Schedule Risk Analysis (SRA) - Overview

Scheduling: Schedule Risk Analysis (SRA)

Why Would A Customer Use This?

In complex business environments—particularly in Project Management (PPM), Finance, or Aerospace & Defense—decisions are rarely based on 100% certainty. Customers use Monte Carlo for three primary reasons:

1. Better Risk Management

Traditional "static" forecasting usually uses a single value (e.g., "This project will cost $1M"). A Monte Carlo simulation shows the customer that while $1M is possible, there is a 70% chance it will actually cost $1.2M. This prevents "happy path" planning and helps teams prepare for the worst-case scenario.

2. Increased Accuracy in GTM & Sales Forecasting

In Go-to-Market (GTM) operations, customers use it to predict revenue. By factoring in variables like:

• Average deal size

• Win rates

• Length of sales cycle The simulation provides a more realistic "probability of hitting quota" than a simple linear forecast.

3. Optimized Resource Allocation

For organizations managing a large portfolio of projects (such as those using Cora PPM), it helps identify which projects are most likely to fail or go over budget. Customers can then move resources or funding to the areas that carry the highest risk or offer the highest potential ROI.

Frequently Asked Questions (FAQ)

• How does it differ from a "What-If" analysis?

  • "What-If" analysis typically changes one variable at a time (e.g., "What if labor costs go up by 10%?"). A Monte Carlo simulation changes all uncertain variables simultaneously, showing you how they interact and providing a statistical likelihood of various outcome

• What data do I need to run a simulation?

  • You don't need a single exact number. Instead, you provide a range for your inputs. Most users provide three data points for every uncertain variable:

    • Optimistic: The "best-case" scenario.

    • Pessimistic: The "worst-case" scenario.

    • Most Likely: What you realistically expect to happen.

• How many "iterations" should I run?

  • For most business applications, running 1,000 to 10,000 iterations is standard. This ensures that the law of large numbers takes effect and the resulting probability distribution is stable and accurate.

• Does this replace human judgment?

  • No. It enhances it. The simulation is only as good as the ranges you input (the "garbage in, garbage out" rule). Its job is to take your expert estimates and show you the mathematical reality of how those risks compound.

• How long does a simulation take?

• Typically a few seconds for 1,000 iterations. Larger schedules or 10,000 iterations may take up to 30 seconds.

• Can I cancel a simulation that is taking too long?

  • Yes. Click the Cancel Simulation button on the progress overlay. No partial results are shown.

• What happens if a task is partially complete?

  • Only the remaining duration is varied. The completed portion is treated as fixed. This ensures the simulation reflects current progress.

• Why are some tasks greyed out in the distributions table?

  • Greyed-out tasks are excluded from variation. They may be summary tasks, inactive tasks, completed tasks, or milestones.

• What distribution shape is used?

  • The engine supports two distribution shapes. The default is a triangular distribution defined by your optimistic, most likely, and pessimistic values. Alternatively, the BetaPERT distribution (also defined by the same three points) provides a smoother, bell-shaped curve that weights the most likely value more heavily. BetaPERT is the industry-standard distribution recommended by PMI and AACE for schedule risk analysis.

• What does the P80 date mean?

  • There is an 80% probability that the project will finish on or before this date, based on the simulation. Equivalently, there is a 20% chance of finishing later.

• Why is my target date probability low?

  • This usually means the planned schedule is optimistic. Consider the P80 date as a more realistic target, or investigate ways to reduce duration uncertainty on the high-sensitivity tasks shown in the Tornado chart.

• Do I need to configure risk events and correlations?

  • No. They are optional. The simulation will run using only the task duration distributions if no risk events or correlations are defined. However, adding them produces more realistic results.

• How is the criticality index different from the deterministic critical path?

  • The deterministic critical path is a single path through the schedule. In a Monte Carlo simulation, the critical path can change from iteration to iteration as durations vary. The criticality index shows how often each task was on the critical path across all iterations, revealing "near-critical" tasks that a deterministic analysis would miss.

• What is the difference between Criticality, Sensitivity, and Cruciality?

  • Criticality measures how often a task is on the critical path (frequency). Sensitivity (Spearman rank correlation) measures how strongly a task's duration variation affects the project finish date (impact). Cruciality combines both into a single metric (Criticality × |Sensitivity| ÷ 100), identifying tasks that are both frequently critical and strongly influential — the true schedule risk drivers.

• What is Spearman rank correlation and why is it used?

  • Spearman rank correlation is a non-parametric statistical measure that assesses monotonic relationships between variables. Unlike Pearson correlation (which assumes linearity), Spearman works on ranked data and is robust to outliers and non-linear effects. It is the industry standard for Monte Carlo sensitivity analysis in tools such as Primavera Risk Analysis and Safran Risk.