Risk Analysis (Qualitative Register & Monte Carlo)

ProjectLibre Cloud combines a qualitative risk register with Monte Carlo schedule simulation—turning one deterministic finish date into measurable schedule confidence, contingency and risk drivers.

This topic covers two complementary parts:

• Qualitative Risk Register + Mapping — capture threats and opportunities, score them, and map them to CPM tasks.

• Monte Carlo schedule risk analysis — sample duration uncertainty across thousands of iterations and read finish-date confidence (P50 / P80 / P95), criticality, sensitivity, and drivers.

Who it’s for: Project managers using ProjectLibre Cloud CPM schedules who need qualitative and quantitative risk insight. The video especially calls out signing contracts with penalties for late delivery.

See also: Project Audit (DCMA Schedule Quality Audit) · Gantt Chart · Main Views

Watch: Monte Carlo risk analysis (~5:41).

Overview

A normal project schedule gives a single planned completion date. Activities can finish early, on time, or late. Monte Carlo analysis answers: What is the probability that this project will actually finish by a particular date?

Product capabilities:

• Works from your existing schedule

• No task estimates required

• Advanced overrides when needed

You do not have to create three-point estimates for every task before running Monte Carlo. ProjectLibre starts with the deterministic task durations already in the plan. If no Optimistic / Most Likely / Pessimistic override exists, project-level Monte Carlo assumptions apply. With Most Likely at 100%, the deterministic task duration is the Most Likely value.

Example: A 30-day deterministic task with project defaults of 90% / 100% / 125% becomes 27 days Optimistic, 30 days Most Likely and 37.5 days Pessimistic.

Six-step workflow

Six steps from schedule to risk insight:

1. Start with your schedule — Use the deterministic schedule already in ProjectLibre.

2. Build the Risk Register — Capture probability, impact, ownership and exposure.

3. Map risks to tasks — Connect each risk event to the schedule activities it can affect.

4. Set Monte Carlo defaults — Choose iterations, distributions and project-wide uncertainty assumptions.

5. Optional task overrides — Enter task-specific O / ML / P values only where extra precision is useful.

6. Analyze results — Read P50/P80/P95, fired risks, criticality, sensitivity and finish drivers.

Steps 2–3 are qualitative. Steps 4–6 are Monte Carlo / quantitative. The video demo path emphasizes steps 1, 4, 5, and 6 without the register/mapping UI.

Part A — Qualitative Risk Register & Mapping

How the qualitative score works

• Score = probability × highest impact.

• This is a prioritization score, not the number of Monte Carlo iterations and not the Fired percentage.

• Examples: L × H = 12 / 64 · M × H = 24 / 64 · H × H = 36 / 64

• Maximum 64 / 64 is the maximum possible qualitative score.

Build the Qualitative Risk Register

Capture specific threats and opportunities with probability, impact, status, owner, exposure and the tasks they may affect. The heat map makes the highest-priority risks easy to see.

UI labels visible in the Qualitative screenshot include sub-toggles Register | Mapping, heat map title Active threats & opportunities - probability × highest impact, button + Add risk, and table columns such as Name, Type, Status, P, I, Score, Exposure, Owner, Tasks.

Map risks to tasks

The Mapping view connects the Risk Register to the CPM schedule. One risk can affect one task or several related tasks, and a task can be exposed to more than one risk.

Why mapping matters: A shared risk fires once in an iteration. If it occurs, all tasks linked to that same event are affected together, preserving the real-world relationship between them. The mapping matrix makes risk-to-task relationships explicit.

Task-level qualitative risk on the schedule

The schedule can show linked risk names, score pills (for example 24/64), and exposure beside the Gantt (task-level qualitative risk view).

Part B — Monte Carlo schedule risk analysis

Monte Carlo samples task durations across many iterations, recalculates the CPM schedule each time, and records finish outcomes—turning thousands of simulated schedules into decisions. Linked qualitative threats can also fire by probability when configured; the walkthrough video itself demonstrates duration-range uncertainty rather than register firing.

