Amazon Technologies, Inc.

Arjun Krishna Subramaniam

Deepak Bhatia

Tolga Han Seyhan

Onur Özkök

Chengliang Zhang

Jeffrey B Maurer

Salal Humair

Jingchen Wu

Hernan P Awad

Nicholas Deming Sherman

Yan Xia

Gordon Mitchell Goetz

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

Techniques for determining a decision to acquire units of an item to be inventoried may be provided. For example, a demand for an item may be simulated to determine a consumption of a capacity for inventorying the item. A discrepancy between the consumption of the capacity and the capacity may be determined. An opportunity cost associated with the capacity may be updated based at least in part on determining that the discrepancy fails a convergence criterion. The opportunity cost may indicate a value associated with using the capacity. The consumption of the capacity may be simulated based at least in part on the updated opportunity cost. A resulting discrepancy may be determined. If the resulting discrepancy meets the convergence criterion, the decision to acquire the units of the item may be generated based at least in part on the updated opportunity cost.

Adaptive control of an item inventory plan

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Seattle, WA, United States of America

Arjun Krishna Subramaniam