Dibyajyoti Banerjee

US Patent:

20200143313, May 7, 2020

Filed:

Jul 9, 2019

Appl. No.:

16/506672

Inventors:

- Redwood City CA, US
Gowtham Bellala - Redwood City CA, US
Dibyajyoti Banerjee - Santa Clara CA, US
Nikhil Krishnan - San Carlos CA, US

International Classification:

G06Q 10/08
G06N 20/00

Abstract:

The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, and/or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed inventory dataset to an optimization algorithm. The optimization algorithm can be used to predict a target inventory level for optimizing an inventory holding cost. The optimization algorithm can comprise one or more constraint conditions.