In the age of metrics and measurement, what are we doing to measure the satisfaction or happiness of those involved in FP&A? When organizations have low employee engagement, they incur additional costs related to the overall drain on productivity, turn-over, etc.., but more importantly companies are missing out on the upside potential to grow and compete based on the creativity, innovation and hard-work invested by an engaged workforce. 

Today’s FP&A practitioners are highly trained professionals with a greater ability to see the big picture, analyse and interpret data, and build predictive models. They are also experts in harnessing the power of information technology. They are able to create detailed cost and revenue databases that unlock patterns and trends in business behaviour and to build sophisticated and responsive forecasting models. We do rolling forecasts because we know they are better and because we can.

Sometimes, what you forecast needs to change dramatically, due to e.g. market disruption or internal changes. You also might not monitor every business the same way, because each might be in different development stage or ´situation´. By looking at the company itself, but also possible (management) crises, you can determine what the focus of the forecast should be. 

Forecasting new product launches are a tricky business with plenty of emotional baggage. They are also often, inevitably, wrong. This blog argues that when commercial finance or FP&A professionals are involved they should focus equally on model flexibly as well as the outcome.

Being critical of one’s own work, is even more important for the financial doing the forecast. A forecaster will undoubtedly have his or her bias and blind spots. However, some can be avoided by looking at the forecast itself, and some by looking at person doing the forecast. The aim here is to create deeper awareness of ‘forecasting’ by presenting some structural elements.

The pressure of globalization and agile decision-making requires companies to improve their business modeling.  They must integrate big data in real-time, synthesize that data to identify causal relationships and value-drivers, and ultimately use the findings to make high-impact business decisions.