Blue Yonder Group, Inc.

Sudhakar Jayapal

Karthik Kondapaneni

Ganesh Muthusamy

Rajneesh Kumar Agrawal

Maruthy Boggarapu

Shankar Lakkaraju

Pankaj Kumar

Rajeev Vasudev

Chaitanya Lella

Samson Sudhahar

Venu Gurram

Lalitha Namburi

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 are multi-level replenishment planning with independent channel demand prioritization that initiates production of a product by a first supply chain entity that receives orders for the product from a second supply chain entity and supplies the product to a third supply chain entity that receives orders for the product from a fourth supply chain entity. Embodiments further disclose calculating the supply of the product for the first supply chain entity for a supply allocation duration time period, receiving demand orders high priority demand orders from the fourth supply chain entity, allocating supplies within the supply allocation duration time period to firm plan arrivals based on a location priority and a demand date, and generating a shipment recommendation for each allocated supply that meets a firmed planned arrival.

Omni-channel multi-level demand prioritization and allocation

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Hyderabad Telangana, India

Sudhakar Jayapal