How does Flowlity handle returns forecasting?

Flowlity calculates return rate and return delay directly from historical sales-and-returns data, at the SKU, product family, or channel level depending on where the variance actually lives, rather than applying one blanket assumption across a catalogue. That returns forecast is then treated as its own inbound supply source inside the platform's replenishment logic, alongside external purchase orders, so that expected returns reduce what actually needs to be ordered from a supplier rather than sitting invisibly inside a padded safety stock number. The same AI-driven inventory optimization engine that powers demand forecasting and buffer sizing extends to this returns flow, which means planners see one reconciled view instead of maintaining a separate spreadsheet to guess at returns by hand. For circulation-heavy business models, including rental and subscription services where inbound returns are a permanent part of the operating cycle rather than an exception, that same logic scales from a percentage-of-sales adjustment to a first-class forecasting input.