Blue Yonder Group, Inc.

Phani Bulusu

Rashid Puthiyapurayil

Vidhi Chugh

Tushar Shekhar

Ganesh Muthusamy

Abhishek Singh

Narayan Nandeda

Vishal Shinde

Deb Mohanty

Embodiments of the following disclosure provide a feature importance system and method to identify features relevant to determining whether a target variable will achieve a particular value. One example method includes generating a probabilistic graphical model to represent the performance of one or more entities in a supply chain and selecting or more target variables. The method further includes collating a list of features pertaining to the one or more selected target variables, pruning at least one of the one or more features from the list and generating one or more bins in which to distribute the one or more features in the list. The method further includes modeling a network graph incorporating the one or more features in the list and bins and determining one or more inferences pertaining to the one or more supply chain entity target variables.

Class level feature importance using lasso and probabilistic graphical models

A system and method are disclosed for applying machine learning to identify anomalous supply chain data that generates a probabilistic graphical model based on training data from historical attributes of a supply chain comprising supply chain entities to represent the performance of the supply chain entities in the supply chain, standardizes input features data related to the probabilistic graphical model, performs data anomaly detection within the probabilistic graphical model using one or more frequentist data anomaly detection algorithms, performs data anomaly detection within the probabilistic graphical model using one or more Bayesian data anomaly detection algorithms, combines according to one or more weighting methods, the data anomaly detection performed using one or more frequentist data anomaly detection algorithms with the data anomaly detection performed using one or more Bayesian data anomaly detection algorithms, and detects in response to the combining, an anomaly within the standardized input features data.

Bayesian and frequentist anomaly detection ensemble

A system and method for performing strategic segmentation including a supply chain network having a strategic segmentation planner, an inventory system, a transportation network and supply chain entities. The strategic segmentation planner includes a computer having a memory and a processor, that selects a workflow depth including an amount of data to analyze, discovers features by analyzing cleansed data to locate features which are characterized by features data, pre-processes the features data to standardize the features data, performs multi-dimension segmentation by computing feature importance to generate multi-dimensional segments, assigns policy parameters to the supply chain network based on the generated multi-dimensional segments, and trains 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.

Strategic and tactical intelligence in dynamic segmentation

A system and method for performing tactical segmentation including a supply chain network having a tactical segmentation planner, an inventory system, a transportation network, supply chain entities and a computer. The computer performs multi-dimension segmentation on input data by computing feature importance to generate multi-dimensional segments, assigns policy parameters to the supply chain network based on the generated multi-dimensional segments, trains a machine learning model by applying a cyclic boosting process to the standardized features data, where the cyclic boosting process iteratively learns relationships associated with the generated multi-dimensional segments, stores the machine learning model in a database, performs multi-dimension segmentation based on the stored machine learning model, determines whether data drift has occurred in the input data and in response to determining that data drift has occurred, repeats the perform, assign, trains steps, and stores an updated machine learning model in the database.

A system and method are disclosed for training a probabilistic graphical model based on historical attributes of a supply chain to represent supply chain performance, selecting supply chain entity target variables, collating with the use of machine learning models, a list of features and classes pertaining to selected supply chain entity target variables, calculating first and second level features associated with the list of features and classes, generating supply chain predictions based on the trained probabilistic graphical model, where the supply chain predictions are based on test data, comparing the supply chain predictions to desired supply chain outputs to determine a delta distance, and generating resolution actions, to decrease the delta distance between the supply chain output predictions and the desired supply chain outputs.

Mutual information resolution recommendations and graphical visualizations using probabilistic graphical models

A system and method for efficiently determining the fair-share bands of a supply chain planning problem modeled as a multi-objective hierarchical linear programming problem include a processor and memory and are configured to model a supply chain planning problem as a multi-objective hierarchal linear programming problem, assign weights at each band of a fixed number of at least two bands, determine a direction of improved band values from a value of a Key Process Indicator (KPI) calculated from an expected demand and short quantities, wherein the expected demand and short quantities are calculated from the multi-objective hierarchical linear programming problem using a sample generated by Gibbs sampling of a conditional Gaussian Bayesian Network, and generate a supply chain plan.

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Bengaluru, India

Phani Bulusu