How One Retailer Eliminated Stockouts Using Machine Learning

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 this playbook. If you've ever wondered how AI actually improves operations on the ground, this episode is for you. #Kroger #MachineLearning #Retail #Stockouts #SupplyChain #DemandForecasting #ShelfEdge #GradientBoosting #AI #Operations #BusinessExecution #BusinessPodcast #FexingoBusiness #TheOperatorsManual #LucasAndLuna #DataScience #RetailTech #InventoryManagement Keep every episode free: buymeacoffee.com/fexingo