Amazon Technologies, Inc.

Deepak Bhatia

Ali Sadighian

Tolga Han Seyhan

Salal Humair

Jeffrey B Maurer

Onur Özkök

Jingchen Wu

Arjun Krishna Subramaniam

Chao Liu

Chengliang Zhang

Jun Tong

Patrick Ludvig Bajari

Jingqiao Zhang

Zhihao Cen

Di Wu

Nicholas Deming Sherman

Hernan P Awad

Abid Kapadya

Julie Wei-Shu Chang

Xiaochang Hu

Mahmoud Ahmed Bishr

Travis Francis Brayak

Gordon Mitchell Goetz

Jialei Rong

Haiyun Wang

Yan Xia

Zhihao Chen

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 computing a feature based on variables may be provided. For example, historical realizations associated with a first variable may be accessed. The first variable may be associated with an item. Realizations of the first variable may be based on one or more factors associated with the item. Historical realization of a second variable associated with the item may also be accessed. The historical realizations of the first and second variables may be analyzed to generate an expected realization of the first variable as a function of the second variable. The feature may be computed based on the generated function.

Efficiently computing a feature based on a plurality of variables

Techniques are provided herein for utilizing an inventory engine to optimize the selection of a set of items to be stored as inventory at a storage location. A candidate set of items may be identified based at least in part on a selection model. In accordance with at least one embodiment, the selection model may be based at least in part on a capacity of the storage location and a threshold time duration by which purchased items of the set of items are to be transported from the storage location to a purchaser. A plurality of probability values corresponding to the candidate set of items may be determined. An optimal set of items may be determined based at least in part on the candidate set of items and the plurality of probability values.

Techniques for optimizing an inventory selection

Techniques for managing a removal channel may be described. In an example, a first estimate may be generated. The first estimate may be associated with removing a volume of items from an inventory space within a planning horizon through a removal channel. A decision to remove the volume of items may be accessed. The decision may be based on the first estimate and a second estimate associated with adding a capacity for inventorying an equivalent volume of items. Based on the decision, a constraint may be generated and imposed on an inventory level of an item during a time period of the planning horizon. A quantity of the item to remove within the time period through the removal channel may be estimated based on the constraint. Usage of the removal channel during the time period may be set based on the quantity of the item to remove.

Managing a removal channel based on inventory capacity

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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Issaquah, WA, United States of America

Deepak Bhatia