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

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.

## No comments:

## Post a Comment