How Decathlon runs demand forecasting at scale with Chronos-2

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This post is co-written with Vianney Bruned, Filippo Giruzzi, Belkiss Saidi, and Carlos Ramirez from Decathlon. Decathlon is one of the world’s largest sporting goods retailers, with more than 100,000 teammates and 400 million users worldwide. The company relies on accurate demand forecasting at scale to support the availability of the appropriate products in each store at the time customers need them. After evaluating multiple time series foundation models (TSFMs), Decathlon selected Chronos-2 as a core component of their forecasting stack. In this post, we share the architecture Decathlon uses to run Chronos-2 at scale on AWS, the business impact on Decathlon’s supply chain operations, and practical lessons learned for other companies that want to adopt foundation models for forecasting. Decathlon’s forecasting challenge Accurate demand forecasting is the backbone of retail supply chain operations. For Decathlon, this challenge is amplified by the sheer scale and diversity of their business: tens of thousands of products spanning over 80 sports, sold across multiple continents with highly seasonal demand patterns. A pair of ski gloves and a surfboard have fundamentally different demand signals, yet both must be forecasted accurately to avoid stockouts or overstock. Decathlon’s forecasting system predicts the weekly sales quantity of all products on two critical horizons. The first is a 12-week replenishment window used by purchase planners to order goods from industrial partners. The second is a 52-week strategic horizon for long-term stock projection and capacity …

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