Demand forecasting is based on the principle of trying to forecast the demand for the outputs of enterprises in the field of manufacturing or service for the next periods. It requires the estimation of various future scenarios, if necessary, taking measures and taking steps, and during the application phase, the technique that is most suitable for the characteristics of the examined data set is selected and used. As a result of a healthy analysis carried out in this way, detailed plans and strict measures can be taken for the unknown, negative scenarios of the future.
This study analyzes the characteristics of a series of power transformers of a company operating in the electromechanical industry in the past years, and as a result of this analysis, the Box Jenkins Autoregressive Integrated Moving Average method (ARIMA), which best fits the results, is expected to occur for power transformers in the future. It was made to estimate the amount of demand.
Within the scope of this study, firstly, the most suitable model was tried to be determined by taking into consideration the past 132 months data of PTS. It was decided that the best choice among the alternative models was the ARMA (4,4) x (0,1) 12 model. The model was found to be stable and it was decided that the root mean square error (RMSE), mean absolute percentage error (MAPE) and Theil inequality coefficient values determined in the performance measurements were appropriate.
ARIMA,, Box Jenkins,, Transformer demand forecast