Amazon Technologies, Inc.

Patrick Ludvig Bajari

Chao Liu

Vikram Sathyanarayana Anbazhagan

Sameer R Rajyaguru

Jin Lai

Zhuo Zhang

Deepak Bhatia

Ali Sadighian

Ivan Eduardo Gonzalez

Jun Tong

Jeffrey Paul Helbling

Jeffrey L Belle

Salal Humair

Kelly Paulson

Di Wu

Salar Jahedi

Lillian S Bleiman

Xiaochang Hu

Yi Huang

Mahmoud Ahmed Bishr

Ioannis Giannakakis

Eric Walter Zivot

Travis Francis Brayak

Margret Steele

Ana Maria Mocanu

Xiaoyu Zheng

Ian Wallace

Eric C Adams

Harry J Paarsch

Devesh R Raval

Features related to a system and method for dynamic resource prediction simulation are described. The predictions consider historical data collected by the organization or user revisions to planning inputs to predict the amount of a resource needed for a certain task and the amount of the resource able to perform the tasks. The system and method displays the predictions in a visual representation including control elements allowing a user to simulate a variety of scenarios and situations to allow the organization to properly reallocate resources or acquire an appropriate amount of the resource.

Dynamic resource prediction simulation

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

Based on historical data describing product sales or other types of transactions, indicator scores may be calculated for users who have purchased, reviewed, or otherwise consumed products, the indicator scores based on subsequent diffusion of the products. Measured, current activities of the scored users with regard to another product may then be employed to develop a diffusion model that predicts the future sales of the other product over time. The diffusion model may be employed to determine product pricing, marketing, distribution, or acquisition.

Diffusion prediction based on indicator scoring

This disclosure is directed to measuring test effects using adjusted outlier data. Test data and control data may include some outlier data (i.e., right-side tails of distribution curves), which may bias the resultant data. The outlier data may be adjusted to reduce bias. A cutoff point is selected along the distribution of data. Data below the cutoff is maintained and used to determine an effect of the data below the cutoff point. The effect of the data above the cutoff may be processed as follows. Predictor data is identified from the data below, but near, the cutoff point. The predictor data may then be used determine the effect of the outlier data that is above the cutoff point. In some embodiments, the predictor data may be weighted and combined with a weighted portion of the outlier data to determine an effect of the data above the cutoff point.

Measuring test effects using adjusted outlier data

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Bellevue, WA, United States of America

Patrick Ludvig Bajari