Predicting on-time delivery in the trucking industry
Name
1014336868-MIT.pdf
Description
Full printable version
Size
5.66 MB
Format
Adobe PDF
Checksum (MD5)
02f82baa588f115a250c743346da8f31
Author(s)
Duarte Alcoba, Rafael
Ohlund, Kenneth W
Advisor(s)
Matthias Winkenbach.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Supply Chain Management Program.
MIT Department
Massachusetts Institute of Technology. Supply Chain Management Program
Terms of Use
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.
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