Forecasting for the long term in practice
A value-driver-based forecast model mainly supports decision-making based on tactical options and does not consider strategic planning to focus on what’s beyond. A forecast model alone is not sufficient to improve forecasting accuracy in the context of high uncertainty and should therefore be part of a multi-stage approach.
- Use economic diligence analysis and statistical testing to identify and prioritise variables as key business and value drivers. Test your forecast model for accuracy and predictive power.
- Use artificial intelligence (AI) combined with business and value drivers to define scenarios and predict the potential evolution of these key drivers to create forecasts and different scenarios of company KPIs.
- Update inputs, review scenarios on a regular basis and repeat this process frequently to counteract uncertainty and to improve the accuracy of your business and financial planning.
Harvesting the power of data analytics and even AI
The ability of people to analyse the enormous input of data is limited. Human information processing can only produce a small number of scenarios that are relevant to current events. Observing and analysing key business and value drivers requires in-depth industry knowledge, significant analytics skills and awareness of the forces that impact specific industries and businesses, as well as a keen historical perspective and some degree of foresight.
Big data, and a step further, artificial intelligence overcome human limitations. Moreover, using data analytics in scenario planning is essential in identifying extreme, yet possible, risks and opportunities that companies usually not consider or include in their daily operations.
Data analytics and artificial intelligence:
- Modernise strategic planning by shifting from annual or quarterly cycles to on demand as events occur.
- Provide a variety of often unexpected potential future outcomes.
- Accelerate creation of these outcomes from four weeks to a few hours.
- Promote a diversified point of view by eliminating natural biases associated with risk planning.
- Encourage exploration thanks to an immense capacity to rapidly analyse and extract business and value drivers from a myriad of sources.