Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysis

Abstract

Organizations today are required to be prepared for future situations. This preparation can generate a significant competitive advantage. In order to maximize benefits, several companies are investing more in techniques that simulate a future scenario and enable more precise and assertive decision making. Among these techniques are the sales forecasting methods. The comparison between the known techniques is an important factor to increase the assertiveness of the forecast. The objective of this study was to compare the sales forecast results of a mechanical components manufacturing company obtained through five different techniques, divided into two groups, the first one, which uses the fundamentals of the time series, and the second one is the Monte Carlo simulation. The following prediction methods were compared: moving average, weighted moving average, least squares, holt winter and Monte Carlo simulation. The results indicated that the methods that obtained the best performance were the moving average and the weighted moving average attaining 94% accuracy.