Patent ID: 11922442
Assignee: BLUE YONDER GROUP, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 14:
15. A non-transitory computer-readable medium embodied with software for explainable supervised machine learning cyclic boosting for prediction and explanation of future customer demand quantities, wherein the software, when executed:
receives historical sales data comprising aggregated sales data for one or more items from one or more stores for at least one past time period, wherein the one or more stores maintain an inventory of the one or more items at one or more stocking locations;
bins categorical features from the historical sales data according to feature categories;
bins continuous features from the historical sales data;
trains a cyclic boosting model to learn model parameters by iteratively calculating, for each feature and each bin, one or more factors for at least one full feature cycle;
wherein a training period is earlier than a prediction period, and each of one or more demand quantities is associated with at least one item of the one or more items and at least one stocking location of the one or more stocking locations during the prediction period, and
wherein trains the cyclic boosting model to learn the model parameters by iteratively calculating, for each feature and each bin, the one or more factors for the at least one full feature cycle, comprises:
calculates, for each of the one or more factors, partial factors for target values based on an iteratively calculated ratio of an actual value of a target for each feature and each bin to a predicted value of the target for each feature and each bin;
calculates aggregated factors based on the calculated partial factors; and
predicts the one or more demand quantities during the prediction period by training a prediction model on historical supply chain data; and

renders, for display on a user interface, a demand prediction feature explanation visualization comprising a predicted demand and one or more features identified during the training of the prediction model that influence the predicted demand.