Predicting on-time delivery in the trucking industry
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 in...
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ndltd-MIT-oai-dspace.mit.edu-1721.1-1128702019-05-02T15:40:52Z 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 are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 51 pages application/pdf Massachusetts Institute of Technology |
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Supply Chain Management Program. Duarte Alcoba, Rafael Ohlund, Kenneth W Predicting on-time delivery in the trucking industry |
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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 |
author2 |
Matthias Winkenbach. |
author_facet |
Matthias Winkenbach. Duarte Alcoba, Rafael Ohlund, Kenneth W |
author |
Duarte Alcoba, Rafael Ohlund, Kenneth W |
author_sort |
Duarte Alcoba, Rafael |
title |
Predicting on-time delivery in the trucking industry |
title_short |
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_sort |
predicting on-time delivery in the trucking industry |
publisher |
Massachusetts Institute of Technology |
publishDate |
2017 |
url |
http://hdl.handle.net/1721.1/112870 |
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AT duartealcobarafael predictingontimedeliveryinthetruckingindustry AT ohlundkennethw predictingontimedeliveryinthetruckingindustry |
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1719025885050306560 |