COST RISK ASSESSMENT CHAPTER 23 Karthika Karuppan Chetti, Thuy Maria Le, Hanh Nguyen, Shangyin Gao
Transcript
1. COST RISK ASSESSMENT CHAPTER 23 Karthika Karuppan Chetti,
Thuy Maria Le, Hanh Nguyen, Shangyin Gao
2. RISK MODEL DEFINITION Cost Risk Assessment: applied the most
for the longest period of time A single value occurs rarely
3. RISK ANALYSIS SAMPLING TECHNIQUES Monte Carlo Technique Most
commonly used technique A large number of random sampling.
4. RISK ANALYSIS SAMPLING TECHNIQUES Generate random numbers
from all areas Iterations will represent all areas of the
distribution right away Latin Hypercube
5. PROBABILITY DISTRIBUTIONS Most used distribution in project
risk assessment Contain more information than uniform distribution
Used when a range is known with some certainty but the relative
likelihood is unclear for any particular value Used frequently in
the situations where no greater likelihood of an overrun than an
underrun
6. RISK ASSESSMENT OUTPUTS Tool: Software program: @RISK
Purpose: To estimate the probability of total cost. Outputs: Total
cost, revenue and profit Sampling: Latin Hypercube and Monte Carlo
Number of iterations: 100
7. RISK ASSESSMENT OUTPUTS
8. RISK ASSESSMENT OUTPUTS
9. RISK ASSESSMENT OUTPUT STATISTICS Skew The degree of
asymmetry of the distribution curve The higher value of skew, the
more asymmetric the curve Left-skewed and right-skewed curve
10. KURTOSIS How flat or peaked a distribution curve is The
higher kurtosis, the more peaked the curve is, i.e. most of the
values clustering near the expected value False optimism in
developing distributions
11. MONTE CARLO SIMULATION CHART Relating output values to
probabilities Presented by value vs. by probability Cumulative
Percentage Values ($ x millions) Probability 2.0 53% 2.5 86% 3.0
98% Cumulative Percentage Probability Value ($ x millions) 0% 1.0
10% 1.1 20% 1.3 Etc. Etc.
12. PURPOSE OF COST RISK ASSESSMENT Determine the degree of
variability in the estimate numbers and the risks in using that.
Provide a strong numerical basis to make decision on cost risk
allocations and bid price
13. HOW TO APPLY RISK ASSESSMENT Small likelihood of an
underrun means the large likelihood of an overrun If the
probability of underrun is low, i.e. the probability of overrun is
high, the final estimate should be increased to reduce risk
Specific high cost risk items can be identified Acceptable level of
risk depends on the project, the market conditions, and the
corporate strategy.
14. EXAMPLE Estimate = $210 millions Probability of underrun:
10% Probability of overrun: 90% Estimate = $220 millions
Probability of underrun: 60% Probability of overrun: 40% Estimate =
$230 millions Probability of underrun: 90% Probability of overrun:
10% Estimate = $250 millions Probability of underrun: 100%
Probability of overrun: 0% 0% 20% 40% 60% 80% 100% 200 220 240 $ x
millions Risk Assessment
15. TRADEOFF IN COST RISK ALLOCATIONS High bid price lose the
contract Low bid price cancellation of the contract Trying to keep
the price as low as possible while still having a reasonable chance
of performing the work for the estimated amount
16. CONDUCTING A RISK ASSESSMENT Purpose Figures out risks
Prevent problems from occurring Process Identify risks and benefits
Best course of action Risk Assessment Planning process of a project
Six steps - conducting a risk assessment
17. SIX STEPS - CONDUCTING A RISK ASSESSMENT 1. Identify 2.
Assess 3. Analyze 4. Make Decisions 5. Implement 6. Review
18. IMPLEMENTATION EXAMPLE PERT Technique in the 1960s Cost
risk analyses- project management tool Used in smaller projects
Small cost, short duration, capital improvement projects Problem -
Internal politics Risk analysis - outcome was dictated 95%
confidence level Underrun 100% confidence level - Overrun
19. CONCLUSION Existence of uncertainty Estimate as a single
point value Better option offers a value analyze its likelihood of
being exceeded Using Monte Carlo approach Sampling of possible
outcomes with improved accuracy in larger samples Management
Program of risk management