5.1 Perform Quantitative Risk Analysis

What Quantitative Risk Analysis Does

Perform Quantitative Risk Analysis numerically analyzes the combined effect of identified individual risks and other sources of uncertainty on overall project objectives. Where qualitative analysis sorts risks into high, medium, and low priority, quantitative analysis answers the sponsor's harder question: "In dollars and days, how exposed are we, and how confident can we be in our targets?"

The process produces a range of possible outcomes with associated probabilities, not a single deterministic number. That is its defining feature on the PMI-RMP exam.

It sits within the Performing Specialized Risk Analyses domain of the PMI-RMP Exam Content Outline. The key mental shift is from individual risks, each handled on its own, to a single integrated model of how every risk and every estimating uncertainty rolls up into one combined cost figure and one combined finish date. Tools include Monte Carlo simulation, Expected Monetary Value, decision trees, three-point estimating, and sensitivity analysis — all covered later in this chapter.

When It Is Warranted

Qualitative analysis is performed on essentially every project; quantitative analysis is selective. It is justified when the cost of analysis is repaid by better decisions. Use it for:

• Large projects where small percentage overruns mean large absolute dollars
• Complex projects with many interacting risks and dependent schedule paths
• Strategically important projects where the sponsor or governance body requires confidence levels before committing funding
• Projects bidding fixed-price work where reserve sizing drives the margin

If a project is small, short, or low-stakes, the modeling effort is not worth it — document that qualitative analysis was sufficient. A frequent exam trap is assuming quantitative analysis is mandatory; it is not.

A second trigger is stakeholder demand for defensible numbers. When a steering committee asks "how confident are you in this deadline?", a high/medium/low rating from qualitative analysis will not satisfy them. Only a probabilistic answer — a confidence percentage tied to a specific date or budget — closes that gap, and that requires quantitative analysis. The agile and hybrid lens matters too: on adaptive projects, heavy up-front simulation may be wasted because scope evolves, so teams favor lighter, iterative quantification.

Inputs

Quantitative analysis is only as good as the models feeding it. Required inputs:

The risk register and a credible, logic-linked schedule are the load-bearing inputs. Garbage estimates produce confident-looking but worthless S-curves.

Before running any simulation, validate the model: confirm the schedule has no missing logic links or open ends, that durations are realistic, and that the risk probabilities and impact ranges in the register have been reviewed for quality. The assess data quality step from qualitative analysis carries forward here — unreliable input data invalidates every downstream number.

Outputs

The process yields decision-grade numbers:

• Probabilistic cost and schedule forecasts — e.g., "There is an 80% chance of finishing under $4.6M and by 14 March."
• Probability of achieving objectives — the likelihood of meeting the current cost target or deadline as planned
• Contingency reserve needs — the dollar and time buffer required to reach a chosen confidence level (often P80 for cost, P50–P80 for schedule)
• A prioritized list of quantified individual risks — ranked by their contribution to overall variance (typically read from a tornado/sensitivity chart)
• Trends in quantitative results as the project progresses

These outputs feed reserve decisions, go/no-go gates, and stakeholder communication. The risk report is updated with the new overall-risk picture, and recommended responses at the project level (such as adding reserve or replanning a critical path) may emerge from the analysis.

A practical example ties them together: a simulation might report "there is a 60% chance of meeting the current $4.0M budget, but an 80% confidence figure requires $4.6M." The probability of achieving the objective is 60%; the contingency reserve need is $600K; and the prioritized list shows which two or three risks contribute most of that gap. Every output is decision-relevant, not academic.

Quantitative vs Qualitative

Know this distinction cold — it is heavily tested.

Qualitative analysis comes first: it screens and prioritizes risks so the team only spends quantitative modeling effort on the risks that matter. Quantitative analysis then aggregates those significant risks into one combined numeric picture of cost and schedule. They are complementary stages of one workflow, not alternatives — a question implying you should skip qualitative and jump straight to Monte Carlo is wrong.