Blue Yonder Group, Inc.

Deb Mohanty

Tushar Shekhar

Vidhi Chugh

Narayan Nandeda

Devanand R

Manish Kumar

Rashid Puthiyapurayil

Phani Bulusu

A system and method for a risk management visualization system comprises a computer having a processor and memory and configured to model a supply chain network as a supply chain planning problem, one or more key process indicators (KPIs) of the supply chain planning problem is based, at least in part, on the one or more input variables, model an impact on the one or more KPIs from each of the one or more input variables at a selected confidence interval using a Bayesian optimization process, and display a visualization of a risk profile for the one or more KPIs, the visualization indicating a probability that an actual KPI value differs from a predicted KPI value.

System and method of cognitive risk management

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 a risk management visualization system having a computer comprising a processor and memory and configured to model a supply chain network as a supply chain planning problem, one or more key process indicators (KPIs) of the supply chain planning problem is based, at least in part, on the one or more input variables, model an impact on the one or more KPIs from each of the one or more input variables at a selected confidence interval using a Bayesian optimization process comprising an exploration phase and a learning phase, and display a visualization of the risk profile for the one or more KPIs, the visualization indicating a probability that an actual KPI value differs from a predicted KPI value.

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Bengaluru, India

Deb Mohanty