To make it easy to identify non-value adding areas, you can build a simple application using SAS® Visual Analytics software. Such an application lets you point and click your way through the organization’s forecasting hierarchy, and at each point view performance of the Naïve, Manual, Statistical, and Automated forecasts (or whatever different types of forecasting methods you are using). This makes it very quick and easy to identify where forecasting methods are not adding value, so these areas can be investigated. SAS Visual Analytics provides a much better environment for doing this kind of analysis than trying to do it in Excel.
The value of an automated (or largely automated) forecasting process is threefold:
- It can significantly reduce the amount of time spent on manual forecasting. This frees those resources currently engaged in forecasting to spend more time on sales and customer service activities that can increase revenue and customer satisfaction. Analysts can focus their efforts on the most important, or most problematic forecasts, and let the rest run on auto-pilot.
- Automated forecasting will generally create more accurate forecasts. In many areas accuracy will be significantly better than the Naïve forecast, although sometimes accuracy will just be comparable to the Naïve. In areas where Manual forecasting does well, because the forecasters have information not available (or not suitable) for computer modeling, then manual adjustments can still be made to the statistical forecast. But automatic forecasting greatly reduces the need for manual efforts – only requiring them in areas where we have determined they are adding value.
- Automatic forecasting should create unbiased forecasts. Manual forecasting processes often have significant bias to over-forecast (forecasts higher than the actual sales turn out to be, due to wishful thinking, or to drive higher inventory and not leave unfilled orders). This adds costs in excess / obsolete capacity and supply. Some organizations have a bias to under-forecast, which might lower inventory costs, at the risk of poor customer service. Automated statistical forecasts should be virtually unbiased – neither chronically too low or too high compared to the actual sales.
In large-scale forecasting situations, automatic forecasting is not just an option, it may be a necessity. It is not uncommon for a retailer, having thousands of items sold at hundreds of stores, to have over a million store/item forecasts they want to create. But no company, in any industry, can afford the army of analysts needed to manually build a million+ forecasting models.
When you combine FVA analysis with the large-scale automatic forecasting capabilities in SAS Forecast Server (and larger-scale capabilities when you also utilize SAS® Grid Manager), even the largest enterprise can efficiently generate quality forecasts, and focus analyst efforts where they are most needed.