The document details the use of Monte Carlo simulation for project estimation, outlining its definition and the necessary steps for implementation. It presents statistical results for different project epic sizes and compares outcomes using Pert and Gaussian distributions. The conclusions emphasize the importance of stakeholder involvement in data-driven analysis and highlight the efficiency of running simulations once the tool is developed.

1. Project estimates using Monte Carlo simulation

2. Speaker introduction: Stefano Martincigh
Project estimates using Monte Carlo simulation
More details: https://www.linkedin.com/in/stefano-martincigh-90ba2132/
Impress them!
Establish
credibility...

3. Our environment
Project estimates using Monte Carlo simulation

4. Our environment
Project estimates using Monte Carlo simulation

5. Planning:
Project estimates using Monte Carlo simulation

6. Planning:
Project estimates using Monte Carlo simulation

7. What can we do?
Project estimates using Monte Carlo simulation

8. Risk Management; Project scheduling simulation
Project estimates using Monte Carlo simulation
https://www.youtube.com/watch?v=NxgVBeTfAio

9. Monte Carlo Method
Project estimates using Monte Carlo simulation
Definition:
A method of estimating the value of an unknown
quantity using the principles of inferential statistics
We have to create a “transition indicator” where we
have a metric that can give some quantitative data
from the facts of the past into building probabilities
into our future.

10. Monte Carlo Method steps:
Project estimates using Monte Carlo simulation
Step 1 – Generating random variables that are uniformly distributed between 0 and 1
Step 2 – Transforming [0, 1] uniform variables into random variables that follow the given distributions
Step 3 – repeat step 1 and 2 for each epic
Step 4 – sum all the data from previous step
Step 5 – repeat several thousand times
Step 6 – plot resulting graph

11. Epics definition
Project estimates using Monte Carlo simulation
Epic = S
Mean = 9 Standard deviation = 0.9
Results:
● 3rd - 7 days
● 50th - 9 days
● 80th - 10 days
● 97th - 11 days

12. Epics definition
Project estimates using Monte Carlo simulation
Epic = M
Mean = 16 Standard deviation = 1.6
Results:
● 3rd - 13 days
● 50th - 16 days
● 80th - 17 days
● 97th - 19 days

13. Epics definition
Project estimates using Monte Carlo simulation
Epic = L
Mean = 30 Standard deviation = 3
Results:
● 3rd - 24 days
● 50th - 30 days
● 80th - 33 days
● 97th - 36 days

14. Project example
Project estimates using Monte Carlo simulation
Project composition
5S 3M 2L
Results:
● 3rd - 142 days
● 50th - 153 days
● 80th - 158 days
● 97th - 163 days

15. Discussion time!
Project estimates using Monte Carlo simulation
Able to influence Graphs observations

16. Deeper analysis
Project estimates using Monte Carlo simulation
The Pert distribution: 4 parameters - Min, Max, Mode, Height

17. Deeper analysis
Project estimates using Monte Carlo simulation
The Pert distribution: 4 parameters - Min, Max, Mode, Height

18. Pert vs Gaussian
Project estimates using Monte Carlo simulation
Project composition
5S 3M 2L
Gaussian Results:
● 3rd - 142 days
● 50th - 153 days
● 80th - 158 days
● 97th - 163 days
Pert Results:
● 3rd - 155 days
● 50th - 172 days
● 80th - 181 days
● 97th - 192 days

19. Factors which negatively impact this analysis
Project estimates using Monte Carlo simulation

20. Acknowledgement
Project estimates using Monte Carlo simulation
● Paul Hampson - https://www.tbble.org/monte-carlo-stefano/
● Geoff Deitz
● Martin Kearns
● Naresh Hirani

21. Conclusions
Project estimates using Monte Carlo simulation
● Once you build the tool it does not take long to run simulations
● Involve stakeholders in the creation of data driven analysis

22. Conclusions
Project estimates using Monte Carlo simulation
● Once you build the tool it does not take long to run simulations
● Involve stakeholders in the creation of data driven analysis

23. Project estimates using Monte Carlo simulation