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 challenging the assumptions on what drives performance. In this blog I’m going to look at the third key of collecting the detail behind the numbers.
As mentioned in my last blog, forecasts are an assumption about what may happen in the future. They are based (or should be) on the actions being carried out by the organisation, and their consequences. And as discussed last time, there is a relationship between resources being applied, the workload being carried out, and the outputs that are produced. Because of this, it should be possible to provide a ‘story’ behind any number being forecast so that management can make a judgement as to whether the figures being entered are likely to be achieved. The forecasts we are talking about are those that are variable. Items such as rents, rates and maybe salaries are typically fixed months or years in advance and so these can be entered centrally from supporting systems. What we are concerned with here are the variable costs that have a direct impact on corporate goals. Quite often forecasts are entered under pressure. For example, if a manager enters a sales forecast that is under budget, then the fear is that senior management will take them to task for not doing a good job. Similarly, if the number is above budget, then there is a fear that targets will be increased in future months. In other words, the forecast can have adverse implications for managers on anything that varies from the budget, and so it’s safer to enter the budget as the forecast. This of course is crazy but it should be recognised that this type of consequence can lead to inaccurate forecasts if not addressed.
Putting political pressure aside, what senior managers really want to know is what is most likely to happen, as this allows them to be better prepared should the forecast turn out to be right. The only way to assess reality is to look at the detail behind the numbers. For sales forecasts this may include collecting prospect situations, such as the date of the last visit, whether a proposal has been sent, the likely chance of success, and details of the competition. Similarly on expenses, knowing planned marketing campaigns, customer visits, etc., can help management assess whether the level of activity being supported is ‘reasonable’ and likely to lead to the outcomes being forecast. To do this, the forecast system must be able to handle defined levels of detail or take its input direct from a supporting system such as SalesForce.com. That detail should provide support for text, dates and notes that can be directly attached to individual forecasts. This should then be available to management to view from reports that show forecast numbers. It's worth pointing out that Financial Driver has such a capability, which we call Detailed Data Tables, or DDT for short. In my next blog on this topic I will look at the fourth key to accurate forecasts of analysing forecast trends.