Risk Based EstimatingRisk Based EstimatingSelf ModelingSelf Modeling

Ovidiu Cretu, Ph.D., P.E.Ovidiu Cretu, Ph.D., P.E.Terry Berends, P.E.Terry Berends, P.E.David SmelserDavid Smelser

Washington StateDepartment of Transportation

Threat 1

All known and unknown All known and unknown risks are equally weightedrisks are equally weighted

Allows little control over the Allows little control over the project cost/scheduleproject cost/schedule

Reactive Reactive

Clear recognition of Clear recognition of project’s threats and project’s threats and opportunitiesopportunities

Allows a reasonable Allows a reasonable control over the project control over the project cost/schedulecost/schedule

ProactiveProactive

Traditional Estimating Risk Based Estimate

Base Estimate

Base Estimate

Contin

genc

y

Opp

ortu

ni

ty

Threat 2

New Threats

En

gin

eer’

s

Esti

mate

Identify Quantify Risks

Likelihood of Occurrence [%]Impact [$,Mo]

Validate Base CostDuration

Cost [$]Duration [Mo]

Variability+2% to +10%

Ris

k

Based

Esti

mat

eMonte Carlo Method

Base Cost and Base Cost and Schedule ValidationSchedule Validation

Review the project assumptionsReview the project assumptions Review the project cost and schedule Review the project cost and schedule

based on the information availablebased on the information available Update unit priceUpdate unit price Update quantitiesUpdate quantities

Capture the cost of unknown cost of Capture the cost of unknown cost of miscellaneous itemsmiscellaneous items

Remove some contingenciesRemove some contingencies

Variability of the Variability of the Base Cost and ScheduleBase Cost and Schedule

The entire construction cost/durationThe entire construction cost/duration A major group of pay itemsA major group of pay items An individual pay itemAn individual pay item

Symmetrical distributionSymmetrical distribution Beta3 DistributionBeta3 Distribution

En

gin

eer’

s

Esti

mate

Validate Base CostDuration

Identify Quantify Risks

Likelihood of Occurrence [%]Impact [$,Mo]

Cost [$]Duration [Mo]

Variability+2% to +10%

Ris

k

Based

Esti

mat

eMonte Carlo Method

Risks Identification and Risks Identification and QuantificationQuantification

Focus is on Focus is on Identify the Identify the key key ‘risky’ events‘risky’ events Estimate Estimate how likelyhow likely it is that the risky it is that the risky

event will materializeevent will materialize Estimate Estimate why why and by and by how muchhow much

events may turn out differently from the events may turn out differently from the base estimatebase estimate

Probability of Risk Probability of Risk OccurrenceOccurrence

Lowest value = 0 Lowest value = 0 Highest value = 1 Highest value = 1 Middle value = 0.5 Middle value = 0.5

Probability of Risk Probability of Risk Occurrence 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 It is important to be “approximately right.” Do not waste time being right.” Do not waste time being “precisely wrong.”“precisely wrong.”

Define Range and Shape Define Range and Shape

Three Point Estimate: about as much Three Point Estimate: about as much information an expert can provide.information an expert can provide.

1.1. ““MIN” the first pointMIN” the first point2.2. ““MAX” the second pointMAX” the second point3.3. ““The Best-guess” The Best-guess”

Range

Shape

Shape Shape

““The Best-guess”: This will be the The Best-guess”: This will be the expert’s “median guess”expert’s “median guess”

Median: Actual outcomes evenly Median: Actual outcomes evenly distributed over the median guessdistributed over the median guess

““The Best-guess” can’t be too close to The Best-guess” can’t be too close to the max or the min.the max or the min.

Entire range (100 to 700) includes

100% of the possibilities

MIN = 100

MAX = 700

ELICIT VALUES:

Best Guess = 400

Most Likely=400

Pert(100, 130, 700)

0 100 200 300 400 500 600 700 800

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 likelyto be at the lower end of the range

Pert(100, 670, 700)

0 100 200 300 400 500 600 700 800

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 likelyto be at the higher end of the range

En

gin

eer’

s

Esti

mate

Validate Base CostDuration

Identify Quantify Risks

Likelihood of Occurrence [%]Impact [$,Mo]

Cost [$]Duration [Mo]

Variability+2% to +10%

Ris

k

Based

Esti

mat

eMonte Carlo Method

Ris

k

Based

Esti

mat

e0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

79

.0

81

.8

84

.6

87

.4

90

.2

93

.0

95

.8

98

.6

10

1.4

10

4.2

10

7.0

10

9.8

11

2.6

11

5.4

11

8.2

12

1.0

Total Project Cost $ Million

Pro

bab

ility

Cost

CY [$]

YOE [$]

Schedule

End CN

Ad Date

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Jan-

08

Apr-0

8

Jul-0

8

Oct

-08

Jan-

09

Apr-0

9

Jul-0

9

Oct

-09

Jan-

10

Apr-1

0

Jul-1

0

End of Construction

Cum

ulat

ive

Prob

abili

ty

RESULTS

INPUT OUTPUT

Base CostDurationVariability

Estimating Date

Escalation Factor

Risks Cost, Duration

StatusProject PhaseProbabilityRange and ShapeCritical Path Info

Markups

CostCYYOE

Diagram

Table Schedule

AD DateEnd CN

DiagramTable

Sensitivity Analysis

TheModel

10,000 Plausible

Cases

MCM

RBE

MCS -- DEMOMCS -- DEMO

Conclusions:Conclusions: Better understanding of the project’s Better understanding of the project’s

challenges challenges Crafts the project risk management plan Crafts the project risk management plan

with clear target on how to enhance the with clear target on how to enhance the project value project value

Helps in maximizing the project’s Helps in maximizing the project’s opportunities and reducing or eliminating opportunities and reducing or eliminating the project’s threatsthe project’s threats

The RBE Self-modelingThe RBE Self-modeling

Two Major FunctionsTwo Major Functions Estimating FunctionEstimating Function Risk Management Function Risk Management Function

Conclusions: Self-Conclusions: Self-modelingmodeling

The model allows registration of The model allows registration of meaningful information and it meaningful information and it produces valuable results that may produces valuable results that may be used by decision makers. be used by decision makers.

The model does not require any The model does not require any special software or specialized skills.special software or specialized skills.

WSDOT - Self-modeling Spread SheetWSDOT - Self-modeling Spread Sheet

Any Questions??Any Questions??