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AI Retail Analytics Enhance Inventory Management

Retail analytics

Customers expect to walk into a store and find what they need. When that doesn’t happen—whether it’s due to rampant labor shortages or inefficient manual stocking processes—they will take their business elsewhere.

But retailers cannot afford to lose customers due to out-of-stock or misplaced items. A recent report found that worldwide, this can result in as much as a $984 billion loss in sales every year. The problem, though, is that many retailers still rely on manual processes to keep shelves stocked. That’s why more and more have started to turn to AI retail analytics solutions to streamline their inventory management practices.

Optimizing Customer Experience with AI Retail Analytics

Take the UK-based retail grocery chain Nisa, for example. Nisa found that not having items properly stocked was having a negative effect on their customer experience. To improve the situation, they turned to Shelfie, a retail analytics platform provider that relies on cloud-based software, to improve their processes and better understand the movement of stock.

With Shelfie, Nisa can take photos with connected cameras in its stores and compare the current stock against a predetermined chart of what each monitored shelf should look like.

To accomplish this, the cameras take video images and transfer them to the cloud, where an advanced machine learning and image processing algorithm analyzes data about stock placement and availability. When an item is running low or products are out of place, staff members receive a real-time alert via their dashboard or mobile app. Alerts can come to a barcode scanner, tablet, or other connected device.

“#Retailers have so much on their hands, and every day brings new challenges. This solution can provide the #data and insights they need to stay ahead and know what to prioritize” – Yehia Oweiss, Shelfie via @insightdottech

In a trial at one of the Nisa stores, Shelfie was able to keep stock availability at approximately 95%. “It provides all the data that I need as a retailer to make decisions about buying, forecasting, and optimizing the positioning of goods within a store,” says Rav Garcha, Owner and Operator of several Nisa locations.

Making Human Efforts More Efficient

With the rapid growth of technology in every industry, it’s surprising to find that most retailers still rely on a human to walk through the store, look at the shelves to see what’s available and what’s running low—and then go into the warehouse or back storage to fill a trolley and replenish the stock, according to Yehia Oweiss, CEO of Shelfie.

Shelfie was developed to ease the burden on human retail workers, making it easy to monitor shelves and gain meaningful insights. “This solution doesn’t replace store personnel—it’s designed to make their jobs more efficient,” Oweiss explains. “Our software will tell you the time and the day you are most out of stock and on which shelf. Now if you have that data to hand, you can deploy the person who’s in charge of replenishing the stock more efficiently.”

Video 1. Shelfie consists of an analytics platform, and image capture device and a reporting dashboard to provide insights into shelf inventory management. (Source: Shelfie)

While similar solutions are complex or costly to scale, Shelfie is simple to deploy and easy to expand. The solution handles all the analytics and alerts store operators of any out-of-stock or low items.

“All you need is a camera pointing at the shelf, connected to the internet,” explains Oweiss. “We do the rest remotely in the cloud.”

The solution was also developed to be camera-agnostic, enabling retailers to use existing security cameras as long as they can connect to the internet. In addition, there is an option to set up the platform on-premises if needed.

To implement AI models capable of detecting what’s happening on the retail floor, Shelfie utilizes the OpenVINO AI toolkit. When a new customer decides to adopt the solution, they provide photos and information about what each shelf should look like, kicking off a two-week training process for the AI neural engine.

AI Retail Solution Addresses More Store Needs Over Time

As the AI software learns the location of various items and tracks their sales, Shelfie can provide many additional insights beyond basic out-of-stock or misplaced item alerts.

For example, dashboards display data about which SKUs are highest- and lowest-selling and which are most often out of stock. Managers can see how long an item has been out of stock and what time of day certain items tend to sell the most and least as well as what are the top-selling items they need to ensure are never out of stock. Granular data like this would be very difficult—if not impossible—to gather using manual processes, and can provide important insights to help optimize sales and stocking efforts, according to Oweiss.

“At the end of the day, what Shelfie does is improve business processes,” he explains. “It has a very good effect on customer satisfaction. Retailers have so much on their hands, and every day brings new challenges. This solution can provide the data and insights they need to stay ahead and know what to prioritize.”

The solution’s use cases extend beyond the retail realm. For example, Oweiss says Shelfie is being introduced in gas stations to monitor spillages, count customers, and maintain regulatory compliance. The company is also implementing the solution in the oil and gas industry to monitor oil heads for leakage and spillage. “Every use case is about business process efficiency,” Oweiss says. “When you have key data at hand, you can deploy humans more efficiently and maximize their effectiveness.”

As use of retail analytics expands, Oweiss sees augmented reality and AI playing ever-increasing roles in future solutions. For now, while large chains and companies with many locations often struggle with the change management needed to succeed with a new solution, smaller companies and independent retailers are adopting solutions like Shelfie with ease.

“For them, flexibility and cost savings are advantages,” says Oweiss. “Our solution doesn’t require specific camera models or high-definition images, so they can get up and running quickly with the technology they already have in place.”

This article was edited by Christina Cardoza, Associate Editorial Director for

About the Author

Jessica Leigh Brown is a writer focused on applications of IoT and emerging technologies in education. As a freelance journalist, her work has appeared in more than a dozen trade and consumer magazines, and she enjoys working with top technology companies to create content such as white papers and case studies.

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