Predicting on-time delivery in the trucking industry

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Supply Chain Management Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Authors dc:creator

• Duarte Alcoba, Rafael
• Ohlund, Kenneth W

Advisor dc:contributor.advisor

• Matthias Winkenbach.

• Supply Chain Management Program.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/112870

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/112870