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 involving more people and the importance of knowing what is likely to happen as opposed to what is desired. In this blog I’m going to look at the second key of challenging assumptions on what drives performance.
Forecasts are assumptions. They assume a given level of resource will be applied to support a certain level of organisational activity, and in response there will be a level of customer response. As such there is an implied connection between resources, workload and outcomes. These implied connections can be used as the basis of a driver based business forecasting model where they can assessed and challenged. In order to do this, cost and revenue forecasts should be split into two types:
- Those that are relatively fixed such as rent, rates, loan repayments, and annual maintenance revenues
- Those that are variable and dependent on other things. For example, raw material purchases and some manufacturing costs, are often dependent on the volume of products made, which itself could be dependent on the volume of product ordered.
Every organisation achieves its aims through a series of connected business processes. For example, sales revenue is the product of marketing, lead generation, sales calls, customer references and signing contracts. Similarly, production costs are the product of buying raw materials, fabricating them into products, packing and shipping them to customers. The linkages can be modelled so that entering information such as workload or targets to be achieved, are then used to generate the resources required. This is sometimes known as ‘driver-based’ planning. Drivers are found by taking a target measure (e.g. revenue or some other ‘outcome’) and establishing what directly impacts its value. For those items, we then establish what impacts them – and so on. Measures at the end of the chain are known as ‘drivers’. By entering data into a driver value allows the model to calculate the target measure. Sophisticated driver models recognise constraints such as production volume and that at certain levels cost and revenue profiles may change e.g. the impact of discounts, late delivery penalties, or that more staff will be needed which will cause a step change in values. They also recognise that there is nearly always a time-lag between the driver and the result it supports. Driver-based models are good for modelling the relationships between activities and can be used to quickly generate future outcomes, but without the time, effort and politics involved in setting these values. However, these models only work for certain measure such as costs/revenues that can be directly related to drivers. Other measures such as overheads will be required to get the full picture. Also they do not take into account unpredictable external influences such as the weather and they can only model what has happened in the past, which may not be a reliable indicator of the future in a volatile market or where product life cycles are relatively short. This linkage between resources, workload and outcomes allow management to assess whether forecasts are realistic and whether the relationships reflect the way business is generated. In my next blog on this topic I will look at the third key to accurate forecasts of collecting the detail behind the numbers.