I love Monte Carlo simulation. It’s fascinating to me, and I like that it’s half-art, half-science. The science part involve statistics, probabilities and probability distributions. The art part involves selecting the right distribution to characterize an uncertainty, then configuring that distribution in the model so it fairly represents the uncertain nature of the uncertainty itself.
The Monte Carlo simulation program I’m most familiar with is an Excel add-in program called @Risk, part of the DecisionTools Suite by Palisade. It’s a pricey piece of software — nearly $1300 for a stand-alone, standard license that covers one year of support and upgrades, after which it will cost you a few hundred dollars to maintain ongoing support.
In future blog posts, I will be using Palisade’s @Risk program to compare the strengths and weaknesses of Statistical PERT. As I continue estimating and experimenting with Statistical PERT, I’m pleased that SPERT creates probabilities that are close to a Monte Carlo simulation.
But is Statistical PERT good enough to use instead of other estimation methods, including Monte Carlo simulation? Before answering that question, we have to wrestle with the difference between accuracy and precision.