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Machine-Learning Offers Significant Benefits For Grocery Retailers

December 1, 2016: 12:00 AM EST
Fresh foods account for about 40 percent of a grocery retailer’s revenue, and 33 percent of cost-of-goods sold. So it is imperative that stores have neither an excess or shortage of fresh food in stock. It’s a complex problem for a variety of reasons. But it can be mostly solved – with significant financial and logistical benefits – using machine-learning. The technology is based on algorithms that allow computers to “learn” from data while significantly boosting forecast and order accuracy. Some retailers using machine-learning have reduced out-of-stock rates by as much as 80 percent and cut write-offs and days of inventory on hand by 10 percent. They have also seen gross-margin increases of up to nine percent.
Christoph Glatzel et al., "The Secret to Smarter Fresh-Food Replenishment? Machine Learning", McKinsey & Company, December 01, 2016, © McKinsey & Company
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