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Codecamp Iasi 7 mai 2011 Monte Carlo Simulation

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Codecamp Romania
Codecamp Romania

This document provides an introduction to Monte Carlo simulation. It discusses the motivation for using Monte Carlo simulation over deterministic models, as it allows for risk assessment and is closer to reality. The document outlines the basic process of Monte Carlo simulation, including generating random inputs, evaluating the model, repeating, and analyzing results. It also provides examples and discusses benefits like better planning due to more realistic estimations. It recommends focusing on high-risk tasks and including minimum, most likely, and maximum estimates in project templates to improve using Monte Carlo simulation.

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26 October 2010 Monte Carlo Simulation Constantinescu Eugen
Index Motivation and objectives Basic Knowledge Practical examples Conclusions Reference Links
Motivation and objectives Motivation: Our estimations from projects are not good enough There is place for risks assessment improvement This model is closer to reality than the classical one Let's try to see our projects from more points of view Objectives: Learn about the basic knowledge MCS What are the main benefits from using MCS? What we can change? Where we can improve?
Basic knowledge Process determinist versus process stochastic Monte Carlo Simulation  is a method for  iteratively  evaluating a deterministic model using sets of random numbers as inputs. This method is often used when the model is complex, nonlinear, or involves more than just a couple uncertain parameters.
Basic knowledge Step 1:   Create a parametric model , y =  f (x1, x2, ..., x q ). Step 2:   Generate a set of random inputs , x i 1, x i 2, ..., x iq . Step 3:   Evaluate the model  and store the results as y i . Step 4:   Repeat  steps 2 and 3 for  i  = 1 to  n . Step 5:   Analyze the results  using histograms, summary statistics, confidence intervals, etc.
Basic knowledge Histograms Data: Frequency Cumulative frequency Min, Mean (Average), Max Skewness Kurtosis Confidence Interval
Practical examples 1.MSP small example MCS sheets Analysis 2.MSP real life example MCS sheets Analysis
Conclusions Learn about the basic knowledge MCS – DONE  Take a look over the MATH behind MCS, or just go to point 2. What are the main benefits from using MCS? CP and PERT (Program Evaluation and Review Technique) are optimistic estimations => MCS helps for a better planning What we can change? We should focus on the tasks which have highest chance to become CP. We should start to change the current estimation template and MSP template so that we can include Min, Most Likely and MAX estimation values.
Conclusions Where we can improve? Identify and define the risks easier ( Assessing Risks Improvement ) Analyze the tasks which have higher impact into the project costs ( See the most sensitive tasks)
Reference Links http://www.vertex42.com/ExcelArticles/mc/MonteCarloSimulation.html http://mathworld.wolfram.com/MonteCarloMethod.html http://rule-of-thumb.net/monte-carlo-simulation-for-ms-project/ http://sourceforge.net/projects/montecarloprj/ file://storage/IIC-ForAll/08_PM_Community/MCS

More Related Content

Codecamp Iasi 7 mai 2011 Monte Carlo Simulation

  • 1. 26 October 2010 Monte Carlo Simulation Constantinescu Eugen
  • 2. Index Motivation and objectives Basic Knowledge Practical examples Conclusions Reference Links
  • 3. Motivation and objectives Motivation: Our estimations from projects are not good enough There is place for risks assessment improvement This model is closer to reality than the classical one Let's try to see our projects from more points of view Objectives: Learn about the basic knowledge MCS What are the main benefits from using MCS? What we can change? Where we can improve?
  • 4. Basic knowledge Process determinist versus process stochastic Monte Carlo Simulation  is a method for  iteratively  evaluating a deterministic model using sets of random numbers as inputs. This method is often used when the model is complex, nonlinear, or involves more than just a couple uncertain parameters.
  • 5. Basic knowledge Step 1:   Create a parametric model , y =  f (x1, x2, ..., x q ). Step 2:   Generate a set of random inputs , x i 1, x i 2, ..., x iq . Step 3:   Evaluate the model  and store the results as y i . Step 4:   Repeat  steps 2 and 3 for  i  = 1 to  n . Step 5:   Analyze the results  using histograms, summary statistics, confidence intervals, etc.
  • 6. Basic knowledge Histograms Data: Frequency Cumulative frequency Min, Mean (Average), Max Skewness Kurtosis Confidence Interval
  • 7. Practical examples 1.MSP small example MCS sheets Analysis 2.MSP real life example MCS sheets Analysis
  • 8. Conclusions Learn about the basic knowledge MCS – DONE  Take a look over the MATH behind MCS, or just go to point 2. What are the main benefits from using MCS? CP and PERT (Program Evaluation and Review Technique) are optimistic estimations => MCS helps for a better planning What we can change? We should focus on the tasks which have highest chance to become CP. We should start to change the current estimation template and MSP template so that we can include Min, Most Likely and MAX estimation values.
  • 9. Conclusions Where we can improve? Identify and define the risks easier ( Assessing Risks Improvement ) Analyze the tasks which have higher impact into the project costs ( See the most sensitive tasks)
  • 10. Reference Links http://www.vertex42.com/ExcelArticles/mc/MonteCarloSimulation.html http://mathworld.wolfram.com/MonteCarloMethod.html http://rule-of-thumb.net/monte-carlo-simulation-for-ms-project/ http://sourceforge.net/projects/montecarloprj/ file://storage/IIC-ForAll/08_PM_Community/MCS

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