ToolsAugust 28, 2026via AWS Machine Learning Blog
How Decathlon runs demand forecasting at scale with Chronos-2
Why it matters
A real deployment case study showing how practitioners can ship time-series ML at scale using open models and commodity hardware—practical ROI for operations teams considering similar workloads.
Key signals
- Decathlon: tens of thousands of products, multi-continent demand forecasting
- Model: Chronos-2 (time-series foundation model)
- Accuracy improvement: 11-15 percentage points
- Infrastructure: AWS CPU-only instances
- Cost: ~$0.03 per week for inference
- Frequency: weekly inference cycles
- Benefit: reduced operational complexity vs. legacy forecasting
- Decathlon: tens of thousands of SKUs, weekly forecasts, multiple continents
- Chronos-2 model deployed on AWS
- Weekly inference cost: ~$0.03 on CPU-only instances
- Operational complexity reduced
- Time-series forecasting use case (demand planning)
The hook
Decathlon cut forecast costs to $0.03/week per product line using Chronos-2 on CPU. 11-15 point accuracy gain without the GPU tax.
Decathlon, one of the world's largest sporting goods retailers, forecasts weekly demand for tens of thousands of products across multiple continents. Learn how they deployed Chronos-2 on AWS to improve forecast accuracy by 11-15 points while cutting operational complexity and running weekly inferenc…