1. Improving your forecasting process requires the ability to track accuracy.
Forecasting should be viewed as a continuous improvement process. Your forecasting team should be constantly striving to improve the forecasting process and forecast accuracy. Doing so requires knowing what is working and what is not.
For example, many organizations generate baseline forecasts using statistical approaches and then make judgmental adjustments to them to capture their knowledge of future events. Organizations that track the accuracy of both the statistical and adjusted forecasts learn where the adjustments improve the forecasts and where they make them worse. This knowledge allows them to focus their time and attention on the items where the adjustments are adding value.
2. Tracking accuracy provides insight into expected performance.
A forecast is more than a number. To use a forecast effectively you need an understanding of the expected accuracy.
Within-sample statistics and confidence limits provide some insight into expected accuracy; however, they almost always underestimate the actual (out-of-sample) forecasting error. This is due to the fact that the parameters of a statistical model are selected to minimize the fitted error over the historic data. The parameters are thus adapted to the historic data, and reflect any of its peculiarities. Put another way, the model is optimized for the past—not for the future.
Generally speaking, out-of-sample statistics (i.e., historic forecast errors) yield a better measure of expected forecast accuracy than within-sample statistics.
3. Tracking accuracy allows you to benchmark your forecasts.
If you are lucky enough to be in an industry with published statistics on forecast accuracy, comparing your accuracy to these benchmarks provides insight into your forecasting effectiveness. If industry benchmarks are not available (usually the case), periodically benchmarking your current forecast accuracy against your earlier forecast accuracy allows you to measure your improvement.
4. Monitoring forecast accuracy allows you to spot problems early.
An abrupt unexpected change in forecast accuracy is often the result of some underlying event. For example, if unbeknownst to you, a key customer decides to carry a competing product, your first indication might be an unusually large forecast error. Routinely monitoring forecast errors allows you to spot, investigate and respond to these changes early on—before they turn into bigger problems.