Estimating cost and schedule for monte carlo simulation
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A simple explanation for applying Monte Carlo Simulation for Risk Management
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Estimating cost and schedule for monte carlo simulation
- 1. ESTIMATING COST AND SCHEDULE FOR MONTE CARLO SIMULATION Risk Management is Project Management for Adults ‒ Tim ListerNiwot Ridge LLC
- 4. We still have to manage By The Book ¨ Requirements must be achievable and documented ¨ Historically derived basis of estimate ¨ Integrated master schedule ¤ All tasks are planned and linked ¤ Well constructed IAW ANSI 748 ¤ Critical path understood and managed ¤ Fully integrated supplier and government schedule dependencies ¨ Integrated data environment ¤ Deliverables identified in contractual language ¤ Deliverables integrated into master schedule ¨ Configuration management established & active ¨ Timely problem resolution across contractual lines ¨ Alternate design paths for critical technologies
- 5. DoD Risk Management Guide
- 6. DoD RM Handbook Objective
- 7. Context of Risk Management
- 8. 1. Baby Bear – just put in a Low, Medium, or High confidence interval 2. Mamma Bear – start with the Baby Bear, see what happens, make narrow band adjustments using past performance and Anchoring and Adjustment iterations to elicit credible numbers 3. Poppa Bear – perform Reference Class assessments, along with Category Risk Drivers for both Tactical and Program wide risks that drive cost and schedule. Approaches to Estimates
- 9. PapaMammaBaby Technical Performance Schedule Cost No matter the size, we need good estimates
- 10. Yes, Monte Carlo Simulation is from Monte Carlo
- 11. Some Monte Carlo Background ¨ All data in the program are derived from random numbers. ¨ We must have some knowledge of the underlying probability distribution that generated these random numbers. ¨ Without this, our estimates for cost, schedule, or technical performance are simply 50/50 guesses. ¨ Reverend Bayes taught us this in Bayesian Statistics, but we forgot that class long ago.
- 12. Baby Bear Approach to Risk Management
- 13. Make a List, Check It Twice 1. What can go wrong? 2. How will you recognize it is going wrong before it is too late to stop it? 3. What can you do to prevent it from going wrong? 4. How much will it cost you if it were to go wrong? 5. How much will it cost you to prevent it from going wrong? ¨ Keep this list up to date on a weekly basis ¨ Ask and answer the five questions every week.
- 14. First Step in Making an Estimate ¨ Make point estimates of the desired values ¤ Cost ¤ Duration ¤ Technical performance ¨ Develop these point estimates from reliable sources ¤ Personal experience ¤ Past performance ¤ Same As comparisons
- 15. Mamma Bear Approach to Risk Management
- 16. Papa Bear Approach to Risk Management
- 17. In Papa Bear’s House ¨ All numbers are random numbers ¨ Knowing the underlying statistical properties of these numbers is mandatory ¨ No number in the absence of its statistics is credible ¨ All numbers are correlated in some way ¨ Knowing the correlation is mandatory as well
- 18. Pearson’s Product Moment Correlation† åå å -- -- = 22 )()( ))(( YYXX YYXX r ( )( ) ( )( )å åå å å å å -- - = 2222 * YYnXXn YXXYn r or number of ordered pairs standard deviation mean first variable of an ordered pair second variable of an ordered pair n X Y s µ = = = = = † Is a measure of the correlation (linear dependence) between two variables X and Y, giving a value between +1 and −1 inclusive. It is widely used in the sciences as a measure of the strength of linear dependence between two variables.
- 19. Total Cost by Layering Risk Assumptions Impact of Risk and Correlation Assumptions on Total System Cost 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% $350,000 $400,000 $450,000 $500,000 $550,000 $600,000 $650,000 $700,000 $750,000 $800,000 FY2003 $K ConfidenceLevel Point Estimate NoRiskOnWBSRiskOnInputs RiskOnWBSNoRiskOnInputs RiskOnWBSRiskOnInputs CorOnWBSRiskOnInputs CorOnWBSCorOnInputs+LowWBS CorOnWBSCorOnInputs+LowAllElse
- 20. Connections between and Technical $ Cost Driver (Weight) Cost = a + bXc Cost Estimate Historical data point Cost estimating relationship Standard percent error boundsTechnical Uncertainty Combined Cost Modeling and Technical Uncertainty Cost Modeling Uncertainty