Predicting on-time delivery in the trucking industry
Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2017.
| Main Authors: | , |
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2017
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| Online Access: | http://hdl.handle.net/1721.1/112870 |
| _version_ | 1826191479824973824 |
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| author | Duarte Alcoba, Rafael Ohlund, Kenneth W |
| author2 | Matthias Winkenbach. |
| author_facet | Matthias Winkenbach. Duarte Alcoba, Rafael Ohlund, Kenneth W |
| author_sort | Duarte Alcoba, Rafael |
| collection | MIT |
| description | Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2017. |
| first_indexed | 2024-09-23T08:56:31Z |
| format | Thesis |
| id | mit-1721.1/112870 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T08:56:31Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1128702022-01-13T07:55:12Z Predicting on-time delivery in the trucking industry Duarte Alcoba, Rafael Ohlund, Kenneth W Matthias Winkenbach. Massachusetts Institute of Technology. Supply Chain Management Program. Massachusetts Institute of Technology. Supply Chain Management Program Supply Chain Management Program. Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (page 51). On-time delivery is a key metric in the trucking segment of the transportation industry. If on-time delivery can be predicted, more effective resource allocation can be achieved. This research focuses on building a predictive analytics model, specifically logistic regression, given a historical dataset. The model, developed using six explanatory variables with statistical significance, results in a 76.4% resource reduction while incurring an impactful error of 2.4%. Interpretability and application of the logistic regression model can deliver value in predictive power across many industries. Resulting cost reductions lead to strategic competitive positioning among firms employing predictive analytics techniques. by Rafael Duarte Alcoba and Kenneth W. Ohlund. M. Eng. in Supply Chain Management 2017-12-20T18:15:31Z 2017-12-20T18:15:31Z 2017 2017 Thesis http://hdl.handle.net/1721.1/112870 1014336868 eng MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 51 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Supply Chain Management Program. Duarte Alcoba, Rafael Ohlund, Kenneth W Predicting on-time delivery in the trucking industry |
| title | Predicting on-time delivery in the trucking industry |
| title_full | Predicting on-time delivery in the trucking industry |
| title_fullStr | Predicting on-time delivery in the trucking industry |
| title_full_unstemmed | Predicting on-time delivery in the trucking industry |
| title_short | Predicting on-time delivery in the trucking industry |
| title_sort | predicting on time delivery in the trucking industry |
| topic | Supply Chain Management Program. |
| url | http://hdl.handle.net/1721.1/112870 |
| work_keys_str_mv | AT duartealcobarafael predictingontimedeliveryinthetruckingindustry AT ohlundkennethw predictingontimedeliveryinthetruckingindustry |