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.

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.

A rolling forecast is not only about seeing the future unravel, but a constant evaluation of the management team to see if they are able to adjust their operations on time. Without it, any form of strategic planning becomes useless. Below you find a real-life case. Step-by-step each question will be briefly discussed. It is about a foreign business unit, which was part of a large European corporation, on the brink of a crisis.

In this article, Steve Morlidge argues that the quality of business forecasting is unacceptably poor. He goes on to present six simple principles that will help executives significantly improve the performance of their forecast processes.

Disclaimer: Financial Modelling has no strict “right” or “wrong” method of application. It does, however, have forms of best practice and this what this article attempts to highlight