by Stefano Martincigh
At Wargaming Sydney we tried to find better ways to estimate projects in order to find balance between estimate accuracy and effort spent in the estimation.
At the moment we are exploring the Monte Carlo simulations technique coupled with story mapping. This approach has the potential to lead to more accurate estimations, project after project, because we are able to feed our data into next projects.
During this session I will explain how we do story mapping, what are the principles behind the Monte Carlo simulations technique and the advantages of using PERT curve distributions over Gaussian normal distributions.
2. Speaker introduction: Stefano Martincigh
Project estimates using Monte Carlo simulation
More details: https://www.linkedin.com/in/stefano-martincigh-90ba2132/
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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
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
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