Use case
AI demand forecasting that corrects for stockouts and promotions
When a product stocks out, its sales history shows what you sold, not what you could have sold. Flieber corrects history for stockouts, spikes and promotions before forecasting each product on each channel. 36% more accurate than our previous portfolio of 16 forecasting models, tested on a random sample of 46,000 products. Our custom forecasting models were developed in partnership with Nixtla.
Examples
Ask Flieber
- “Review my forecast every week and flag products where it looks wrong: new products with little history, products affected by stockouts, sudden accelerations, discontinued products still showing demand and products where actual sales are consistently above or below forecast.”
- “What did stockouts cost us last month?”
Questions
Questions
How accurate are Flieber’s forecasts?
36% more accurate than our previous portfolio of 16 forecasting models, tested on a random sample of 46,000 products.
Can I adjust the forecast?
Yes, at SKU, store, channel or group level, including planned promotions.
Next step