Interesting Estimation methodology

Just sat through a very interesting talk by Jan Pries-Heje from the IT University of Copenhagen.
I liked the model they proposed - a combination of traditional use case points based estimation combined with what they call the "successive method". The slightly oddly named succesive method is simply this :

- Try to quantify uncertainty for every use case by making a range of estimates from most likely, optmisitc and pessimistic.
- use a beta distribution to estimate a mean and standard deviation of the individual use cases and then the combined project.
- focus on the use cases with the highest variance( i.e maximum uncertainty) and then drill down further.

They have a 'stopping' heuristic that serves as a guideline for when to stop drilling down.

The estimation looks pretty similar to the PERT process though I'm not sure if the formulae are exactly the same - but it's pretty much PERT.

The crucial thing here is that it allows the quantification of uncertainty which is not often done in project estimation. Especially important for vendors who submit fixed bids based on estimates. the range of uncertainty that this method spews out ( rather than just one number - the Estimate). This allows the bidder to make a clear decision to either decide that the project is too risky to bid for or to use the uncertainty ( the variance) to decide on the risk premium they can put on their costing to make sure they are being sufficiently compensated for the risk. The key is that risk is quantified and factored into the estimate and that is amost without qualification a good thing.