Date of Award
August 2020
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Industrial Engineering
Committee Member
Scott J. Mason
Committee Member
William G. Ferrell
Committee Member
Mary E. Kurz
Abstract
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.
Recommended Citation
Stoll, Franz, "A Comparison of Machine Learning and Traditional Demand Forecasting Methods" (2020). All Theses. 3367.
https://tigerprints.clemson.edu/all_theses/3367