Time Series Analysis on Sales Quantity in an Automotive Company and Estimation by Artificial Neural Networks

The automotive sector, today, is a key sector for developed and developing countries. A powerful automotive sector is one of the common characteristics of industrialised countries. Two significant problems of a genuine production environment are unknown demand and unbalanced production times. These two parameters impact the semi-finished and finished product inventory levels which cause an increase in the total cost of production systems. Forecasting the possible demand for automobiles has gained importance in this sense in recent years. In one of Turkey’s leading automobile companies operating in the provice of Sakarya, the number of orders for future months is estimated over the number of orders for past months while determining the number of automobile sales. In this study, it was aimed to determine this company’s automobile sales by using demand forecasting methods. However, the company’s managers do not want to depend on a single method while deciding on any issue. To this end, time series analysis, causal methods and artificial neural networks were used to chieve demand forecasting. The method that makes the best estimation will be used for this company by comparing these methods. Considering the forecasts to be made using this method, it was aimed to establish a firm base for the annual budgets and main production plan of the company. By using this method, the company will be able to better predict some of its policies and production plans about the automotive sector by predicting the numbers regarding sales in advance.

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