How Retailers Predict Demand Without Guesswork

Retail inventory waste costs billions annually, yet many companies still rely on gut feel for restocking. This episode examines how Walmart uses real-time point-of-sale data and machine learning to predict local demand with remarkable precision. We break down the specific algorithmic adjustments that turn raw transaction logs into shelf-ready forecasts, reducing spoilage in perishables by nearly fifteen percent. Lucas and Luna discuss the friction between centralized planning and store-level reality, exploring why a one-size-fits-all model fails in diverse neighborhoods. You will learn how feature engineering captures weather patterns, local events, and even competitor promotions to refine these predictions. It is a deep dive into the mechanics of supply chain intelligence, showing how data teams move beyond historical averages to anticipate the next shift in consumer behavior. #Walmart #SupplyChainAI #DemandForecasting #RetailTech #MachineLearning #DataScience #InventoryManagement #Perishables #FeatureEngineering #PointOfSale #FexingoBusiness #BusinessPodcast #TechTrends2026 #OperationsResearch #ConsumerBehavior #Logistics #PredictiveAnalytics #DataDrivenDecisions Keep every episode free: buymeacoffee.com/fexingo