Why, when everyone hates it, do we still have traditional budgeting? My tentative answer to this would be that most people were not aware of the alternatives.

If measurement – or the lack of it – is the biggest weakness in most forecasting processes, risk is the least well understood concept…mainly because it is something that we think we understand, but we don’t. In my view, there are three major sources of this misconception.

About the only thing that everyone seemed to agree on in my old company was that forecasting was really important and that our forecasts were poor. I looked in the corporate controller’s database for a definition of what constitutes a ‘good forecast’. But I got zero hits.

In my last blog, I promised to give you some tips about how to introduce some Beyond Budgeting ideas into your business to help make it more flexible and reduce the gaming behaviour associated with the budgeting game. Traditional budgeting has three major weaknesses:

In the last blog, I described how it is possible to implement Beyond Budgeting in a step-by-step fashion. If you choose to go down this route you will become increasingly uncomfortably aware of a disconnect between Beyond Budgeting style processes and the rules and routines that govern normal business life in a traditional organisation. In the brave new world I have described, senior managers perched high in the organisational pyramid can no longer use targets and incentives to remotely control the activities of their subordinates and measurement systems no longer highlight deviations from pre agreed plans and budgets and trigger ‘corrective action’.  Resources are allocated continuously and an annual set piece planning ritual cannot effectively coordinate activities in an organisation that is continuously adapting to events.

Analytic models are rarely static. Their aim is to model the organisation in such a way as to allow managers to investigate what is actually going on and to assess changes to the way it operates.

FP&A practitioners rely on raw materials, i.e. data, to conduct their work. In order to conduct their work a variety of data types should be utilized. There are three types of data that FP&A practitioners should utilize. The first type of data is financial numbers. Financial numbers must be utilized due to the goal of FP&A. The goal of FP&A is to assess whether wealth is or will be created based on decisions within businesses. These decisions can be assessed through elements within financial statements.  Income, stockholders’ equity, and cash are the elements most commonly used to assess wealth. These elements are expressed through financial numbers so it seems foolish to describe their importance but it is necessary to do so. This is due to the role of other data types.

In July 2017, I presented at the 2017 AICPA FP&A Conference in Las Vegas over AI (Artificial Intelligence) & machine Learning impact on FP&A. The session received great feedback from attendees and other speakers. So, I wanted to share an article summarizing the presentations main points. The goal of this article is to provide insights into the impact AI & machine learning will have on people, processes and technologies in FP&A. Hope you enjoy!

In July 2017, I facilitated 5 meetings of the International FP&A Board in Asia and Australia. It was an amazing journey to 3 countries, 5 cities, and two continents. I was on the road for one month and met so many passionate FP&A professionals in Kuala Lumpur, Singapore, Perth, Melbourne and Sydney. Every meeting was a great success.