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Getting fresh: How supermarkets are using AI to predict sales

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A supermarket deep in rural Germany had a problem. Far from the ample workforce of the nearest city, the shop was staffed largely by local teenagers still at school. The kids were motivated but inexperienced, says Avik Mukhija, co-founder of retail tech startup Freshflow. He declines to reveal the shop’s exact location.

When ordering fresh produce each day, the adolescent employees tended to over-stock for fear of running out of fruit and veg. That led to unnecessary waste as some unsold produce inevitably went bad in store.

But then the shop’s managers turned to a machine learning system from Freshflow that can make predictions about how much fresh food customers will buy in the coming days — and suggest what stock to bring in, accordingly. With this tool at hand, the amount of produce wasted at the supermarket dropped sharply, by nearly 30%.

“Those are the best waste reduction rates we’ve achieved so far,” says Mukhija. Freshflow currently employs 15 people and has raised €3 million. The company’s software is live at multiple shops owned by two regional grocery chains in Germany, and pilot deployments in France are also underway.

Fresh produce managers in supermarkets and small local shops have long had to decide for themselves how many apples, strawberries or potatoes they need to order, simply to keep up with demand. But demand is constantly in flux. In-store promotions, public holidays, the condition of stock already on shelves, and the weather — all these things can shape sales. The person ordering fresh stock needs to get their numbers right, every day.