US navy has developed something similar with beta statistical distribution. You estimate "Optimistic", "Most likely" and "Pessimistic" time estimates for each task in the project and then use beta distribution on it. Some tasks take way longer than estimated.
I used it in the past and I think it is a good framework to discover uncertainty and make it more visible, because it makes you talk about optimistic and pessimistic cases.
The estimates are also good enough to come up with a draft schedule.
I do this but only with best and worst case. Any estimate with a worst case of more than 5 days need to be broken down again unless it's a known quantity. Thanks for the link, I had no idea this was part of 6 sigma
I find this approach very interesting, but it hinges on the assumption that project completion times follow a beta distribution. What's the basis for that?
It may reflect the observations in OP kind of well -- with one difference: it assumes we're good at estimating the mode, not the median. But other than that, within the range we're talking about (1 < alpha < beta) it has somewhat similar shape to the lognormal distribution.
The three things that still bother me about that idea are:
1. I haven't tried fitting it to the dataset in OP;
2. It's bounded to the right, which seems unrealistic;
3. I haven't come up with intuitive interpretations for the alpha and beta parameters in this context. If the beta distribution means something, then its parameters must have natural interpretations as well.
I've seen a similar model that I've adopted for my own projections which is 30/60/90 - the 30% chance scenario which is highly successful but less likely, 60% chance scenario that is good with a relatively decent chance of success, and the 90% chance scenario that is the lowest outcome but will almost certainly happen.
Here is the link to the time estimation described above with Beta distribution. https://www.isixsigma.com/methodology/project-management/bet...