Qualitative risk analysis tells you which risks matter most; quantitative risk analysis tells you, in dollars and days, how much they matter in aggregate. PM5334 pushes graduate students toward the numeric rigor that separates a risk register from a defensible, decision-ready risk model.
Quantitative risk analysis and Monte Carlo simulation
PM5334 introduces expected monetary value (EMV) analysis and decision tree analysis for evaluating risk-laden decisions with multiple branching outcomes, then extends into Monte Carlo simulation — running thousands of probabilistic iterations across a project's cost and schedule model to produce a distribution of possible outcomes rather than a single-point estimate. Students learn to read a Monte Carlo output (e.g., "there is an 80% confidence the project will finish within 14 months") and use it to set realistic, defensible schedule and budget contingencies instead of arbitrary padding.
Ongoing risk monitoring and control
The course treats risk management as continuous rather than a one-time planning exercise: reassessing the risk register at every milestone, watching for risk triggers (early warning conditions), retiring risks that are no longer relevant, and identifying new risks that emerge as the project progresses. Students study the difference between residual risk (what remains after a response is applied) and secondary risk (a new risk introduced by the response itself) — both of which must be logged and monitored just like original risks.
Key topics in PM5334
- Expected monetary value (EMV) and decision tree analysis for branching risk decisions
- Monte Carlo simulation: probabilistic modeling of cost and schedule outcomes
- Reading confidence-level outputs to set defensible contingency reserves
- Risk triggers and early warning indicators for proactive risk monitoring
- Residual risk vs. secondary risk, and the ongoing risk reassessment cycle
- Risk audits and risk reviews at project milestones
- Sensitivity analysis and tornado diagrams for identifying the highest-impact risk variables
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Worked example: interpreting a Monte Carlo schedule output
- Deterministic estimate: The traditional CPM schedule shows project completion in 12 months
- Monte Carlo simulation: 10,000 iterations, varying each activity's duration within its optimistic/likely/pessimistic range
- Result: Only a 35% probability the project finishes within the original 12-month estimate
- Confidence-level finding: An 80% confidence level requires 14.5 months
- Decision: Sponsor is given the full probability distribution and chooses to commit to 14.5 months publicly, protecting the project from an unrealistic deadline set from a single-point estimate
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Frequently asked questions
A single-point estimate (like a CPM-derived 12-month schedule) hides the uncertainty in every individual activity duration behind one number, giving a false sense of precision — it says nothing about how likely that 12-month date actually is to be met. Monte Carlo simulation instead runs the schedule (or cost) model thousands of times, each time randomly varying each activity's duration within a realistic range (optimistic, most likely, pessimistic), and aggregates the results into a probability distribution. This lets a project manager report findings like "there is only a 35% chance of finishing within 12 months, but an 80% chance of finishing within 14.5 months" — giving sponsors an honest, confidence-level-based view of schedule risk instead of a single number that may have been unrealistic from the start. PM5334 treats this as the key upgrade from qualitative to quantitative risk analysis.
Residual risk is the risk that remains after a response strategy has been applied — for example, after mitigating a vendor-delay risk by adding a schedule buffer, some smaller residual probability of delay still exists, just reduced from the original level. Secondary risk is an entirely new risk introduced by the response strategy itself — for example, transferring a risk by hiring a new, unfamiliar subcontractor to handle a specialized task introduces a new secondary risk around that subcontractor's reliability and quality, a risk that didn't exist before the response was implemented. PM5334 requires students to explicitly identify and log both residual and secondary risks after choosing a response, since treating a risk as "handled" once a response is selected — without accounting for what risk remains or what new risk was created — is one of the most common gaps in real-world risk registers.