Comparisons between forecast models are necessary for decision making in industry, es- pecially for demand prediction. In the presence of few historical data, there could be difficulties in the compliance of theoretical premises. In this paper, a comparison is presented designed in R program, using four types of models: Bayesian linear regression with normal prior distribution, bayesian dynamic linear model, ARIMA and exponential smoothing, based on criteria: Mean Absolute Percentage Error (MAPE) of forecasts, and therefore different data scenarios are simulated, reecting demand behavior with and without a Ingeniera Industrial, Magister en Estad