公开(公告)号：US20230297948A1

公开(公告)日：2023-09-21

申请号：US17698666

申请日：2022-03-18

Applicant: Oracle International Corporation

Inventor： Setareh BORJIAN ,  Su-Ming WU ,  Shenghao WANG

IPC: G06Q10/08 ,  G06Q30/06

CPC classification number: G06Q10/087 ,  G06Q30/0605

Abstract: Embodiments predict supply chain policies using machine learning. A machine learning model trained to predict one or more supply chain metrics for a first product can be stored. The machine learning model can generate a plurality of supply chain metric predictions for the first product using a plurality of candidate replenishment policies for the first product. A candidate replenishment policy with a corresponding supply chain metric prediction that meets a criteria can be selected. The selected replenishment policy can be implemented for the first product within an inventory system, where one or more physical locations are restocked with the first product based on restocking parameters defined by the selected replenishment policy.

公开(公告)号：US20230096633A1

公开(公告)日：2023-03-30

申请号：US17449112

申请日：2021-09-28

Applicant: Oracle International Corporation ,  MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Inventor： Leann THAYAPARAN ,  Kiran V. PANCHAMGAM ,  Setareh BORJIAN ,  Georgia PERAKIS

IPC: G06Q10/06 ,  G06N20/20 ,  G06K9/62 ,  G06Q10/08

Abstract: Embodiments generate an optimized demand model for a retail item. Embodiments train a tree ensemble machine learning model comprising a plurality of trees, the training comprising storing upper bounds for each of the trees, the trees comprising levels and branches that correspond to the demand features that influence demand for the item. Embodiments generate an objective function for the demand model. At a top split of each tree, embodiments determine optimal child nodes using the stored upper bounds and calculate a new feasible region for each tree. Using bounds on the new feasible region, embodiments move down each tree to a next level of splits and generate the optimized demand model when a leaf node of every tree has been reached.

公开(公告)号：US20240303577A1

公开(公告)日：2024-09-12

申请号：US18666037

申请日：2024-05-16

Applicant: Oracle International Corporation ,  MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Inventor： Leann THAYAPARAN ,  Kiran V. PANCHAMGAM ,  Setareh BORJIAN ,  Georgia PERAKIS

IPC: G06Q10/067 ,  G06F18/214 ,  G06N20/20 ,  G06Q10/08 ,  G06Q10/087

CPC classification number: G06Q10/067 ,  G06F18/2148 ,  G06N20/20 ,  G06Q10/08 ,  G06Q10/087

Abstract: Embodiments generate and train an optimized demand model for predicting a demand of an item. Embodiments receive a plurality of trees, each of the plurality of trees including one or more levels of splits and a plurality of nodes, each of the plurality of nodes corresponding to a demand feature that influences demand for the item. Embodiments store a first bound as a current bound for each of the plurality of trees. Starting at a top split of each of the plurality of trees, embodiments select a first demand feature that a greatest number of the plurality of trees split on. Embodiments optimize the first demand feature using the stored current bound to generate a second bound. Embodiments store the second bound as the current bound for each of the plurality of trees and move down each of the plurality of trees to a next level of splits.