Date of Award

August 2020

Document Type


Degree Name

Master of Science (MS)


Industrial Engineering

Committee Member

Scott J. Mason

Committee Member

William G. Ferrell

Committee Member

Mary E. Kurz


Obtaining accurate forecasts has been a challenging task to achieve for many organizations, both public and private. Today, many firms choose to share their internal information with supply chain partners to increase planning efficiency and accuracy in the hopes of making appropriate critical decisions. However, forecast errors can still increase costs and reduce profits. As company datasets likely contain both trend and seasonal behavior, this motivates the need for computational resources to find the best parameters to use when forecasting their data. In this thesis, two industrial datasets are examined using both traditional and machine learning (ML) forecasting methods. The traditional methods considered are moving average, exponential smoothing, and autoregressive integrated moving average (ARIMA) models, while K-nearest neighbor, random forests, and neural networks were the ML techniques explored. Experimental results confirm the importance of performing a parametric grid search when using any forecasting method, as the output of this process directly determines the effectiveness of each model. In general, ML models are shown to be powerful tools for analyzing industrial datasets.



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