Accurately forecasting costs, sales, user growth, patient readmission, etc is an important step to providing directors actionable information. This can be difficult to model by hand or in Excel. In addition, using traditional methods like moving averages might not provide enough insight into the various trends and seasonality.
Using models like the ARIMA and ETS provides analysts the ability to predict more accurately and robustly by considering multiple factors like seasonality and trend. What is even better is that languages like R and Python make it much easier for analysts and data teams to avoid all the work they would usually have to do by hand. This can reduce the time to develop a model by more than half and increase accuracy.