Kinaxis Inc.

Sebastien Ouellet

Zhen Lin

Chantal Bisson-Krol

Christopher Wang

Phillip Williams

Nathaniel Stanley

Jeffery Downing

Liam Hebert

Seyednaser Nourashrafeddin

Ishan Amlekar

Kevin Shen

Ali Khanafer

Behrouz Haji Soleimani

Systems and methods for dynamic demand sensing in a supply chain in which constantly-updated data is used to select a machine learning model or retrain a pre-selected machine learning model, for forecasting sales of a product at a specific location. The updated data includes product information and geographic information. Also disclosed are systems and methods relating to demand forecasting and readjusting forecasts based on forecast error.

Systems and methods for dynamic demand sensing and forecast adjustment

Methods and systems that provide one or more recommended configurations to planners using large data sets in an efficient manner. These methods and systems provide optimization of objectives using a genetic algorithm that can provide parameter recommendations that optimize one or more objectives in an efficient and timely manner. The methods and systems disclosed herein are flexible enough to satisfy diverse use cases.

Systems and methods for parameter optimization

Systems and methods for features engineering, in which internal and external signals are received and fused. The fusing is based on meta-data of each of the one or more internal signals and each of the one or more external signals. A set of features is generated based on one or more valid combinations that match a transformation input, the transformation forming part of library of transformations. Finally, a set of one or more features is selected from the plurality of features, based on a predictive strength of each feature. The set of selected features can be used to train and select a machine learning model.

Systems and methods for features engineering

A computer-implemented method of interactive machine learning in which a user is provided with predicted results from a trained machine learning model. The user can take the predicted results and adjust the predicted data to retrain the model.

Interactive machine learning

A computer-implemented method of interactive machine learning in which a user is provided with predicted results from a trained machine learning model. The user can take the predicted results and either: i) adjust the predicted results and input the adjusted results as new data; or ii) adjust the predicted data to retrain the model.

Systems and methods for dynamic demand sensing in a supply chain in which constantly-updated data is used to select a machine learning model or retrain a pre-selected machine learning model, for forecasting sales of a product at a specific location. The updated data includes product information and geographic information.

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Ottawa, Canada

Sebastien Ouellet