SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED IDENTIFICATION OF DYNAMIC RESOURCE REORDERING POINTS

• Main Authors: Koehn, Rebekah, Garcia, Pilar, Siemens, Allen, Liang, Zhaofeng, Antonino, Gregorio, Erukala, Arpana, Post, Christian Howard
• Format: Patent
• Language: English
• Published: 18.01.2024
• Subjects: CALCULATING; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

Systems and methods for machine learning-based identification of dynamic resource reordering points are disclosed. In one embodiment, a method may include a resource management computer program executed on an electronic device: (1) receiving historical resource availability data, historical resource consumption data, and historical resource reordering data for a resource; (2) training a machine learning engine to predict dynamic resource reordering points for the resource using the historical resource availability data, the historical resource consumption data, and the historical resource reordering data; (3) receiving current resource availability data, current resource demand data, and current resource reordering data for the resource; (4) predicting a dynamic resource reordering point for the resource based on the current resource availability data, the current resource demand data, and/or the current resource reordering data; and (5) requesting additional resources in response to threshold for the dynamic resource reordering point being met.