Hi Steve -- As you are trying to communicate to your leadership, with just one point (or even a small number of points) any FVA results could be due entirely to chance. So you definitely wouldn't want to draw conclusions about "value added" by your forecasters with so little data. Donald Wheeler's short book "Understanding Variation" has a lot of good examples and guidance for management analysis and reporting, and I highly recommend it. I also have a white paper "Forecast Value Added Analysis: Step-by-Step" that goes into more detail on FVA data collection and reporting. This is available on the SAS Forecasting website: http://www.sas.com/technologies/analytics/forecasting/index.html#section=6. I don't have hard rule about the number of observations necessary, but what you are trying to do is determine whether you can reject the null hypothesis that FVA = 0 (i.e. the forecaster override has no impact). For example, in one month the FVA could be positive or negative entirely by chance. If FVA is positive for 3 or even 6 straight months you may start to feel that value is being added, but I still wouldn't draw any firm conclusions since it is still possible that this occurred by chance. However, if you track data for a year, and your FVA is positive for 11 or 12 of those months, that is much stronger evidence that the forecasting is making a positive difference. Wheeler's book can help you come up with good rules to use for your data.