| dc.contributor.advisor | Zheng, Y. Karen | |
| dc.contributor.advisor | Williams, John | |
| dc.contributor.author | Yao, Rong (Jenny) | |
| dc.date.accessioned | 2024-07-10T20:17:49Z | |
| dc.date.available | 2024-07-10T20:17:49Z | |
| dc.date.issued | 2024-05 | |
| dc.date.submitted | 2024-06-25T18:23:53.054Z | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/155603 | |
| dc.description.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. | |
| dc.publisher | Massachusetts Institute of Technology | |
| dc.rights | In Copyright - Educational Use Permitted | |
| dc.rights | Copyright retained by author(s) | |
| dc.rights.uri | https://rightsstatements.org/page/InC-EDU/1.0/ | |
| dc.title | Delivery Estimate Accuracy: Understanding and Reducing Virtual-Physical Mismatches and Missorts in Fulfillment Centers | |
| dc.type | Thesis | |
| dc.description.degree | S.M. | |
| dc.description.degree | M.B.A. | |
| dc.contributor.department | Sloan School of Management | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering | |
| mit.thesis.degree | Master | |
| thesis.degree.name | Master of Science in Civil and Environmental Engineering | |
| thesis.degree.name | Master of Business Administration | |
