4 ms·
The fact that planning estimates represent a probability distribution is well known and there is an established process (PERT)[1] for estimating the expected ti
by _paulc 11y ago
The fact that planning estimates represent a probability distribution is well known and there is an established process (PERT)[1] for estimating the expected time of a set of estimates - essentially instead of asking for a single 'most likely' estimate you should as well explicitly walk through some of the risks around this and the ask for an 'optimistic' estimate (which is frequently very similar to the initial estimate) and a 'pessimistic' estimate (which is frequently much larger) - given:
m = 'most likely' time
o = 'optimistic' time
p = 'pessimistic' time
You can then estimate the expected time 'e' by modelling a triangular distribution based on these and sum the estimates on the critical path.
e = ∑(oi + 4mi + pi)/6
This is actually a very useful technique but sadly is done very infrequently (probably because it usually comes up with an number that people don't want to hear - but is usually much more realistic).
[1] https://en.wikipedia.org/wiki/Program_evaluation_and_review_technique https://en.wikipedia.org/wiki/Program_evaluation_and_review_...
The program (or project) evaluation and review technique, commonly abbreviated PERT, is a statistical tool, used in project management, which was designed to analyze and represent the tasks involved in completing a given project. First developed by the United States Navy in the 1950s, it is commonly used in conjunction with the critical path method (CPM).
- andrebaaij 11y agoAs a side project, a friend and I are developing a probabilistic scheduling tool "Probaplan". It not only uses PERT but also incorporates risks for more accurate scheduling. Using the planning, risks and PERT distributions we simulate the project in a monte carlo simulation. The result is a distribution of simulated end dates, these dates can be used to say: The probability that the project is finished before June 2016 is 80%. If anyone is interested in trying out the beta version, drop me an email at andre /at/ thebroadbaycompany.com
- netghost 11y agoThe typical PERT three point distribution is better than assuming the mean, but does have a few drawbacks when modeling joint probable outcomes. I honestly can't remember if it over our under represents the optimistic or pessimistic result. The other quirk is that it breaks down when you map from effort or work to duration, the actual time something takes when you factor in availability, interruptions, communication costs, etc.
- _paulc 11y agoI think that the real value is in forcing people to think about optimistic vs worst case estimates - in my experience the initial estimate is almost always actually the optimistic number and when they start to think about risks people tend to become much more conservative.
- danieltillett 11y agoIs there any reason to use a triangular distribution these days? I can see it made sense in the 1950s, but now it should be trivial to use a more accurate distribution.
- _paulc 11y agoProbably not, but I would guess that the difference is well within the error margin of the original estimates.
- vitd 11y agoI'm in an organization that uses this method, and I don't think it helps. I believe our "optimistic" metric is supposed to be something like "We have a 10% chance of hitting this goal," our "realistic" metric is supposed to be something like "We have a 50% chance of hitting this goal" and our pessimistic is "We have a 90% chance of hitting it". (Or something like that. I don't remember the exact values off the top of my head.) But what happens is the engineers estimate what they think it will take, claim that's the "realistic" option, then add or subtract some percentage from that for the pessimistic and optimistic values. Nobody really understands how (or if) it's supposed to work, so the estimates are still bad.