Driver Based Budgeting

Driver based budgeting is a great way of reducing the volume (and hence time) of data entered when creating a budget, while at the same time provides a way to circumvent ‘game playing’. In most organisations, income is directly impacted by sales, which itself is driven by other business activities. The same thing happens with costs, with each supporting expense activity being an effective predictor of many other business variables. For example, the following schematic shows the relationship between profit, revenue, costs and the activities carried out by staff. Items that appear at the end of the chain are known as ‘drivers’. From this a mathematical model can be built within Financial Driver that can be used to predict future values based on entering the driver values.
Example of a simple model that shows the relationships between drivers (in yellow) and net profit.

These models can be used to challenge relationships implied within a budget or forecast, or they can be used to actually generate submissions.

The above example is a simple one, however Financial Driver rules can be more sophisticated with the use of ‘IF’ conditional expressions and ‘LEADLAG’ that can test values in future or prior periods.

Although these rules may be used to generate future values, they can be restricted or altered between versions of data. For example, the rule ‘Revenue = Price x Volume’ can be used to set budget revenue based on price and volume, however, when reporting actual data, the model can accept revenue and volume values from the general ledger, and then work out an average price from these two numbers.

Finally, Financial Driver has a global value capability that allows administrators set values such as tax rates and prices, that are then used throughout the organisation, without the need for these to be entered by each unit.

Implementing a driver-based approach to budgeting / forecasting using Financial Driver can have a significant impact on reducing the time your organisation spends in creating data, which will also improve the realism of submissions.