2026-09-22T21:30:46Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1128702026-06-06T01:04:09Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Matthias Winkenbach.
Duarte Alcoba, Rafael
Ohlund, Kenneth W
Massachusetts Institute of Technology. Supply Chain Management Program.
Massachusetts Institute of Technology. Supply Chain Management Program
2017-12-20T18:15:31Z
2017-12-20T18:15:31Z
2017
2017
http://hdl.handle.net/1721.1/112870
1014336868
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
51 pages
eng
Massachusetts Institute of Technology
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
Supply Chain Management Program.
Predicting on-time delivery in the trucking industry
Thesis
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