The strength of those working in FP&A often comes when they worked in different industries or with BU’s from different countries. They learned a little bit more about the impact management can have on the numbers under different circumstances. To develop a long-range forecast, financials need to look beyond current events and steer away from business plans based on extrapolation.

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.

Takeshi Murakami, Group Controller at Microsoft, shares an interesting case study on leveraging AI/ML in decision-making. Microsoft Finance enhanced forecast accuracy by using ML instead of the traditional bottom-up process.

The democratization of technologies is underway. Tools like machine learning (ML), which were confined to universities, hedge funds or investment banks just until a decade ago, are now finding their way into industry-wide applications. The finance function is set to reap the benefits of this democratization wave.