Master of Science (MS), Ohio University, 2015, Industrial and Systems Engineering (Engineering and Technology)

This study is an initial attempt to investigate the relationship between economic factors and monthly tire sales, using artificial neural networks (ANNs) and comparing the results to stepwise regression. Data for this research were collected through a privately held tire warehouse located in Wheeling, West Virginia. Research has shown that artificial neural network models have been successfully applied to many real world forecasting applications. However, up to this date no research has been found using artificial neural networks and economic factors to predict tire demand. The first part of this research describes why the chosen economic factors were selected for this study and explains the initial methodology with results. The next stage of the research gives details on why the methodology was revised and also clarifies why Google Trends and additional mathematical inputs were applied to the study. The final research focused on separating the master database into three different categories based on selling percentages. The results of the study show that the artificial neural network models were capable of forecasting the number of high selling tires, with a validation technique, but were unable to be applied sufficiently for the medium and low selling products.

Committee: Gary Weckman Ph.D. (Advisor) Subjects: Engineering; Industrial Engineering