Predicting on-time delivery in the trucking industry

• Main Authors: Duarte Alcoba, Rafael, Ohlund, Kenneth W
• Other Authors: Matthias Winkenbach.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2017
• Subjects: Supply Chain Management Program.
• Online Access: http://hdl.handle.net/1721.1/112870

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