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.
By Asif Khan, Global FP&A at PayU
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.
The first step in forecasting is to understand where we are today and how we arrived at that point from the past. This is gain through analysis and reporting.
Statistical approaches to forecasting can provide a framework for creating rolling budgets to which analytical skills and judgment can be applied in supporting a sound budgeting process.