How Retailers Predict Demand Without Guesswork
transcript
show notes
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
