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Risk Based Estimating Self Modeling

Risk Based Estimating Self Modeling. Ovidiu Cretu, Ph.D., P.E. Terry Berends, P.E. David Smelser. All known and unknown risks are equally weighted Allows little control over the project cost/schedule Reactive. Clear recognition of project’s threats and opportunities

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Risk Based Estimating Self Modeling

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  1. Risk Based EstimatingSelf Modeling Ovidiu Cretu, Ph.D., P.E. Terry Berends, P.E. David Smelser

  2. All known and unknown risks are equally weighted Allows little control over the project cost/schedule Reactive Clear recognition of project’s threats and opportunities Allows a reasonable control over the project cost/schedule Proactive New Threats Traditional Estimating Risk Based Estimate Threat 1 Base Estimate Base Estimate Contingency Opportunity Threat 2

  3. Cost [$] Duration [Mo] Variability +2% to +10% ValidateBase Cost Duration Risk Based Estimate Engineer’s Estimate Monte Carlo Method Likelihood of Occurrence [%] Identify Quantify Risks Impact [$,Mo]

  4. Base Cost and Schedule Validation • Review the project assumptions • Review the project cost and schedule based on the information available • Update unit price • Update quantities • Capture the cost of unknown cost of miscellaneous items • Remove some contingencies

  5. Variability of the Base Cost and Schedule • The entire construction cost/duration • A major group of pay items • An individual pay item • Symmetrical distribution • Beta3 Distribution

  6. Likelihood of Occurrence [%] Identify Quantify Risks Impact [$,Mo] Cost [$] Duration [Mo] Variability +2% to +10% ValidateBase Cost Duration Risk Based Estimate Engineer’s Estimate Monte Carlo Method

  7. Risks Identification and Quantification • Focus is on • Identify the key ‘risky’ events • Estimate how likely it is that the risky event will materialize • Estimate why and by how much events may turn out differently from the base estimate

  8. Probability of Risk Occurrence • Lowest value = 0 • Highest value = 1 • Middle value = 0.5

  9. Probability of Risk Occurrence • Very Low: = 5% • Low: = 25% • Medium (As Likely As Not) = 50% • High = 75% • Very High: = 95% It is important to be “approximately right.” Do not waste time being “precisely wrong.”

  10. Define Range and Shape Three Point Estimate: about as much information an expert can provide. • “MIN” the first point • “MAX” the second point • “The Best-guess” Range Shape

  11. Shape • “The Best-guess”: This will be the expert’s “median guess” • Median: Actual outcomes evenly distributed over the median guess • “The Best-guess” can’t be too close to the max or the min.

  12. ELICITVALUES: MIN = 100 MAX = 700 Best Guess = 400 Most Likely=400 Entire range (100 to 700) includes 100% of the possibilities

  13. ELICIT VALUES: MIN = 100 MAX = 700 Best Guess = 200 Expert: Costs are more likely to be at the lower end of the range Most Likely 130 Entire range (100 to 700) includes 100% of the possibilities

  14. ELICIT VALUES: MIN = 100 MAX = 700 Best Guess = 600 Expert: Costs are more likely to be at the higher end of the range Most Likely=670 Entire range (100 to 700) includes 100% of the possibilities

  15. Cost [$] Duration [Mo] Variability +2% to +10% ValidateBase Cost Duration Risk Based Estimate Engineer’s Estimate Monte Carlo Method Likelihood of Occurrence [%] Identify Quantify Risks Impact [$,Mo]

  16. CY [$] Cost YOE [$] RESULTS Risk Based Estimate End CN Schedule Ad Date

  17. INPUT RBE OUTPUT • Base • Cost • Duration • Variability • Estimating Date • Escalation Factor • Risks • Cost, Duration • Status • Project Phase • Probability • Range and Shape • Critical Path Info • Markups MCM • Cost • CY • YOE • Diagram • Table • Schedule • AD Date • End CN • Diagram • Table • Sensitivity • Analysis The Model 10,000 Plausible Cases

  18. MCS -- DEMO

  19. Conclusions: • Better understanding of the project’s challenges • Crafts the project risk management plan with clear target on how to enhance the project value • Helps in maximizing the project’s opportunities and reducing or eliminating the project’s threats

  20. The RBE Self-modeling • Two Major Functions • Estimating Function • Risk Management Function

  21. Conclusions: Self-modeling • The model allows registration of meaningful information and it produces valuable results that may be used by decision makers. • The model does not require any special software or specialized skills. • WSDOT - Self-modeling Spread Sheet

  22. Any Questions??

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