The mean and moving average forecasting models are typically best for mature products.
1. **Mean Forecasting Model**: This model calculates the average of past data points to predict future values. It smooths out fluctuations in data to provide a stable prediction. For mature products, where historical sales data is available and stable trends exist, the mean forecasting model can be quite effective in predicting future demand or sales.
2. **Moving Average Forecasting Model**: This model takes the average of a specific number of past data points to forecast future values. It is useful for smoothing out short-term fluctuations in data. In the case of mature products with consistent demand patterns, the moving average model can help in making accurate forecasts based on historical performance.
Both these forecasting models are suited for mature products because they rely on historical data and trends to make predictions. In contrast, for innovative or new products, where there is limited or no historical data to base forecasts on, other forecasting methods may be more suitable.
