How Walmart Rebuilt Its Supply Chain with Real-Time Data

Episode notes

Walmart's supply chain is the largest in the world, moving over 5 billion units annually. In this episode, Lucas and Luna explore how Walmart rebuilt its supply chain on real-time data from edge to shelf. They break down the 2019 shift from batch processing to streaming events, the use of Kafka at massive scale, and how a machine learning model called 'Eddie' predicts demand by the hour. Lucas explains why Walmart moved its inventory management from mainframes to a cloud-native architecture built on Google Cloud, and how real-time visibility reduced out-of-stocks by 16% in pilot stores. The conversation covers the technical trade-offs - why they chose Apache Beam for processing, how they handle data locality across 4,700 stores, and what happens when a hurricane disrupts the supply graph. Luna pushes back on whether smaller retailers can replicate this, and Lucas outlines the core principles that transfer. This is a deep, non-obvious look at how the world's largest retailer treats logistics as a data platform.

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