The persistence of the current economic uncertainty (5 years now and counting) seems to have heightened the importance of forecasting accuracy. In fact there seems to be more articles and blogs on forecasting now than there are on budgeting.
Given the sophisticated budgeting solutions available today, it is quite possible to go into overkill mode with forecasting, asking the business for more detail, such as adding extra dimension to line items such as revenue; and asking for it more frequently aka monthly rather than quarterly. My implementation colleagues tell me that unless you can demonstrate clear benefits, such approaches are typically poorly received by business users so they need to be handled with kid gloves. So what to do?
As a first step, I would look back at past forecasts and identify those areas of the business where there is an obvious need to improve forecasting accuracy; most likely the revenue generating folk or those responsible for big chunks of variable cost. Either way I’d be looking for the handful of line items and a few responsibility centers that typically account for most of the historic variance.
Then I’d work with them to identify ways to improve forecast accuracy that didn’t overburden them with additional work – believe me they will have good ideas of their own to contribute:
- Perhaps adding greater dimensionality to a few key line items such as revenue, (ie by key account, or by product).
- Possibly automating some of these important line items, by incorporating business rules that are easy to update as well as upstream drivers such as the number of sales orders in the pipeline and the like.
- Forecasting these important line items more frequently, but again trying to make it a light-touch exercise that is supported by workflow so it is quick and easy to complete.
In a recent posting, my colleague Lance Holbert suggests setting up a Forecast Accuracy Review Committee and I guess we are talking about the same idea with the folk involved in the workgroup I‘ve suggested above. They are the ones responsible for the variances and surely they should have some flesh in the game.
Like many things in life, absolute accuracy is unattainable and improving forecasting accuracy is subject to a law of diminishing returns with a tipping point beyond which it’s really not worth going. Surely the key thing is to avoid surprises that would compromise the business in some way – i.e., cashflow, investor confidence, profit warnings etc – not pursuing six sigma forecasting for its own sake.
Whichever approach you choose to adopt, SAP’s financial performance management solutions will undoubtedly help.