Friday, January 11, 2013

The Schedule – Improving Estimate Confidence

“My own idea is that these things are as piffle before the wind.

                                                                                                - Daisy Ashford

My previous post on the probability of schedule success, as pessimistic as it was, still left something hanging.  I talked about estimating an individual task to a 50% or 90% confidence level.  Is that just bunkum or is there a way to really get that kind of confidence?
Well, there really is, but a stated confidence level is just a piffle unless the techniques described in this post are used.  My post today is how to really get that level of confidence and how to recognize when it’s real.

The first step is to produce three estimates for each task.  This is not the same as Delphi estimating method, getting estimates from three experts.  Instead we’ll use the PERT three-point estimate, as mentioned in The Schedule – Estimate Task Effort.  For this exercise, the task estimator produces three independent estimates:  an optimistic estimate (O), a pessimistic estimate (P), and a most likely estimate (L).  The expected time is then calculated as E = (O + 4L +P) / 6.  So, if the optimistic estimate is 3 days, the likely estimate is 5 days, and the pessimistic estimate is 13 days, the expected time is (3 + (4 * 5) + 13) / 6, or 36/6, or 6 days.
Next, calculate the standard deviation using the formula SD = (P – O) / 6, or 1.67.  From these two values, we can now produce an estimate to the desired confidence level.

So, the 50% confidence level is determined using the expected time calculation above, or 6 days in this example.
The 84% confidence level is one standard deviation (4.33 – 7.67 days, or, rounding up, 5-8 days).  A confidence level of 98% is achieved by going out two standard deviations, or 3-10 days.  You can even get to a better than 99% confidence level by going out three standard deviations (1 – 11 days).

However, this only gets the individual tasks up to the target probability of success.  As noted in my previous post, the project’s probability of success is determined by multiplying the probabilities of the critical path tasks.  If you have ten critical-path tasks in your project and you estimate each task to a 95% confidence level (two standard deviations), your project still only has about a 62% probability of success.
To get the project to a 90% probability of success, you have to get each task up to a 99.7% probability of success (three standard deviations).  (This will actually get this example project up to a 97% probability of success.)

In my next post, I’ll provide some example numbers to demonstrate just how significant this is.  Which will also show why we as PMs woefully underestimate our projects, why we have such a dismally Chaotic success record, and why attempting to fix it will receive push back from our stakeholders.
CAVEAT:  This math works for standard distributions (you know, those bell curves).  Task estimating is not a standard distribution; the curve is compressed on the left and stretched on the right.  There is a formula/ process for converting a non-standard distribution to a standard distribution, but, even without that, what I’ve described above is still a vast improvement over the typical schedule twaddle.

How often do your projects complete on schedule.

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