues mean the algorithms for cashierless and self-checkout systems need to be running close to the checkout and not in some distant data center.
In addition to tracking who is carrying what items out of the store, the multiple camera system can also recognize when items have been put back on the wrong shelf, directing staff to reshelve them so that other customers can find them and stock outages are avoided, she said.
“We’re seeing huge adoption of frictionless shopping in Asia-Pacific and Europe, driven by shortage of labor,” said Martin.
Nvidia will face competition from Amazon in the cashierless store market, though, since while Amazon initially developed its Just Walk Out technology for use in its own Amazon Go and Amazon Fresh stores, it’s now offering it to third-party retailers, too. The first non-Amazon supermarket to use the company’s technology opened in Kansas City in December.
Assessing cost control
The tool to prevent ticket switching is intended to be integrated with camera-equipped self-service point-of-sale terminals, augmenting them with the ability to identify the product being scanned and verify it matches the barcode.
The cost of training the AI model to recognize these products went beyond the usual spending on computing capacity.
“We bought tens of thousands of dollars of products like steak and Tide and beer and razors, which are the most common items stolen, and we trained these algorithms,” said Martin.
Nvidia kept its grocery bill under control using its Omniverse simulation platform. “We didn’t buy every size of Tide and every packaging of beer,” she adds. “We took Omniverse and created synthetic data to train those algorithms even further for higher accuracy.”
Beer presents a particular challenge for the image recognition system, as it often sells in different-size multipacks or in special-edition packaging associated with events like the Super Bowl. However, the system continues to learn about new product formats and packaging from images captured at the checkout.
While implementation will be left up to retailers and their systems integrators, Martin suggested the tool might be used to lock up a point-of-sale terminal when ticket switching is suspected, summoning a member of staff to reset it and help the customer rescan their items.
Nvidia is touting high accuracy for its algorithms, but it remains to be seen how this will work out in deployment.
“These algorithms will deliver 98% accuracy in detecting theft and shutting down the point of sale and preventing it,” she said.
But that still leaves a 2% false positive rate, so CIOs will want to carefully monitor the potential impact on profitability, customer satisfaction, and frequent resets to prevent ticket switching.
A $100 billion problem
A 2022 survey by the National Retail Federation found that inventory shrink amounted to 1.44% of revenue — a relatively stable figure over the last decade — and in 2021, losses due to shrink totaled almost $100 billion, the NRF estimated.
Of that, survey respondents said 26% was due to process or control failures, 29% due to employee or internal theft, and 37% due to external theft.
But Nvidia suggests that its loss prevention technology could eliminate 30% of shrinkage. That, though, would mean it could prevent four-fifths of all external retail theft, even though in addition to ticket switching, that category also includes shoplifting and organized retail crime activities such as cargo theft, and the use of stolen or cloned credit cards to obtain merchandise.
Plus, potential gains must be weighed against the cost of deploying the technology, which, Martin says, “depends on the size of the store, the number of cameras and how many stores you deploy it to.”
More positively, Nvidia is also offering AI workflows that can process surveillance camera video feeds to generate a dashboard of retail analytics, including a heatmap of the most popular aisles and hour-by-hour trends in customer count and dwell time. “All of this is incredibly important in optimizing the merchandising, how the store is laid out, where the products go, and on what shelves to drive additional revenue,” Martin said.
Artificial Intelligence, IT Strategy, Retail Industry