This study aims to obtain an accurate bread demand forecasting method in one of the major bread producers in Indonesia. Given the many types of bread sold, this study is limited to five categories of products. Using several time-series forecasting methods including moving average, exponential smoothing, multiple regression, ANN (Artificial Neural Network), and SVR (Supports Vector Regression) method. Forecasting bread demand using the best method, which is the method that produces the smallest error value. Forecasting error methods used are Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), and Mean Squared Error (MSE). This research empirically proves that store classification combined with seasonal factors such as weekends, public holidays, and pay periods has an influence on forecasting accuracy.