Decathlon Deploys Chronos-2, Cuts 12-Week WAPE by 11–15 Points

Decathlon has put Amazon’s Chronos-2 time-series model into production for demand forecasting in Southeast Asia and Latin America, replacing a more frequently retrained approach with a model it fine-tunes every six months. The retailer says the change lowered 12-week weighted absolute percentage error, or WAPE, by 11 percentage points in Southeast Asia and 15 points in Latin America—an outcome that ties a foundation-model deployment to the immediate work of stocking stores.

Demand forecasts determine how much Decathlon orders from industrial partners over a 12-week replenishment window, while a 52-week view supports stock and capacity planning. Those jobs span supply zones and product categories with sharply different seasonal patterns, so the company needs one system to forecast weekly sales across large product catalogs rather than a one-size-fits-all estimate.

A local benchmark, not a leaderboard choice

The predecessor system had already evolved. From 2021 through 2024, Decathlon used DeepAR for shorter forecasts and Holt-Winters smoothing for later weeks. It then adopted a Temporal Fusion Transformer, or TFT, with covariates—known inputs such as price or store count—to improve long-horizon forecasts. But the system required weekly retraining and extra engineering work to reach new regions.

Rather than assume a pre-trained forecasting model would transfer to retail, Decathlon evaluated multiple time-series foundation models on its own data. The test used 101 rolling forecast cutoffs from late 2022 through late 2024, roughly 25,000 products at each cutoff, and 39,000 product time series across the full evaluation. It measured both 12- and 52-week horizons, using WAPE as the primary metric alongside error, bias, and pairwise winning measures.

Decathlon says fine-tuned Chronos-2 produced the lowest error among the models it tested at both horizons. Its zero-shot version, meaning it was used without domain-specific training, matched or exceeded the existing retrained production baseline in the company’s benchmark. Fine-tuning added further gains, even on a six-month schedule.

The operating model changes with the model

The contrast is not only accuracy. Chronos-2’s design can incorporate covariates natively through group attention, which exchanges information across related series while time attention follows each series over time. Decathlon uses Low-Rank Adaptation, or LoRA, to adjust the model without fully retraining it. Its production pipeline runs weekly batch inference, but fine-tunes only every six months and versions models separately for each supply zone.

That lighter update schedule is paired with a conventional production stack: PySpark prepares and delivers data, Databricks triggers jobs, Airflow orchestrates pipelines, and MLflow holds zone-specific model versions. The company says CPU inference took about 40 seconds for 7,000 Latin American series and about 75 seconds for 15,000 Southeast Asian series. Chronos-2 also met its benchmark requirement of completing inference for 25,000 products in under two minutes.

A production result with a defined boundary

At the longer 52-week horizon, Decathlon reports smaller but still material reductions: WAPE fell from 44% to 38% in Southeast Asia and from 55% to 46% in Latin America. The company also estimates that every one-point 12-week WAPE improvement corresponds, on average across zones, to 0.3 days of inventory savings, 0.3 points of product availability, and roughly 0.12 points of sales. Those business translations are company-supplied relationships, not independently reported financial results.

Decathlon says a new regional deployment now takes two to three months with a data scientist, ML engineer, and data analyst, compared with about six months and three people under TFT. The company plans a full multi-zone rollout during 2026, with the Middle East and Africa next. That expansion is the consequential test: the published accuracy and operating gains currently come only from its Southeast Asia and Latin America supply zones.