Fuzzy Plans
“The most dangerous strategy is to jump a chasm in two leaps.” Benjamin Disraeli.
It’s October, and for many, this the month wherein our business plans go from aspirations to commitments and planned investments, or, what we will do and how much we will spend. It is at this juncture that many “stretch goals” are stretched out across time and spending with some promise of results and outcomes. In other words, we get money, people and kit in order to execute processes and projects for our enterprises. What are the probabilities that next year will be as we’ve planned? Well, how confident are we in our predictions and assumptions for next year? Does our plan plot out as a line over time?
One attribute of plans and budgets that I’ve frequently observed is the application of linearity, treating each month as a point on a line graph, not fuzzy at all. We feel fuzzy, but our system doesn’t want fuzzy as an input. How did we estimate or predict our planned inputs, processes, projects, and outputs? Reality is always fuzzy because randomness is such a big influence. When randomness is a part of multiple factors, it is a very safe bet to say that the plan and budget are not going to match a line.
There is one aspect of fuzziness in planning that can bite us. It has to do with the relationship between outcomes and consequences. Let’s assume that we have performance targets that we’ve committed to. These targets have measurable attributes that speak to time (cycle time and schedule), cost, and quality (defects and meeting requirements). Also, unless we’ve introduced some bias (made them bigger or smaller) due to timidity, overconfidence, bad math, or enforced optimism, our targets have some probability of being too high or too low. They have fuzzy, not certainty. Let’s assume for now that they have an equal probability of being higher or lower. What about the impact on the business if performance comes in higher or lower?
Often, the consequences of being late are far greater than the consequences of being early. Similarly, the consequences of spending less are not the same as the consequences of spending more. While the probabilities of an outcome might look like a familiar, like a normal bell curve, the consequences curve will not look anything like that. Multiplying likelihood by impact and then summing the values might well be a negative number. This is particularly true if we use historical data as a unit value to forecast future performance.
• Should we then introduce bias?
• What are the objectives of the plans and budgets?
• How do they affect what will happen?
• Are they static or dynamic instruments or tools?
• As time’s arrow moves ahead, does our view of the future shrink with what is left of the plan year or is the plan year always the next twelve months?
