AI based inventory management for your eCommerce Business
There is no single best forecasting model. Demand shapes differ by SKU. We run a competition, validate on actuals, and force outputs into decisions you can execute
Request DemoHoldout validation
Models tested on unseen data before selection
Bias tracked
Systematic over/under-forecasting measured per model
Stability scored
Forecast consistency across re-estimation cycles
| Model | MAE (holdout) | Bias | Stability | Notes |
|---|---|---|---|---|
| SARIMA Winner | Low | Low | High | Best holdout performance |
| Holt-Winters | Med | Med | Med | — |
| Prophet | Med | High | Low | — |
When SKU behavior changes, the system can switch winners
Each cycle we re-validate on what happened
If a SKU changes behavior, the system adapts
The goal is fewer systematic planning errors over time
Quantity and timing recommendations that respect lead times and constraints
Transfer recommendations when stock is in the wrong place
Clear, bundle, or de-prioritize to release cash and reduce risk
Constraints handled in the logic engine