2026-09-21T14:29:44Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1556032024-07-11T03:01:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Zheng, Y. Karen
Williams, John
Yao, Rong (Jenny)
Sloan School of Management
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
2024-07-10T20:17:49Z
2024-07-10T20:17:49Z
2024-05
2024-06-25T18:23:53.054Z
https://hdl.handle.net/1721.1/155603
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.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Delivery Estimate Accuracy: Understanding and Reducing Virtual-Physical Mismatches and Missorts in Fulfillment Centers
Thesis
application/pdf
Master
Master of Science in Civil and Environmental Engineering
Master of Business Administration
Publication
unknown
unknown
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<Abstract>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.</Abstract>
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