In those environments, range estimating helps to address the most common problems with developing both preliminary and detail budgets.

Glen is absolutely correct in noting additional concerns for projects done under contract, and especially when those projects are done under government contracts.

“Bill is NOT doing this”

But all the same trying to simplify may be useful for educational purposes, but I’ve seen nothing but grief when those class room examples are found on live programs.

Even at the recent PMI CPM conference, there were several example of “simplifying” how management reserve is used and performance calculated.

If DCMA (Defense Contract Management Agency) saw that, they’d be writing up a CAR (Corrective Action Request) and asking for the PM to example why she hasn’t read the ANSI-748B.

Let’s not let the classroom 101 example become the basis of program management.

The triangle distribution is best when there is not underlying probability distribution function (PDF).

The current guidance in A&D is to NOT develop the three point estimates as Bill suggests but to develop a ordinal classification system. This system defines the upper and lower bounds of the PDF for each risk category. Look at the DoD PMBOK (http://www.dau.mil/pubs/gdbks/DoDExtPMBOK–June%2003.pdf) for guidance here.

As well the seminal work for risk management and the topic Bill is presenting is Edmund Conrow’s “Effective Risk Management: Some Keys to Success,” 2nd Edition, AIAA Press, 2003.

The is a large body of estimating and programmatic risk management material out there that ranges from weak to wrong.

The best cost estimating sources are:

1. GAO Cost Estimating and Assessment Guide: Best Practices for Developing and Managing Capital Program Costs, GAO-09-3SP, March 2009.

2. NASA Cost Estimating Handbook, 2008. http://ceh.nasa.gov/ceh_2008/2008.htm

3. Join The Society of Cost Estimating and Analysis (http://www.sceaonline.org/) These are the folks that earned their living doing cost estimating.

Start with these and apply them in your environment. Ignore most of the other stuff for all the right reasons.

Read Ed’s book to see why what Bill suggests is not allowed in NASA or DoD. DID 81650 mandates Monte Carlo simulation as does the Basis of Estimate CDRL’s that accompany most contracts.

There is absolutely no reason these approaches can’t be applied universally to any project in any domain. There is no reason to “simplify” the approach. There numerous free and affordable Excel tools – Crystal Ball being my favorite for cost modeling.

I posted a link awhile back about coupling between WBS elements and its influence on the cost estimate.
http://herdingcats.typepad.com/my_weblog/2009/05/undercorrelated-wbs-elements-means-understated-cost-risk.html

If we’re going to go down the path of building credible “basis of estimate,” let’s look at how it is done in “high risk” domains.

There is a move afoot to try and simplify things, to essential take the hard work out of the work. EVM has such a move afoot. I personally see this as dumbing down what is a critically important process.

I’m saying Bill is doing this, but care needs to be taken here (estimating) and with EV, not assume you’re “doing ev” or “doing estimating,” when in fact you’re just playing in the sand box, while the real work remains outside.

As a final not, Bill’s last sentence is also not allowed at NASA. The distributions of the actuals MUST be correlated with the BCWS. Using the actuals to adjust the estimate is a one-side point estimate statistic against a two sided-estimator and pretty much meaningless for forecasting the future, since the correlation factor is lost.

This is the common disease of most of the non-technical world from Ophra to public policy.

The confusion between correlation and causality. We want to know the causality of the estimating so we can improve not only the estimate but the cause of the “over target baseline” outcomes. Knowing the correlation is nice of classroom discussion, but I can’t run the program with that information. I’m driving in the dark AND looking in the rear view mirror.

The first thing to understand is that the basic PERT assumption — that a beta distribution defines the possible outcomes for any work-item — is nothing more than an assumption. A couple of analysts from the RAND Institute decided way back in the early 1960s that this would be a good assumption to use. There is no research behind it. It was based entirely on gut feel.

Second, there is some recent research, from the Texas Department of Corrections, that showed that a triangular distribution was a more realistic assumption.

Finally, I’m not just “averaging” the three points; I’m calculating the expected value of the distribution: check any statistics text for confirmation.

I don’t have any objection to the PERT formula. In fact, the “correct” approach is to look at each work-item and make an assumption about the likely distribution of results. There is no reason why you couldn’t mix betas, triangles, flat, and others on the same project. But I think that most of the benefit from using range estimates comes from simply considering the range.

Once you get to the point where most of your actuals are within the range, then you can start to consider fine-tuning the distribution assumptions.