Abstract: Examples provide a system and method for predicting future wastage of perishable goods received at a distribution center based on shelf life of each type of perishable item and age of arrival (AoA) when received. The system implements a ML model to predict the wastage for a given item or batch of items based on item-specific data, supplier data, transit data, etc. The system estimates quantity of throws at a DC level and/or a store level to reduce future wastage and ensure quality of perishable items received at the DC and stores. The system generates anomalous shipment alerts after quality control inspections at user-configurable intervals enabling users to take action to mitigate wastage. The predictions are used to determine whether to recommend rejection of a shipment and return to a supplier or acceptance of the shipment with possible additional actions to mitigate the predicted future wastage.