Kinaxis Inc.

Chantal Bisson-Krol

Zhen Lin

Marcio Oliveira Almeida

Seyednaser Nourashrafeddin

Behrouz Haji Soleimani

Sebastien Ouellet

Christopher Wang

Phillip Williams

Ishan Amlekar

Kevin Shen

Ali Khanafer

Systems and methods for embedding a computational notebook within an enterprise application software. A computational notebook editor embedded is embedded within a software client interface which is in communication with the software client interface. The application server comprises a reverse proxy server that is embedded within the application server. A container management system is in communication with the application server and comprises a multi-user server, a notebook interactive development environment, and a notebook execution tool.

Systems and methods for embedding a computational notebook

A method and system for a machine learning duster analysis of historical lead time data, which is augmented by one or more features. The data can also be divided into groups, based on time-density of the data, with clustering performed on each group. Furthermore, clustering can also be projected onto two dimensions. In addition, the historical lead time data is separated into a plurality of tolerance zones based on tolerance criteria. The clusters are separated in accordance with a tolerance zone of each group; and further separated according to one or more lead time identifiers to provide one or more separated clusters.

Analysis and correction of supply chain design through machine learning

A dynamic supply chain planning system for analysis of historical lead time data that uses machine learning algorithms to forecast future lead times based on historical lead time data, weather data and financial data related to locations and dates within the supply chain.

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

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.

Systems and methods for dynamic demand sensing

A method and system for a machine learning cluster analysis of historical lead time data, which is augmented by one or more features. The data can also be divided into groups, based on time-density of the data, with clustering performed on each group. Furthermore, clustering can also be projected onto two dimensions. In addition, the historical lead time data is separated into a plurality of tolerance zones based on tolerance criteria. The clusters are separated in accordance with a tolerance zone of each group; and further separated according to one or more lead time identifiers to provide one or more separated clusters.

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Kanata, Canada

Chantal Bisson-Krol