Most businesses are not well served by the conventional budget process and the resulting monthly reports and variance analysis. Failures are often incorrectly identified and managers hassled over insignificant issues that fail to address fundamental issues of profitable operations.
- Simple Expense v Budget evaluation fails to consider Volume Context.
- Annual Financial Budget is built on Dubious Forecast of Volume.
- Budget “Commitments” promote bad Can’t Invest / Must Buy preconceptions.
- Variances are often Failures of the Budget rather than Operational Problems.
Human nature doesn’t necessarily draw us to the correct answers, but rather inclines us to embrace either the most obvious or the most agreeable answer. When faced with ambiguity it’s enticing to assign higher value to the data that supports our preconceptions or desired outcomes.
Spreadsheets can be confusing. Context often requires some degree of analysis, but the simplest evaluation of “greater than / less than” is usually expedient. Unfortunately the simple evaluation is often misleading. In fact, the simplest evaluation is USUALLY MISLEADING.
How can that be?
Simplified, imagine a modestly random world with a perfectly forecasted budget, but with only minor fluctuations in operations results and minor fluctuations in volume context (sales, appointments, widgets, etc.). At face value half the random variations would fall above the perfect budget and half would fall below. A simply prima facie evaluation would see half success / half failure. That evaluation is only half complete.
There also exists a random variation of the context in each of these evaluations. Even if our proposed “perfect” forecast/budget has only modest random variation, each financial data evaluation requires appropriate volume context to claim authentic success. If expenses appear to be 2% below budget, that success only holds as long as volume hasn’t dropped more than 2%. If the factors of finance and context vary independently the probability of valid prima facie success is only 75%, and erroneous prima facie failure is likewise 25%.
So, that may not seem too bad. How do I make the claim that the simplest evaluation is usually misleading? Here’s where an element of human nature ruins our theoretical “perfect” budget. The introduction of any forecast bias, or financial hedging has an acute impact of the probabilities and the interaction of factors.
The mathematical modelling gets complex, and assumptions of independence are open to challenge, but the search for specific probability is not ultimately critical to our approach. The key take-away is that the substantial probability of mis-interpretation CAN BE ELIMINATED.
Simply base all budgeting, variance reporting, and financial analysis on ratios!
