1999AM_RISK01_Slides.pdf

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Basic Techniques for Analyzing

and Presentation of Cost Risk Analysis

Randal B. Lorance P.E.

and

Robert V. Wendling

1999 Annual Meeting

AACE International



Monte Carlo Risk Analysis

Steps in process– Develop deterministic model of the estimate

– Identify the uncertainty in the estimate - Variables

– Analyze the estimate with Monte Carlo simulation

– Make decisions based upon results

• This presentation examines the last step

– What decisions can be made

– How to present them to the project stakeholders



Estimate Quality Categories

• Historical– Conceptual (factored)

– Preliminary

– Budget

– Definitive

• Recent Publications by AACE

– Recommended Practices

• Monte Carlo is a tool to determine which level of

Project Definition you have achieved.



AACE - Process Industry

Preliminary

Characteristic

ESTIMATE

CLASS

LEVEL OF

PROJECT

DEFINITIONExpressed as % of

complete definition

END USAGETypical purpose

of estimate

METHODOLOGYTypical estimating

method

EXPECTED

ACCURACY

RANGETypical variation in

low and highranges [a]

PREPARATION

EFFORTTypical degree

of effort relative

to least costsindex of 1 (b)

Class 5 0% to 2% Concept Screening

Capacity Factored,

Parametric Models,

Judgment, or

Analogy

L: -20% to - 50%

H: +30% to +100%1

Class 4 1% to 15% Study or Feasibility

Equipment

Factored or

Parametric Models

L: -15% to -30%

H: +20% to +50%2 to 4

Class 3 10% to 40%

Budget,

Authorization, or

Control

Semi-Detailed Unit

Costs with

Assembly Level

Line Items

L: -10% to -20%

H: +10% to +30%3 to 10

Class 2 30% to 70%Control or Bid /

Tender

Detailed Unit Cost

with Forced

Detailed Take-off

L: -5% to -15%

H +5% to +20%4 to 20

Class 1 50% to 100%Check Estimate or

Bid / Tender

Detailed Unit Cost

with Detailed Take-

Off

L: -3% to -10%

H: +3% to +15%5 to 100

Notes: [a] The state of process technology and availability of applicable reference cost data affect the range markedly.The +/- value represents typical percentage variation of actual costs from the cost estimate after application ofcontingency (typically at a 50% level of confidence) for given scope.

[b] If the range index value of "1" represents 0.005% of project costs, then an index value of 100 represents 0.5%.Estimate preparation efforts is highly dependent upon the size of the project and the quality of estimating data antools.

Secondary Characteristic



TYPICAL PROJECT SEQUENCE OF ACTIVITIES

Screening Phase Conceptual Phase Definition Phase Design & Construction Phase

PROGRAM AFC

RBL 12/97

Conceptual PhaseAFC Approval

Definition PhaseAFC Approval

Project AFCApproval

Feasibility Studies for

Screening Est imates

Exploration &Reservoir Eng.

New BusinessVentures

Write &Approve

AFC

ScreenEstimate &Schedule

Initialreservoir

engr .assess-

ment

Current Estimate

+100%

-50%

Write &Approve

AFC

ConceptualEstimate &Schedule

+60%

-40%

+30%

-20%

+15%

-13%

Marketing / ContractsNegotiations

FinalizeNegotiation

Continued

Reservoir Engr.

Composition& product

Specs.

Prod. ratesover time

Conceptual Studies

& Engineering

Finalreservoir

engr. input

PreliminaryEngineering

Write &Approve

AFC

Trending

Trending from coneptual est imate &schedule to reflect decisions made during

prelimi nary engineer ing (S creeni ng or bet ter quality estimates.)

Execution Trend ing

Oper.Input

freeze

Operations inputs to P&ID's,equipment layouts , & maj.

equip. purchase specs.

Commitmentfor long lead

itemsVendorEngr.

Equipment & bulksprocurement &manufacture

DetailEngineering

Construction Commissioning

Trending f rom the control estimate

& schedule

Project ControlEstimate &Schedule

8 0 % C o n f i

d e n c e

I n t e r v a

l

+8%

-8%

High end ofaccuracy range

Low end ofaccuracy range

Rec ieve budget quotesMajor Equip. &long lead items.

MechanicalCompletion

CommercialSale



Statistics and Confidence IntervalsStatistics and Confidence Intervals

Population of Data P oints

0

10

20

30

40

50

60

70

80

90

10 0

1 6 1 1

1 6

2 1

2 6

3 1

3 6

4 1

4 6

5 1

5 6

6 1

6 6

7 1

7 6

8 1

8 6

9 1

9 6

M

i n u t e s

Histogram

0

5

10

15

20

25

30

35

4

0

4

5

5

0

5

5

6

0

6

5

7

0

7

5

8

0

8

5

9

0

M o r e

Bin

F r e

q

u

e

n

c y

Frequenc y

0

5

10

15

20

2530

35

40 45 50 55 60 65 70 75 80 85 90 More

Cumulative

0%

20%

40%

60%

80%

100%

120%

4 0 4 5 5 0 5 5 6 0 6 5 7 0 7 5 8 0 8 5 9 0

M

o r e

Bin

F r e q

u

e

n



0.0

0.9

0.0 1.3 2.5 3.8 5.0

Concepts, Terms, & DefinitionsProbability Distribution - Skewed to Right Probability Distribution - Skewed to Right

