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Amazon Inventory Reconciliation using AI

Team Members:

  • Pablo Rodriguez Bertorello, Computer Science, Stanford University
  • Sravan Sripada, Computer Science, Stanford University
  • Nutchapol Dendumrongsup, Computational and Mathematical Engineering, Stanford University

Abstract

Amazon Fulfillment Centers are bustling hubs of innovation that allow Amazon to deliver millions of products to over 100 countries worldwide. These products are randomly placed in bins, which are carried by robots. Occasionally, items are misplaced while being handled, resulting in a mismatch: the recorded bin inventory, versus its actual content. The paper describes methods to predict the number of items in a bin, thus detecting any inventory variance. By correcting variance upon detection, Amazon will better serve its customers.

Report

For details see the project report https://github.com/pablo-tech/AI-Inventory-Reconciliation/blob/master/ProjectReport.pdf

Summary

picture

Environment

conda env create -f environment.yml
source activate ai-inventory
conda env remove -n ai-inventory
conda info --envs

About

Response to Amazon's Bin Image Data Set Challenge. Inventory reconciliation with machine learning: SVMs and CNNs. Research at Stanford University, by: Pablo Rodriguez Bertorello, Sravan Sripada, and Nutchapol Dendumrongsup

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