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Accurate business forecasts - Assessing variability of individual forecasts

In this series of blogs I’m looking at how to improve the accuracy of business forecasts and the role of forecasting systems such as Financial Driver.  In my last blog I looked at the evidence shown in trends and statistical analyses. In this last blog I will look at assessing the variability of individual forecasts.
Have you noticed that some people are good at forecasting while others don’t seem to have a clue?  Of course there is an element of luck when trying to predict the future, and it’s easy to explain why the future didn’t quite happen as previously forecast.  But there is one way you can judge a forecast and hence its accuracy, and that is by tracking the history of prior forecasts by person/department and measure. Today’s modern forecasting systems such as Financial Driver allow you to capture and hold prior forecasts.  Then as the actual results come in, the system is able to contrast these with previous submissions that can determine how far into the future a particular measure/department can be forecast with a certain level of accuracy.  For example it may show that departmental costs are on average within 5% of budget six months in advance, whereas sales may only be accurate 3 months in advance. This ‘average’ can then be used to identify which departments are ‘above’ or ‘below’ average.  If this shows the same department then there is probably a good reason.  For example it could indicate that the people involved don’t really know what’s going on.  Or it may be due to political pressure whereby they only forecast the budget as they don’t want to be the bearer of bad news.  It could also mean that some measures under their control just can’t be predicted that far into the future as they are dependent on variable measures outside of the their control. Whatever the reason, knowing that a measure or department cannot produce accurate forecasts is just as important as knowing what the true number is going to be.  This then signals that contingency plans are required to offset any performance that could jeopardise overall results.
In this series of blogs I have tried to outline ways in which organisations can produce more accurate forecasts and the benefits of a forecasting system.  These include: Interestingly, most organisations still use spreadsheets to collect and analyse forecasts, but as we have seen these do not facilitate many of the techniques we have covered.   Similarly, forecasting systems are only as good as the way in which they are set up, the truth of the data being entered, and the analyses that are applied to them. If you want to transform your forecast process, then make sure you choose a software vendor that has both the product and the expertise.  If you would like help in this area then do get in contact with me via the Contact Us page.