Blue Yonder Group, Inc.

Eric Prescott-Gagnon

Thierry Moisan

Yossiri Adulyasak

Vincent Raymond

Gabrielle Gauthier Melancon

Philippe Grangier

Emmanuel Sabourin

Benoît Rochon

Marc Brisson

A system and method are disclosed including a demand planner that receives a demand for two or more options that are needed to produce at least one automobile. The demand planner also models the two or more options as a network of arcs and nodes and generates one or more valid configurations of the two or more options. The demand planner further determines the demand for the one or more valid configurations and causes at least one manufacturer to manufacture, the at least one automobile based on the determined demand for the one or more valid configurations.

Method, medium, and system for demand planning

A system and method are disclosed for an inventory planner that generates an inventory policy using any form of demand distributions, non-linear cost functions and/or multiple target measures of service levels, while taking into account a supply order lead time, such as, for example, a static or stochastic supply order lead time. The inventory policy generated by the inventory planner comprises an optimal and reproducible solution to one or more supply chain planning problems.

Distribution-independent inventory approach under multiple service level targets

A system and method are disclosed including a computer and a processor and memory. The computer receives option constraint limiting values of a second option parameter based on a value for a first option parameter, wherein the first option parameter and the second option parameter comprise mix rates for options for a configuration. The computer further generates an interactive element for setting values of the first option parameter and an interactive element for setting the value for the second option parameter and displays the one or more interactive elements. The computer still further, in response to a value being set for the first option parameter, calculates limits for the values of the second option parameters and prevents the interactive element for setting the value for the second option parameter to be set to values outside of the calculated limits.

System and method of user interface for automotive production planning

A system and method are disclosed for a low-touch centralized system to predict service level failure in a supply chain using machine learning. Embodiments include receiving only historical supply chain data from an archiving system for one or more supply chain entities storing items at stocking locations, predicting one or more supply chain events during a prediction period by applying a predictive model to a sample of historical supply chain data, calculating an occurrence risk score for at least one of the one or more supply chain events and indicating a possibility that the at least one of the one or more supply chain events will occur, generating one or more alerts identifying at least one item and at least one alert stocking location, rendering an alert heatmap visualization comprising one or more selectable user interface elements, and provide one or more tools for initiating corrective actions to be undertaken.

System to predict service level failure in supply chains

A system and method are disclosed for searching alternate inventory policies from an initial inventory policy of an inventory of one or more supply chain entities to a target inventory policy by receiving a current state of items in a supply chain network determining an initial inventory policy comprising a reorder point and a target quantity, identifying one or more provisional inventory policies calculating a cost, a fill rate, and a no-stockout probability, and transporting items among the one or more supply chain entities to restock the inventory.

System and method for inventory planning in a supply chain network

A system and method are disclosed for receiving only historical supply chain data from an archiving system for one or more supply chain entities storing items at stocking locations, predicting one or more supply chain events during a prediction period by applying a prediction model to a sample of historical supply chain data, calculating an occurrence risk score for at least one of the one or more supply chain events and indicating a possibility that the at least one of the one or more supply chain events will occur, generating one or more alerts identifying at least one item and at least one alert stocking location, rendering an alert heatmap visualization comprising one or more selectable user interface elements, and provide one or more tools for initiating corrective actions to be undertaken in order to resolve one or more underlying causes of the at least one alert supply chain event.

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Montréal, Canada

Eric Prescott-Gagnon