Amazon Technologies, Inc.

Eric Mathew Mack

Devesh Mishra

Jeffrey B Maurer

Jason W Murray

Robert Howard Sawers

Edward Michael Leonard

Kip Christopher Larson

Minshu Xu

Techniques for generating a forecast associated with an item may be described. For example, the forecast may be generated based on simulations of supply and demand variables associated with the item over a planning horizon. The forecast may include at least one of: a labor forecast or a capacity forecast associated with inventorying units of the item in an inventory over the planning horizon. Further, real-time data associated with realizations of the supply and demand variables during the planning horizon may be monitored. At least a portion of the real-time data may be available from a management system. The management system may be configured to manage orders for the units of the item from the inventory. The forecast may be updated during the planning horizon based on the real-time data. The update may include changing the at least one of: the labor forecast or the capacity forecast.

Updating a forecast based on real-time data associated with an item

Systems and methods for supply risk detection are disclosed. In some embodiments, a method includes identifying fulfillment-related features corresponding to an item to be stored in inventory. The method also includes selecting a subset of the fulfillment-related features that is correlated with a supply constraint associated with the item. For example, in some cases the correlation is based on historical supply constraint data. The method further includes building a supply risk early detection model based, at least in part, upon the subset fulfillment-related features and evaluating the model to determine a probability that a third-party vendor will suffer a shortage of the item, as well as an expected duration of the shortage. Upon evaluation of the model, the method may include creating a purchasing plan for the item that takes into account the probability of the shortage and the expected duration of the shortage.

Supply risk detection

Systems and methods for predictive drop shipping are disclosed. In some embodiments, a method may include maintaining inventory of an item that is offered for sale to customers via a webpage. A ratio may be calculated between a number of views of the webpage and a number of units of the item purchased over a first time period when a fast shipping or delivery option is available, and over a second time period when such option is not available. The method may identify the item as a candidate for drop shipping in response to the ratio being larger during the first time period than during the second time period. Upon receipt of an order, the received order may be fulfilled via drop shipping, in some cases, despite the inventory being sufficient to fulfill the order, and despite a cost of fulfillment via drop shipping being higher than fulfillment from inventory.

Predictive drop shipping

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Seattle, WA, United States of America

Eric Mathew Mack