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Predicting on-time delivery in the trucking industry

Author(s)
Duarte Alcoba, Rafael; Ohlund, Kenneth W
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Massachusetts Institute of Technology. Supply Chain Management Program.
Advisor
Matthias Winkenbach.
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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).
 
Date issued
2017
URI
http://hdl.handle.net/1721.1/112870
Department
Massachusetts Institute of Technology. Supply Chain Management Program
Publisher
Massachusetts Institute of Technology
Keywords
Supply Chain Management Program.

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