Date of Award


Degree Type


Degree Name

Master of Mathematics (MMath)


Mathematics and Statistics

Committee Chair(s)

Joseph Koebbe


Joseph Koebbe


Tyler Brough


Kevin Moon


Invista, a Koch subsidiary, is a multinational producer of fibers, resins, and intermediaries, particularly nylon. To keep the company operating required them to take over 1.5 million orders over the course of - years, less than a third of which arrived on-time. Orders arriving other than when expected can cause many problems for any company. While arriving late is a clear problem, it also troublesome for them to arrive early. In the face of this, it becomes important to be able to tell a-priori if an order will arrive on-time or not.

To address this problem, we made use of those 1.5 million orders to try and learn how to predict if an order would be on-time or not. There are many methods for doing so and we tried three approaches: Neural-Networks, Gradient Boosting, and Time series. In the end we found the Gradient Boosting algorithm worked the best. We utilized the popular XGBoost framework of Gradient boosting. This was made further appealing by the company having utilized this algorithm before.

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