In many companies, rolling forecast projects are on the rise as the understanding of the need for faster planning cycles increases. This is due in part to the numerous crises and changes that have made the business environment increasingly unpredictable in recent times.
A rolling forecast is not only important for being able to quickly respond to changes in turbulent times, but also to improve planning quality through continuous variance analysis and plan-to-actual comparisons.
Simply repeating the traditional annual planning is not enough to achieve the desired effect. Forecast models are often too complex and opaque, so the information is already outdated by the time it is completed. In addition to accuracy, new parameters for planning quality should be considered: robustness, resilience, and speed. In all these aspects, many planning systems — especially during the COVID-19 pandemic — completely failed.
Check drivers for causal logic
Unforeseeable events not reflected in historical data can only be represented in planning models if causal relationships are correctly captured. Models are too often calibrated too precisely to current conditions and no longer work under major changes in circumstances. It is advisable to keep the number and complexity of planning parameters low — unnecessary details increase planning effort and uncertainty in relationships.
Use automation and modern technology
A comparison and linkage to actual figures should always be maintained to continuously work on planning accuracy and conduct variance analyses. Modern technologies and automation should be used to create forecasts in hours instead of days — leaving more time to engage with the results. To find a good starting point, first try to replicate the past with the rolling forecast model.
Does this sound familiar?
If your forecast costs more effort than the certainty it delivers, let's look together in a free first conversation at which drivers actually carry it.
Measure robustness through stress testing
Through consistent improvement of assumptions and driver logics, as well as fast and automated data processing, a forecasting system can become very robust — meaning it delivers meaningful results under all circumstances. Robustness can be measured by stress-testing the model: individual parameters or combinations deviate unusually strongly from expected data, and experts assess whether the real company would behave the same way in an exceptional situation.
A robust forecasting system delivers meaningful results under all circumstances — not only in calm times.