A colleague of mine recently sent me a blog post explaining the difference between project contingency and padding. The blogger made the distinction that padding is what often gets added to an individual’s estimate of the effort required to perform a task (in her example, a software development task) to account for project ‘unknowns’. The estimator determines the most likely required effort, then pads it with a little more effort in order to arrive at an estimate to which he or she can commit. Thus, padding represents an undisclosed effort reserve (and implied schedule reserve) to buffer against potential risk. Contingency reserve, she explains, is “an amount of money in the budget or time in the schedule seen and approved by management. It is documented. It is measured and therefore managed.” Ms. Brockmeier is correct in promoting contingency as the better management tool. The challenge is having a method to measure and document this contingency and the known unknowns it is buffering.
Implicit Risk Buffer
Padding is a natural result of bottoms-up, effort-based estimation techniques. Estimating low-level WBS elements creates more opportunity for padding, because the number of unknowns grows with the task list. The estimator is consciously or unconsciously assessing the risk of each task, considering its dependencies and complexities. What is being implied in the effort estimate is: 1] an assessment of product size and complexity, and 2] a productivity valuation.
Explicit Risk Buffer
QSM’s SLIM-Estimate tool provides an alternative approach that eliminates padding by taking a top-down view of the project. At the project level, as opposed to the task level, the vast majority of unknowns are attributed to just two factors: the product size, and team productivity. Using project productivity values measured from past projects, plus an estimate of the entire product size, SLIM calculates the total effort and duration needed to complete the project. Risk is explicitly calculated using expected values for size and productivity (along with uncertainty ranges for each input) to produce a probability distribution of effort and duration outcomes.
Read more about how to explicitly set and manage contingency.