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Project (agile) estimates using Monte Carlo Simulations

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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.

Published in: Business
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Project (agile) estimates using Monte Carlo Simulations

  1. 1. Project estimates using Monte Carlo simulation
  2. 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. 3. Our environment Project estimates using Monte Carlo simulation
  4. 4. Our environment Project estimates using Monte Carlo simulation
  5. 5. Planning: Project estimates using Monte Carlo simulation
  6. 6. Planning: Project estimates using Monte Carlo simulation
  7. 7. What can we do? Project estimates using Monte Carlo simulation
  8. 8. Risk Management; Project scheduling simulation Project estimates using Monte Carlo simulation https://www.youtube.com/watch?v=NxgVBeTfAio
  9. 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. 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. 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. 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. 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. 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. 15. Discussion time! Project estimates using Monte Carlo simulation Able to influence Graphs observations
  16. 16. Deeper analysis Project estimates using Monte Carlo simulation The Pert distribution: 4 parameters - Min, Max, Mode, Height
  17. 17. Deeper analysis Project estimates using Monte Carlo simulation The Pert distribution: 4 parameters - Min, Max, Mode, Height
  18. 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. 19. Factors which negatively impact this analysis Project estimates using Monte Carlo simulation
  20. 20. Acknowledgement Project estimates using Monte Carlo simulation ● Paul Hampson - https://www.tbble.org/monte-carlo-stefano/ ● Geoff Deitz ● Martin Kearns ● Naresh Hirani
  21. 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. 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. 23. Project estimates using Monte Carlo simulation

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