P r o b a b

i l i t y

P r o b a b

i l i t y

50% 50%

Mode Mode

Median Median

Mean Mean

10% 10% 90% 90%

80% Confidence Interval 80% Confidence Interval



Standard Deviation --- σ

-

0.005

0.010

0.015

0.020

0.025

0.030

P r o

b a

b i l i t y

+/− 1σ= 68.27 %+/− 1σ= 68.27 %+/− 1σ= 68.27 %+/− 1σ= 68.27 %

+/− 2σ= 95.45 %+/− 2σ= 95.45 %+/− 2σ= 95.45 %+/− 2σ= 95.45 %

+/− 3σ= 99.73 %+/− 3σ= 99.73 %+/− 3σ= 99.73 %+/− 3σ= 99.73 %

+/− 1.28σ= 80 %+/− 1.28σ= 80 %+/− 1.28σ= 80 %+/− 1.28σ= 80 %

σσσσ ==== Square Root Of Variance



Skewness

-

0.0050

0.0100

0.0150

0.0200

0.0250

0.0300

0.0350

- 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0



Cumulative Probability Distribution

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

- 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

C u m u l a t i v e P r o b

a b i l i t y



More Definitions

• Standard Deviation– Measure of average variance from mean.

– Measure of how widely dispersed the values are

in the distribution.

• Skewness

– Asymmetric distribution.

– Usually right skewed for cost estimates.

– Usually left skewed for revenue estimates.



Output from Risk AnalysisModel

• Graphical • Tabular

Simulation Statistics

Date: 5/19/93 at 21:11

Iterations: 1500

Simulations: 1

Worksheet: HA_ONSHR.XL

Output Range: Total Hainan Is

Cell: $M$29

Base Estimate= 72,128

Mean= 84,864

Std Deviation= 13,892

Variance= 192,990,082

Skewness= 0.451398462

Kurtosis= 3.2461517

Percentile Values

Minimum= 51,160

5Perc= 63,816

10Perc= 67,84415Perc= 70,343

20Perc= 72,820

25Perc= 75,174

30Perc= 76,653

35Perc= 78,550

40Perc= 80,017

45Perc= 82,028

50Perc= 84,084

55Perc= 85,901

60Perc= 87,579

65Perc= 89,324

70Perc= 91,381

75Perc= 93,759

80Perc= 95,977

85Perc= 99,174

90Perc= 102,96495Perc= 109,962

Maximum= 144,382

Frequency Chart

$ in Thousands

.000

.008

.016

.024

.033

0

16.25

32.5

48.75

65

40,000 62,500 85,000 107,500 130,000

2,000 Trials 10 Outliers

Forecast: Total estimate



Alternative graphics output



ContingencyContingency• General Definition: An Assessment, made by a team of professionals,

of the amount of funds which, when added to the line item estimate will reflect an equal chance of underrun or overrun given relative stability

of the scope and assumptions upon which the estimate is based.

• DOE, AACE, & PMI discuss uncertainty and statistical analysis in

their definitions in addition to experience.

• DOE adds: Contingency is not to be used to avoid making an accurate

assessment of expected costs.

• AACE adds: Usually excludes changes in scope or unforeseeable

major events such as strike, earthquakes, etc.

• PMI adds: Contingency reserves are normally included in the project’s

cost and schedule baselines.



Contingency Analysis

• Median - 50/50 point

– Equal Chance of overrun / underrun

– Target

• Overrun / Underrun Limits - Range

• Specified Accuracy versus Achieved

Accuracy



Outputs - Tabular - Each ProjectSimulation Statistics

Date: 5/19/93 at 21:32

Iterations: 1500

Simulations: 1

Worksheet: DRILL.XLS

Output Range: Total Drilling.

Cell: $M$61

Base Estimate= 75,152

Mean= 84,718

Std Deviation= 10,149

Variance= 102,992,737

Skewness= 0.38689996

Kurtosis= 2.966930395

Contingency

Percentile Values $M %

Minimum= 57,313 -17,839 -23.7%

5Perc= 68,893 -6,259 -8.3%

10Perc= 72,189 -2,963 -3.9% 86.2% -13.8%15Perc= 74,229 -923 -1.2%

20Perc= 75,923 771 1.0%

25Perc= 77,547 2,395 3.2%

30Perc= 78,744 3,592 4.8%

35Perc= 80,131 4,979 6.6%

40Perc= 81,401 6,249 8.3%

45Perc= 82,638 7,486 10.0%

50Perc= 83,726 8,574 11.4% 100.0%55Perc= 85,142 9,990 13.3%

60Perc= 86,723 11,571 15.4%

65Perc= 88,111 12,959 17.2%

70Perc= 89,664 14,512 19.3%

75Perc= 91,139 15,987 21.3%

80Perc= 93,247 18,095 24.1%

85Perc= 95,528 20,376 27.1%

90Perc= 98,433 23,281 31.0% 117.6% 17.6%

95Perc= 102,352 27,200 36.2%Maximum= 121,119 45,967 61.2%

Contingency Amount Contingency Amount

80% Confidence 80% Confidence

Interval Interval



Output Shown as CumulativeTypical Cost Risk Analysis Presentation

-25

-20

-15

-10

-5

0

5

10

15

20

25

30

35

40

45

50

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Probability of Overrun (%)

159.3

179.3

199.3

219.3

239.3

259.3

279.3

299.3

C o s

t i n M i l l i o

n s

10.40%

-7.50%

225.3

248.7

208.4

Contingency 6%

Specifiedmaximum

accuracyrange

15%

-13%

Specified confidence interval

A d d t o B a s e +

E s c a

l a t i o n

( % )



Sensitive Analysis

• Which inputs are most significant

• How do you reduce the risk (Improve

Accuracy Range)

• Quantification of Subjective

Impressions



Sensitivity Graphics



Conclusions

• Monte Carlo uses:– Establish contingency

– Establish overrun/underrun limits

– Determine if estimate accuracy meets

expectations

• Graphical presentation

– Cumulative showing “Probability of Overrun”

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