Myth 4: Using Monte Carlo simulations to generate contingency budgets or schedules is an appropriate approach and should be more widely adapted.
Truth: Monte Carlo simulations are needlessly complex and shouldn't be used.
Of the three most common risk analysis methods used in creating a contingency schedule or budget--risk classification, decision tree analysis or Monte Carlo analysis--the latter is by far the most complex, so naturally it has the reputation for being the most robust.
But is it really?
Consider the data points your Monte Carlo simulation driver asks of you: original budget (or duration), one or two "things-going-wrong" alternatives, their odds and costs, and at least one "things-go-great" scenario, with its odds and estimated costs.
This is the exact same data set that would support a single-tiered decision tree analysis, except that the Monte Carlo version invokes a random-number generator to fill in hundreds (or even thousands) of other data points, which can then be used to analyze confidence intervals--at least supposedly.
But all of these other data points are artificial! The ensuing confidence intervals are far from reliable, hoopla notwithstanding.
Myth 3: Risk management is so important to project management that it should be employed throughout the project's life cycle.
Truth: After the baseline is set, formal risk management is pretty useless.
This last assertion is guaranteed to invoke a passionate debate, but consider your personal performance. Do you function better when you are confident or when you are worried? And what does formal risk management bring to the table once the project is underway, other than institutional worrying?
Analyzing ominous trends or performance information indicating a problem in order to head off threats to project success is what project managers do on a daily basis. Spending excess time quantifying those threats doesn't improve your odds of success.