US 12,260,370 B1
System to predict service level failure in supply chains
Gabrielle Gauthier Melancon, Montréal (CA); Philippe Grangier, Montréal (CA); Eric Prescott-Gagnon, Montréal (CA); and Emmanuel Sabourin, Uden (NL)
Assigned to Blue Yonder Group, Inc., Scottsdale, AZ (US)
Filed by JDA Software Group, Inc., Scottsdale, AZ (US)
Filed on Nov. 16, 2018, as Appl. No. 16/193,547.
Int. Cl. G06Q 10/083 (2024.01); G06F 17/18 (2006.01); G06N 20/00 (2019.01); G06Q 10/0635 (2023.01)
CPC G06Q 10/0838 (2013.01) [G06F 17/18 (2013.01); G06N 20/00 (2019.01); G06Q 10/0635 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A low-touch centralized system to predict service failure in the supply chain using machine learning, comprising:
a server comprising a processor and a memory, the server configured to:
receive only 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, wherein the one or more supply chain entities store one or more items at one or more stocking locations;
aggregate the historical supply chain data to a certain granularity level during a training phase of a gradient boosted trees machine learning model, wherein the aggregated historical supply chain data comprises a sample;
train the gradient boosted trees machine learning model using the sample;
predict one or more supply chain failures during a prediction period by applying a prediction model to a sample of historical supply chain data comprising a sample period earlier than the prediction period; each of the one or more predicted supply chain failures associated with at least one item of the one or more items and at least one stocking location of the one or more stocking locations during a prediction period, wherein the prediction model comprises the gradient boosted trees machine learning model;
calculate an occurrence risk score for at least one of the one or more predicted supply chain failures, the occurrence risk score indicating a possibility that the at least one of the one or more predicted supply chain failures will occur;
determine precision and recall measurements for the prediction model;
determine whether to retrain the gradient boosted trees machine learning model;
calculate a magnitude and a direction of an effect one or more prediction features have on the occurrence risk score;
generate one or more alerts for the one or more supply chain events, each of the one or more alerts representing an output value whose metrics indicate a threshold of greater than 50% chance of occurring associated with at least one alert supply chain event of the one or more supply chain events, the one or more alerts comprising at least one alert item and at least one alert stocking location, the at least one alert item identifying the at least one item of the at least one alert supply chain event and the at least one alert stocking location identifying the at least one stocking location of the at least one alert supply chain event;
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 at least one alert supply chain event; and
initiate the one or more corrective actions by instructing automated machinery to produce one or more products based, at least in part, on the one or more predicted supply chain failures.