Journal of Decision Engineering

In many real commercial and administrative issues, relations are often complex, non-routine and are not predictable by conventional methods. Considering the importance of feasibility studies in making decision to start a production activity, forecasting in marketing studies is very important. There are lots of tools and techniques which are used for having an exact prediction, neural networks can be utilized for forecasting with high degrees of accuracy. The purpose of this article is to demonstrate the preference of using neural networks in forecasting nonlinear processes in comparison with conventional techniques and also to increase its accuracy by using economic parameters such as inflation and exchange rates. As a case study, this paper uses the production rate data of PET bottles from 1379 to 1392, then the production rate of 1393 is predicted by using artificial neural networks and nonlinear models. For validating the model, indexes MAPE and MSE obtained from these methods are compared. The result shows the preference of using the neural network for prediction in comparison with time and exponential series techniques, due to the lowest error in forecasting