1. Monte Carlo Schedule Analysis
The Concept, Benefits and Limitations
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2. What is Monte Carlo Analysis?
Monte Carlo simulation is a mathematical method used in risk analysis. Monte Carlo simulations are used to approximate the distribution of potential results based on probabilistic inputs.

3. Monte Carlo Simulations
Monte Carlo simulations use distributions as inputs, which are also the results

4. Monte Carlo Schedule Analysis
Monte Carlo simulations take multiple distributions and create histograms to depict the results of the analysis

5. Two Approaches to Estimating Probabilities
The relative frequency approach, where probability equals the number of occurrences of specific outcome (or event) divided by the total number of possible outcomes.
The subjective approach represents an expert’s degree of belief that a particular outcome will occur.

6. Two of Approaches for Defining Uncertainties
Distribution-based approach
Event-based approach
Monte Carlo can be used to simulate the results of discrete risk events with probability and impact on multiple activities

7. What Distribution Should Be Used?
Also useful for Monte Carlo simulations:
Lognornal
Beta

8. Ignoring Base-Rate Frequencies
Historically, the probability that a particular component will be defective is 1%.
The component is tested before installation.
The test showed that the component is defective.
The test usually successfully identifies defective components 80% of the time.
What is the probability that a component is defective?
The correct answer is close to 4%, however, most people would think that answer is a little bit lower than 80%.

9. Emotions can affect our judgment
Role of Emotions
Emotions can affect our judgment

10. Eliciting Judgment About Probabilities of Single Events
Pose a direct question: “What is the probability that the project will be canceled due to budgetary problems?”
Ask the experts two opposing questions: (1) “What is the probability that the project will be canceled?” and (2) “What is the probability the project will be completed?” The sum of these two assessments should be 100%.
Break compound events into simple events and review them separately.

11. Probability Wheel
Use of visual aids like a probability wheel can aid in the increasing validity of estimates

12. Eliciting Judgment: Probability Method

13. Eliciting Judgment: Method of Relative Heights
Plotting possible estimates on a histogram can help improve estimatesc

14. How Many Trials Are Required?
Huge number of trials (> 1000) usually does not increase accuracy of analysis
Incorporate rare events
Use convergence monitoring

15. What Is The Chance That a Project Will Be on Time And Within Budget?

16. Analysis of Monte Carlo Results
Sensitivity and Correlations
Critical Indices
Crucial tasks
Critical Risks
Probabilistic Calendars
Deadlines
Conditional Branching
Probabilistic Branching
Chance of Task Existence

17. Crucial tasks for project duration
Monte Carlo analysis identifies task cruciality, how often tasks are on the critical path.

18. Critical Risks

19. Conditional Branching

20. Monte Carlo and Critical Chain
Monitoring Project Buffer

21. Tracking Chance of Project Meeting a Deadline

22. When Monte Carlo Is Useful
You have reliable historical data
You have tools to track actual data for each phase of the project
You have a group of experts who understand the project, have experience in similar projects, and are trained to avoid cognitive and motivational biases

23. Additional Resources Project Think:
Intaver Institute 2011
Additional Resources
Project Think:
Why Good Managers Make Poor Project Choices
Project Decisions: The Art and Science
Introduction to Project Risk Management and Decision Analysis
Project Risk Analysis Made Ridiculously Simple

24. Questions?