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

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

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

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

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

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

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

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

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

11. Define Range and Shape Three Point Estimate: about as much information an expert can provide. 1. “MIN” the first point 2. “MAX” the second point 3. “The Best-guess” Range Shape

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

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

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

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

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

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

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

19. MCS -- DEMO

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

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

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

23. Any Questions??