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.
FP&A Insights
FP&A Insights is a collection of useful case studies from leading international companies and thought leadership insights from FP&A experts. We aim to help you keep track of the best practices in modern FP&A, recognise changes in the ever-evolving world of financial planning and analysis and be well equipped to deal with them.
Stay tuned for more blogs and articles from great authors.
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!