Monday, October 19, 2015

Integrating the risk register with the project

David Hulett has a presentation on his website,, entitled "Schedule Risk Analysis using the Risk Driver Method and Monte Carlo Simulation".  In this presentation, he develops an interesting operating idea: first, shift the Monte Carlo method to the higher level of events on the risk register, and then use those results as drivers on the schedule.

His idea he explains this way:
"Applying first principles requires that the risk to the project schedule be clearly and directly driven by identified and quantified risks. In the Risk Driver Method the risks from the Risk Register drive the simulation.

The Risk Driver Method differs from older, more traditional approaches in which the activity durations and costs are given a 3-point estimate which results from the influence of, potentially, several risks which therefore cannot be individually distinguished and kept track of.

Also, since some risks will affect several activities, we cannot capture the entire influence of a risk using traditional 3-point estimates of impact on specific activities.

Here are the steps:
1. Assign risk distributions to the events on the risk register.  This is more than just a simple PxI calculation.  It means do a three point estimate on each risk event; the real world distribution, likely unknown, can be modeled as a Triangular distribution for simulation purposes.

2. Also evaluate whether or not the risk event, if it occurred, would actually effect every task on the schedule, or just some tasks, and--here's a twist--evaluate whether or not a true event would actually effect the schedule/cost plan. 

That is, if for example labor rate escalation is a risk event, and it actually occurs, does the escalation really get applied or not?  Perhaps there are reasons that the escalation, even though authorized, would not be applied in every case; only be applied in some cases.  Hulett recommends that the latter assessment be on a continuous scale of 0 to 1. 

With this evaluation, you now have two probabilities associated with a risk event: will it occur, and if it occurs, does it have an effect?

3. Now run a Monte Carlo on the schedule; then, run a Monte Carlo on the risk register; then create an intersection between them, in effect identifying correlations between schedule and risk drivers.

What do you get?  You get a simulation result that integrates the probabilistic characteristics of the risk events on the risk register with the probabilistic characteristics of the schedule/resource plan. 

Of course, this is method is all but impossible without a tool to model both the three point estimates and the correlations. That's more or less the secret sauce behind the curtain, but nonetheless the idea of the risk driver method is first rate.

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