Blue Yonder Group, Inc.

Jda Software Group, Inc.

Ganesh Muthusamy

Abhishek Singh

Rashid Puthiyapurayil

Phani Bulusu

Chandrashekar Srikantiah Konanur

Probal Bardhan

Ardyanto Iskandar Alam

Karthik Kondapaneni

Sudhakar Jayapal

Rajneesh Kumar Agrawal

A system and method for allocation planning comprise a server comprising a processor and memory and configured to calculate a reward for a historical allocation of a product to one or more stores associated with a retailer. Embodiments include simulating what-if scenarios for the historical allocation to identify an allocation having a greater reward than the historical allocation and allocating a quantity of a product for a current allocation to the one or more stores based, at least in part, on a distance calculation of one or more independent variables for the historical allocation and the current allocation and the identified allocation having the greater reward then the historical allocation.

System and method of reinforced machine-learning retail allocation

A system and method of autonomous multi-dimensional segmentation for a supply chain network. Embodiments include a supply chain network of one or more supply chain entities, a segmentation planner having a computer and memory, the segmentation planner configured to access input data relating to one or more supply chain entities, discover one or more features related to the input data, pre-process the input data and features, perform multi-dimension segmentation on the input data, generate one or more segment output visualizations, assign policy parameters to the multi-dimension segmentation performed on the input data.

Autonomous multi-dimension segmentation user interface workflow

A system and method for performing strategic segmentation including a supply chain network having a strategic segmentation planner, an inventory system, a transportation network and supply chain entities. The strategic segmentation planner includes a computer having a memory and a processor, that selects a workflow depth including an amount of data to analyze, discovers features by analyzing cleansed data to locate features which are characterized by features data, pre-processes the features data to standardize the features data, performs multi-dimension segmentation by computing feature importance to generate multi-dimensional segments, assigns policy parameters to the supply chain network based on the generated multi-dimensional segments, and trains a machine learning model by applying a cyclic boosting process to the standardized features data wherein the cyclic boosting process iteratively learns relationships associated with the generated multi-dimensional segments.

Strategic and tactical intelligence in dynamic segmentation

A system and method for performing tactical segmentation including a supply chain network having a tactical segmentation planner, an inventory system, a transportation network, supply chain entities and a computer. The computer performs multi-dimension segmentation on input data by computing feature importance to generate multi-dimensional segments, assigns policy parameters to the supply chain network based on the generated multi-dimensional segments, trains a machine learning model by applying a cyclic boosting process to the standardized features data, where the cyclic boosting process iteratively learns relationships associated with the generated multi-dimensional segments, stores the machine learning model in a database, performs multi-dimension segmentation based on the stored machine learning model, determines whether data drift has occurred in the input data and in response to determining that data drift has occurred, repeats the perform, assign, trains steps, and stores an updated machine learning model in the database.

A system and method is disclosed for computing optimal inventory targets in bill of materials in a multi-echelon supply chain. The system includes one or more memory units and on or more processing units collectively configured to determine a delay time from at least one second plan level node in an at least second indenture plan level to at least one first plan level node in a first indenture plan level. The one or more memory units and on or more processing units is further collectively configured to determine a delay time from the at least one first plan level node in the first indenture plan level to a finished good.

Computation of optimal inventory targets in bill-of-materials in a multi-echelon supply chain

A system and method is disclosed for computing optimal inventory targets in bill of materials in a multi-echelon supply chain. The system includes one or more memory units and one or more processing units collectively configured to determine a delay time from at least one second plan level node in an at least second indenture plan level to at least one first plan level node in a first indenture plan level. The one or more memory units and one or more processing units is further collectively configured to determine a delay time from the at least one first plan level node in the first indenture plan level to a finished good.

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Tamil Nadu, India

Ganesh Muthusamy