Planners and managers in supply chain organizations are accustomed to using the Mean Absolute Percentage Error (MAPE) as their best (and sometimes only) answer to measuring forecast accuracy. It is so ubiquitous that it is hardly questioned.

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.

As far as I know, we are not legally required to forecast. So why do we do it? My sense is that forecasting practitioners rarely stop to ask themselves this question. This might be because they are so focussed on techniques and processes.

The gold rush is a defining part of Silicon Valley. The gold of today is data, and many solutions are rushed to the world market from a small radius around Princeton University. On the other side of the Bay lies the University of California, Berkeley, a place of the Liberal Arts in contrast to the technology-driven Princeton.

One can find many definitions of financial analysis. Investopedia defines financial analysis as “the process of evaluating businesses, projects, budgets and other finance-related entities to determine their suitability for investment.”

One of the realities that FP&A professionals need to realize is people tend to be too optimistic in their financial plans.  People tend to expect higher revenues, lower expenses, or less time to recover the amounts of their investments.  Psychologists label these expectations as optimism bias. As an accountant, I am guided by the conservatism principle.