Delivery Estimate Accuracy: Understanding and Reducing Virtual-Physical Mismatches and Missorts in Fulfillment Centers

Delivery Estimate Accuracy (DEA) is the Amazon Operations metric that measures the percentage of items that attempted delivery on or before the Promised Delivery Date (PDD). There are significant costs and customer experience impacts when packages are not delivered on time, resulting in a DEA miss....

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Bibliographic Details
Main Author: Yao, Rong (Jenny)
Other Authors: Zheng, Y. Karen
Format: Thesis
Published: Massachusetts Institute of Technology 2024
Online Access:https://hdl.handle.net/1721.1/155603
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author Yao, Rong (Jenny)
author2 Zheng, Y. Karen
author_facet Zheng, Y. Karen
Yao, Rong (Jenny)
author_sort Yao, Rong (Jenny)
collection MIT
description Delivery Estimate Accuracy (DEA) is the Amazon Operations metric that measures the percentage of items that attempted delivery on or before the Promised Delivery Date (PDD). There are significant costs and customer experience impacts when packages are not delivered on time, resulting in a DEA miss. Specifically, there are two types of DEA misses that are less well-understood than others and make up a large proportion of the overall missesVirtual-Physical Mismatch (VPM) and Missort. This project focuses on understanding and reducing the number of VPM and Missort misses in Fulfillment Centers, with the scope being Amazon’s Traditional Non-Sort Fulfillment Centers in the US.
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spelling mit-1721.1/1556032024-07-11T03:01:03Z Delivery Estimate Accuracy: Understanding and Reducing Virtual-Physical Mismatches and Missorts in Fulfillment Centers Yao, Rong (Jenny) Zheng, Y. Karen Williams, John Sloan School of Management Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Delivery Estimate Accuracy (DEA) is the Amazon Operations metric that measures the percentage of items that attempted delivery on or before the Promised Delivery Date (PDD). There are significant costs and customer experience impacts when packages are not delivered on time, resulting in a DEA miss. Specifically, there are two types of DEA misses that are less well-understood than others and make up a large proportion of the overall missesVirtual-Physical Mismatch (VPM) and Missort. This project focuses on understanding and reducing the number of VPM and Missort misses in Fulfillment Centers, with the scope being Amazon’s Traditional Non-Sort Fulfillment Centers in the US. S.M. M.B.A. 2024-07-10T20:17:49Z 2024-07-10T20:17:49Z 2024-05 2024-06-25T18:23:53.054Z Thesis https://hdl.handle.net/1721.1/155603 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology
spellingShingle Yao, Rong (Jenny)
Delivery Estimate Accuracy: Understanding and Reducing Virtual-Physical Mismatches and Missorts in Fulfillment Centers
title Delivery Estimate Accuracy: Understanding and Reducing Virtual-Physical Mismatches and Missorts in Fulfillment Centers
title_full Delivery Estimate Accuracy: Understanding and Reducing Virtual-Physical Mismatches and Missorts in Fulfillment Centers
title_fullStr Delivery Estimate Accuracy: Understanding and Reducing Virtual-Physical Mismatches and Missorts in Fulfillment Centers
title_full_unstemmed Delivery Estimate Accuracy: Understanding and Reducing Virtual-Physical Mismatches and Missorts in Fulfillment Centers
title_short Delivery Estimate Accuracy: Understanding and Reducing Virtual-Physical Mismatches and Missorts in Fulfillment Centers
title_sort delivery estimate accuracy understanding and reducing virtual physical mismatches and missorts in fulfillment centers
url https://hdl.handle.net/1721.1/155603
work_keys_str_mv AT yaorongjenny deliveryestimateaccuracyunderstandingandreducingvirtualphysicalmismatchesandmissortsinfulfillmentcenters