Today, we continue this conversation and examine the estimation process.
There are three basic methodologies used for estimating, with some variants.
The first is a parametric estimate, where we know two or three parameters of a project and we extrapolate a range from those parameters – which are usually tracked and tallied over 100’s if not 1000’s of similar projects.
These parameters can be anything from the number of classrooms to be installed (including projectors, displays, smart boards and controls), to square footage (if building a house), to a number of stories (if building a skyscraper), to the weight and speed of an aircraft. The range of a parametric estimate is typically -25% to +75% from the basis point but can be higher (software development projects can have an initial variance as wide as -50% to +400% at their inception).
The key is to determine the most important and meaningful parameters and then collect data that correlates to those parameters. This is the also the quickest form of estimation, but it can be risky if the parameters are not selected or measured correctly, or if the range is not respected and optimism takes over.
This method is usually used for a very preliminary estimate.
The second methodology is the analogy, or top-down, methodology.
In this method the estimating team looks at previous, similar projects at the work breakdown structure (WBS) level 2 or 3 and estimates costs based upon the likelihood that the current project will correlate to those of the past.
The key to this methodology is to understand the analogies, but you should also have an effort- and cost-tracking system that aligns with the Work Breakdown Structure (often a phase-based approach). Using of a common site-survey form will help develop meaningful analogies across projects.
Many times, I’ve seen companies use the analogy methodology with no relevant historical data, in which case they’re really just guessing. Or worse, they’re making up numbers and believing them to be true because “we always used those numbers”.
The range of an analogy estimate is typically -10% to +25%, but often can be wider.
The third methodology is the bottom-up estimate, where actual data from a current project is used to extrapolate a forecast for the remaining work. It’s often employed in conjunction with the analogy methodology.
If we determined that 50 classrooms would take a day apiece (two techs, 16 labor hours, plus or minus two hours), and we found that the first four classrooms took 17 hours, we would use those numbers to move forward. Our range using the bottom-up methodology is typically -5 % to +10%. When it comes to forecasting within the bottom-up system, some companies, for example, will make a precise estimate of 16 hours per classroom.
When the first four classrooms take 18 hours, they then forecast — often erroneously — that the rest of the classrooms will take less time, based on the learning curve, and that crews can speed up enough to make up for the overage of the first four classrooms.
I’ve found that established trends usually beat out wishful thinking.
It also means that valid and numerous assumptions and relevant historical information are critical when using estimating methodologies in situations where the estimate needs to have smaller variation (which we will discuss in Part IV of this series).
These methods may help you win jobs, but they can also help bankrupt the AV company.
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