Statistical forecasting is often used when there is a large amount of data, a low degree of uncertainty, or a need for accuracy and efficiency. For example, it can be helpful for inventory management, sales forecasting, or demand planning. Advantages of statistical forecasting include its objectivity and consistency, its ability to handle complex data, and its optimization of forecasting processes with advanced tools. However, there are also some drawbacks to using statistical forecasting. Data quality, availability, and relevance can all be limited. Additionally, it may not account for randomness, volatility, or outliers in the data patterns and trends. Lastly, it may not adapt to new information or scenarios that are not included in the data models and assumptions.