"Try not to
become a man of success, but rather try to become a man of value.”
-
Albert
Einstein
I completed the series on the
well-formed schedule, but the final post, on
adding risk buffers, was pretty light.
I want to take a couple of posts and expand on the topic of risk-based
or risk-adjusted scheduling, which also will start some interesting metrics discussion.
How tough is the job of project management? Planning a new project, the PM prepares a
schedule that documents months in advance a completion date. What is that PM’s probability of success?
It turns out that this is a fairly easy mathematical
calculation. If you have all of the
tasks estimated and know the probability of success for each individual task,
then you can calculate the project’s probability of success – the probability
of completing the project on schedule.
You merely multiply the probabilities of success for each task (in the
critical path) together.
For a simple example, if you have a 10-task project (each
task in the critical path) and you’ve estimated each task with a 50% likelihood
of success (i.e., equally likely to finish early or late), then the likelihood
of completing the project on time is 0.50 x 0.50 x 0.50 x … or 0.5010
or 0.09.8%. With a very simple project,
you have less than 1 in 1,000 likelihood of completing your project on
schedule.
Even if you upgrade the individual tasks to a 90%
probability estimate, you only have a 35% chance of success (1in 3) in this
example. And with each task you add, the
probability of success drops proportionately.
To emphasize the point: even when
you use aggressively conservative scheduling, you will miss the date more often
than you hit it.
In other words, we’re screwed before we start.
This sounds very pessimistic. What are the best practices that can improve
this performance? I explore that in my
next post. Stay tuned…
How do you generate estimates on individual tasks? What is your expectation for the task
success?
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