Amazon Technologies, Inc.

Hernan P Awad

Chengliang Zhang

Ali Sadighian

Deepak Bhatia

Tolga Han Seyhan

Salal Humair

Arjun Krishna Subramaniam

Onur Özkök

Nicholas Deming Sherman

Andrew Bass

Alvaro Maggiar

Stevie Robinson

Yan Xia

Features related to systems and methods for predictive estimation for network inventory planning for items for distribution through a multi-echelon network are disclosed. The problem may be modeled to account for demand for an item that may be satisfied by fulfillment centers within the network. The model considers benefits to ensuring adequate inventory to meet expected demand streams along with any savings accruing from a lower transfer and salvage costs.

Multi-echelon inventory planning under dynamic fulfillment policies

Embodiments herein are directed to determining an expected demand for an item. The specialized computer system can model an impact to an expected demand for the item between a current planning horizon and a next planning horizon, including an estimated leftover demand to be fulfilled during the next planning horizon. As calculated, this leftover demand may be less than an unfulfilled demand from the current planning horizon and may be estimated based at least in part on the unfulfilled demand and on historical leftover demand between planning horizons.

Inventory optimization based on leftover demand distribution function

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Bellevue, WA, United States of America

Hernan P Awad