Strategic and tactical intelligence in dynamic segmentation

A computer-implemented method for autonomously performing tactical segmentation, comprising:

• performing, by a computer comprising a memory and a processor, the computer connected to a database, multi-dimension segmentation on input data by computing feature importance to generate multi-dimensional segments;
• assigning, by the computer, policy parameters to the supply chain network based on the generated multi-dimensional segments;
• training, by the computer, a machine learning model by applying a cyclic boosting process to the standardized features data, wherein the cyclic boosting process iteratively learns relationships associated with the generated multi-dimensional segments;
• store the machine learning model in the database;
• performing, by the computer, multi-dimension segmentation based on the stored machine learning model;
• determining autonomously, by the computer, whether data drift has occurred in the input data;
• and
• in response to autonomously determining that data drift has occurred, repeating, by the computer, the performing, assigning and training steps, and storing an updated machine learning model in the database.

A non-transitory computer-readable medium embodied with software for performing strategic segmentation, the software when executed using one or more computers is autonomously configured to:

• perform multi-dimension segmentation on input data by computing feature importance to generate multi-dimensional segments;
• assign policy parameters to the supply chain network based on the generated multi-dimensional segments;
• train a machine learning model by applying a cyclic boosting process to the standardized features data, wherein the cyclic boosting process iteratively learns relationships associated with the generated multi-dimensional segments store the machine learning model in the database;
• perform multi-dimension segmentation based on the stored machine learning model;
• determine whether data drift has occurred in the input data;
• and
• in response to determining that data drift has occurred, repeat the perform, assign, and train steps, and store an updated machine learning model in the database.