Learn about the simulation analysis technique used by project managers to determine possible outcomes and impacts of project risk events by analyzing best case, worst case and most likely scenarios. Understand how this helps assess risks to project scope, schedule and cost.
Table of Contents
Question
You are the program manager for your organization and you are trying to determine the possible outcomes of a risk event. You’re analyzing the risk event’s worst case scenario, most likely scenario, and optimistic scenario to simulate the possible affects of the risk on the program’s cost, time, and scope ramifications. What simulation technique are you using in this situation?
A. Monte Carlo simulation
B. Sensitivity analysis
C. Force field analysis
D. Decision tree analysis
Answer
A. Monte Carlo simulation
Explanation
Monte Carlo simulation is a risk quantification technique that uses probabilistic analysis to determine the range of possible outcomes and impacts of risk events on project objectives like scope, schedule, and cost. It does this by analyzing multiple scenarios, typically the best case (optimistic), worst case (pessimistic), and most likely scenarios for each risk event.
In a Monte Carlo simulation, the project model is calculated many times (iterated), with the input values chosen at random for each iteration from the probability distributions of those variables. The outcome is a probability distribution of the project’s overall objectives, showing the likelihood of achieving different results.
This fits the scenario described in the question, where you as the program manager are trying to determine possible outcomes of a risk event by analyzing the worst case, most likely, and optimistic scenarios to simulate the risk’s potential effects on the program’s scope, schedule and budget.
The other options are different types of analysis techniques:
B. Sensitivity analysis examines which project elements have the most potential impact on project outcomes, but doesn’t look at probability distributions and scenarios like Monte Carlo does.
C. Force field analysis identifies forces that drive or restrain change, but is not a probabilistic simulation technique.
D. Decision tree analysis maps out decisions and their potential consequences, but also does not perform probabilistic scenario simulations.
So in summary, Monte Carlo simulation is the risk analysis technique that best matches the approach described in the question of analyzing best case, worst case, and most likely scenarios to quantify the range of possible impacts of a risk event on project objectives. It’s an important tool for project risk management.
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