How to open Schedule risk analysis (Monte Carlo)

Workflow:

1. Open a project so the Gantt chart is in view.

2. On the top toolbar above the Gantt, click the gauge / speedometer icon.

3. Tooltip: Schedule risk analysis (Monte Carlo).

4. Dialog opens: Schedule risk analysis.

On the same toolbar, Schedule quality audit (DCMA) sits adjacent (see Project Audit).

Set Monte Carlo defaults (project-level)

Choose project-level uncertainty assumptions and the number of iterations. These defaults apply to every task that does not have its own task-level estimates. The normal project duration remains the Most Likely duration when Most Likely is set to 100%.

Iterations:

• Quick (1,000)

• Standard (3,000)

• Deep (10,000)

Uncertainty defaults: Optimistic 90% · Most Likely 100% · Pessimistic 125%. Video speech: optimistic about 10% before the deterministic value; pessimistic about 25% worse.

Distributions:

• Named in the walkthrough video: Triangular, PERT (Beta), Uniform.

• Quantitative Risk simulation UI also lists Lognormal and Trigen (five total), with Anchors P10 / P90 noted for Lognormal & Trigen only.

Optional task-level O / ML / P overrides

Simple rule: No task estimate → use project defaults. Task estimate entered → use the task-specific values instead.

1. Double-click a task → task details modal.

2. Open the Risk tab (modal tabs include: Main, Performance, Actuals, Calendar, Scheduling, Dependencies, Risk, Notes).

3. Set Risk Distribution, Optimistic Duration, Pessimistic Duration (and most likely / schedule duration as applicable).

4. Example from the video: most likely 4 days, optimistic 2 days, pessimistic 9 days — overrides project Monte Carlo assumptions for that task.

An Estimates view provides for task-specific Optimistic / Most Likely / Pessimistic durations (columns may show Project Default when not overridden).

Reading results

Each iteration samples task durations, determines which linked risk events fire (when qualitative threats are linked), recalculates the CPM schedule and records the outcome.

Finish confidence and metrics:

Term	Definition
P50 / P80 / P95	Finish dates achieved on or before 50%, 80% and 95% of simulated outcomes.
Fired	The percentage of Monte Carlo iterations in which that risk event actually occurred.
Criticality	The percentage of simulation runs in which a task is on the critical path.
Sensitivity	How strongly variation in a task or risk is associated with variation in project finish.
Risk Score	Probability × highest impact (qualitative prioritization; max 64).
Risk Exposure	Financial exposure based on cost basis, probability and cost impact.

Summary cards: Deterministic finish; P50 finish; P80 finish; P95 finish; On time - vs current plan finish. Dashboard cards may also show contingency-to-P80 style labels.

Charts:

• FINISH-DATE CONFIDENCE (S-CURVE) — tooltip style P(finish ≤ date)

• WHAT DRIVES THE FINISH RISK — correlation / sensitivity tornado

Table: HIGHEST CRITICALITY — columns Task, P80 finish, Criticality, Sensitivity. The dashboard also shows Register Risks in this Simulation (Probability, Impact, Fired, Sensitivity).

Results grid: columns can include Name, Duration, Finish, P50 Finish, P80 Finish, Criticality, Sensitivity, Cruciality, SSI, plus Gantt with risk-layered bars.

Engineering project example: Deterministic finish Feb 17, 2027; P50 Apr 1, 2027; P80 Apr 29, 2027; P95 May 26, 2027.

Decision uses:

• Evaluate whether a committed completion date is realistic

• Identify where schedule uncertainty is concentrated

• Decide on resources, contingency, vendor management, or risk mitigation

• Select a completion date by explicit confidence level rather than intuition

Closing contrast: a traditional schedule tells when the plan finishes; Monte Carlo tells how confident you can be in that date.

Watch the walkthrough

Watch: Monte Carlo risk analysis (~5:41) — ProjectLibre project management software.

Covers opening Schedule risk analysis from the Gantt toolbar, project-level iterations/distributions, task Risk tab overrides, S-curve / tornado / criticality / sensitivity, and P50 / P80 / P95. It does not walk through the Qualitative Risk Register or Mapping matrix.