How One Retailer Eliminated Stockouts Using Machine Learning

The Operator's Manual with Fexingo: Daily Decisions, Tactical Wins, and Business Execution · May 26, 2026 · 8m · transcript

In this episode, Lucas and Luna dive into how the grocery chain Kroger used machine learning to solve one of retail's oldest problems: stockouts. Instead of guesswork and spreadsheets, Kroger deployed a demand forecasting system called 'Shelf Edge' that cut out-of-stock rates by 30% and saved millions. We break down the specific machine learning model they used — a gradient-boosted decision tree — and why it worked better than traditional methods. We also explore the hidden costs of empty shelves, the data requirements for such a system, and whether smaller retailers can copy…