System and Method to Predict Service Level Failure in Supply Chains

A method to predict service failures in a supply chain using machine learning, comprising:

• receiving, by a computer comprising a processor and a memory, historical supply chain data from an archiving system, the archiving system storing historical supply chain data from a supply chain network comprising one or more supply chain entities;
• preparing, by the computer, the historical supply chain data for a prediction problem, wherein the prediction problem comprises a classification problem;
• training, by the computer, a prediction model to solve the prediction problem based on, at least part, of the prepared historical supply chain data;
• predicting, by the computer, whether one or more supply chain events will occur during a prediction horizon, the one or more supply chain events associated with at least one supply chain entity of the one or more supply chain entities;
• calculating, by the computer, precision and recall scores for the prediction model, wherein the precision scores indicate a proportion of predicted supply chain events that actually occur, and wherein the recall scores indicate a proportion of supply chain events that occur will be predicted;
• generating, by the computer, a master visualization dashboard comprising one or more alerts for the predicted one or more supply chain events and further comprising a model performance visualization;
• receiving, by the computer, a selection of an interactive element of the master visualization dashboard to select or input filters to not display one or more alerts based on a criteria of the selected or input filter;
• and
• providing, by the computer, one or more tools for initiating one or more corrective actions to be undertaken in order to resolve one or more underlying causes of the displayed one or more alerts for the predicted one or more supply chain events.

A non-transitory computer-readable medium embodied with software to predict service failures in a supply chain using machine learning, the software when executed configured to:

• receive historical supply chain data from an archiving system, the archiving system storing historical supply chain data from a supply chain network comprising one or more supply chain entities;
• prepare the historical supply chain data for a prediction problem, wherein the prediction problem comprises a classification problem;
• train a prediction model to solve the prediction problem based on, at least part, of the prepared historical supply chain data;
• predict whether one or more supply chain events will occur during a prediction horizon, the one or more supply chain events associated with at least one supply chain entity of the one or more supply chain entities;
• calculate precision and recall scores for the prediction model, wherein the precision scores indicate a proportion of predicted supply chain events that actually occur, and wherein the recall scores indicate a proportion of supply chain events that occur will be predicted;
• generate a master visualization dashboard comprising one or more alerts for the predicted one or more supply chain events and further comprising a model performance visualization;
• receive a selection of an interactive element of the master visualization dashboard to select or input filters to not display one or more alerts based on a criteria of the selected or input filter;
• and
• provide one or more tools for initiating one or more corrective actions to be undertaken in order to resolve one or more underlying causes of the displayed one or more alerts for the predicted one or more supply chain events.