For the next couple of blogs I want to turn to the subject of business forecasts and how to make them more accurate. I also want to cover the role of forecasting systems such as Financial Driver, and what can be done to improve the forecast process.
Most organisations don’t have a problem with collecting a forecast, after all they just ask managers to submit what they think will happen over the next 3 months. Everyone will have an opinion. But what organisations struggle with is the accuracy of those forecasts, as critical business decisions will be based on them. If the forecast is not grounded in reality, then who knows what damage can be done to both the organisation and the way in which performance is managed.For the sake of this series of blogs, a forecast is a short-term view of what is most likely to happen, irrespective of the targets that may have been set. It takes into account the resources available as depicted in a budget, and sees the business environment in an assumed state.
The accuracy of forecasts depends on a number of factors, one of which is luck. Just because we have a good feel for the market and our role in it, doesn’t mean to say we can actually predict what will happen. At best we can get a good idea of what may occur, but even then the forecast could be completely wrong as future performance will depend on factors that are both unknowable and that can’t be controlled. For example, the impact of the weather, comments made on social media, or the actions of a competitor can all greatly impact an organisation’s performance, but that doesn’t mean to say we shouldn’t forecast.What forecasts do is to give an indication of the most likely outcome, within an assumed business environment. For this reason, forecasts should always be a range of values as this isn’t an exact science. However, what is important is that forecasts should be the subject of reason and backed up with some form of empirical evidence.
With this in mind, forecasts can be greatly improved by:
- Involving more, relevant, people in the process
- Challenging assumptions on what drives performance
- Collecting the detail behind the numbers (workload, resources and outcomes) to give management a more comprehensive story
- Producing trends to see if submitted forecasts are logical
- Allowing managers to determine the variability of individual forecasts