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ADVANCED ANALYSIS IN MONTE CARLO 211

was critical for 68 percent of the iterations, and the lower path for 30 percent. (In
our earlier illustration, Figure 6.3b, the lower path was shown to be critical 98
percent of the time. Yet, we didn’t change the duration of that path or the proj-
ect.) With risk analysis, we gain both greater sensitivity to the effect of converging
paths, and additional information to direct our attention to the tasks that are at
the greatest risk to extend the project.

Risk+ can also perform risk analysis for costs, as well as time. The discussion
here is deliberately limited to schedule analysis.

Using Monte Carlo

Our second risk management software example is Monte Carlo, from Primavera
Systems, Inc. Monte Carlo sits on top of Primavera Project Planner (P3). When
Monte Carlo is loaded, it can be accessed from the P3 menu. You can use Monte
Carlo on any project file that has been saved in the P3 format (from P3 for Win-
dows, SureTrak for Windows, or P3 for DOS). As with Risk+, Monte Carlo can
analyze both schedule and cost risk, and generate date and cost probability
graphs. Where Monte Carlo goes beyond Risk+ is in its ability to handle proba-
bilistic and conditional branching, and several other statistical functions. You can
skip that part, if you’re sigmaphobic. I had to read the manual several times be-
fore the process sank in. The basics are not really that complex. But many of the
advanced features can be a challenge to the statistically disadvantaged.

Basic Risk Analysis in Monte Carlo

Basic schedule and cost risk analysis, in Monte Carlo, is similar to that in Risk+.
Click on Monte Carlo and you get a chance to specify risk parameters and num-
ber of iterations. Initial results are shown in Primavera Look (an output reader).
Monte Carlo employs ReportSmith (bundled with P3) for detailed reporting.
Several predesigned reports are furnished.

Advanced Analysis in Monte Carlo

Up to now, we have looked at routines for analyzing schedules based on a fixed
work flow. We defined potential variables for the time of each task. However, all
tasks were assumed to be performed, and in the defined sequence.

A frequent planning concern is the scheduling of alternative strategies. Monte
Carlo deals with this in two ways. The first is probabilistic branching. You would
use probabilistic branching when you want to define the percentage likelihood
that a specific activity will occur after another activity.