Car Sales Forecasting Using Artificial Neural Networks and Analytical Hierarchy Process - Case Study: Kia and Hyundai Corporations in the USA

摘要

In this study, we evaluate different effective factors related to marketing and sales and discuss the various prediction methods. The field of this study is the car industry and the tools used for classification, comparison and weight determination is the Analytical Hierarchy Process (AHP). Artificial Neural Networks are used for identifying the architecture and shaping the process of prediction. In order to do so, using a questionnaire presented to experts in the field, the factors affecting car sales in North America were identified and the processed weights obtained from these opinions were fed to the neural network as input, so that, ultimately, by teaching the network through different algorithms, the optimal solution can be obtained. The conceptual model of the research first identifies the factors affecting sales and then tries to determine the interconnection among the data. In order to compare the performance of this method, we needed a valid and established measure so that we can assess the methods based on it. Therefore, linear and exponential regression methods were selected to compare the degree of error and to obtain a more desirable final output which is closer to reality. The obtained result indicates the successful performance of the neural network compared to other selected methods and it was found that it has a lower Minimum Square Error (MSE) compared to others.