METHODS AND SYSTEM FOR ADJUSTING AVAILABLE INVENTORIES AND FOR ALLOCATION OF ORDERS BASED ON PREDICTED ITEM UNAVAILABILITY

原文（中文）

Disclosed herein are systems and methods for predicting the probability of an inventory-not-found occurrence during fulfillment of an order, and for utilizing that predicted probability to improve order allocation applications and inventory systems. The disclosed systems and methods utilize machine learning predictive models to determine and score fulfillment performance of individual nodes by predicting their likelihood of an inventory-not-found event. At least some data received at the machine learning predictive application may be realtime data to reflect current conditions. In some examples, some data received at the machine learning application may be pre-computed data. The machine learning models generated by training at the machine learning application may include individual models used for different item categories. In some examples, a threshold suppression amount is utilized to adjust an amount of available inventory utilized to provide item availability (i.e. availability to place an order online) to a customer.