Risk Modelling of a Notional Mega Project Cost Estimate 11 Nov 2011

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Overview

Mega Projects by their very nature consist of smaller individual projects that integrate together to form a whole.

In a similar fashion the estimate for such a project also comes from a multitude of diverse disciplines with differing sources and work practices.

This paper will use a case study to show how a risk model for the Cost Estimate was built in Palisade @Risk to identify appropriate levels of Contingency and Management Reserve.

Inputs to the model were taken from a variety of sources including

• Control Estimate• Systemic risk models• Quantitative schedule risk models• Risk ranging workshops • Various risk registers

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Contingency & Management Reserve

ContingencyContingency is the amount of money used in the estimate to deal with the uncertainties

inherent in the estimating process. Contingency is required because estimating is not an exact science.

• The amount of infill and concrete to cover an underground pipe, the number of man-hours to complete the task, and the actual all in labour rate, are all best estimates until the work is complete

Management ReserveAn amount added to the estimate to allow for specific risks that may or may not occur that

are within the project’s control or influence. Risk is defined as an undesirable potential outcome and its probability of occurrence

• Does not include force majeure, currency risk, political risk etc

ScopeBoth amounts are based on a defined scope. Should scope change , the estimate must

be revised to reflect such changes in scope. Neither Contingency nor Management Reserve are a source of funds to cover scope

changes

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Mega Project vs Project Portfolio

Key DifferencesSo why is a Mega Project so different to a Portfolio of Projects being executed by a

Company. Key differences are:• Same key personnel• Mega project is a number of inter related projects with numerous key interfaces. Any

delay or changes to one can have a significant effect on one or the others• Site Wide Services common to all sub projects• Same geographical location

EffectMust not underplay the significance of these differences. Sub projects must not be

modeled in isolation• Liberal use of correlation between sub projects to avoid nodal bias• Schedule must be modeled at the mega project level ensuring all interfaces are

included.

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Identifying the Risks

The Risk RegisterPrior to estimating contingency or otherwise quantifying risk impacts, the risks must first

be identified and then logged in the Risk register. Husky uses a standard 5 x 5 risk Matrix as a Probability Impact Diagram for Project Risk. Impacts are defined specific to project

Likelihood of Occurrence Consequence Very Low Low Medium High Very High

Very High 5A 5B 5C 5D 5E High 4A 4B 4C 4D 4E Medium 3A 3B 3C 3D 3E Low 2A 2B 2C 2D 2E Very Low 1A 1B 1C 1D 1E

Priority Action Setting

Critical Immediate Action must be taken to Prevent or Mitigate the Risk

Serious Mitigation Action Required Moderate Mitigation Strategies to be investigated Acceptable Be aware of Potential Mitigation Strategies

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Typical Risk Register

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Typical Boston Square

The Cost Model

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Main input to the Cost Model was the Control Estimate. However elements of the estimate were modelled using a variety of techniques and then brought into an overall Cost Risk model

• Risk ranging workshops using range estimating techniques• Systemic risk modelling• Quantitative schedule risk models• Specific risk modelling

Traditional Probability Distribution Functions

@Risk contains a wealth of distribution functions. Most are useful to the in depth simulation requiring sophisticated tools. Only a few are suitable for general cost modelling

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Typical Probability Distribution Functions

Risk UniformUsed when we have no idea what the

value is between two limits

Risk TriangleMost Popular distribution to show Most

Likely value tapering to a Min and Max

Risk TrigenModification of TriangleAllows for a finite probability of

achieving Min & Max Values

Fundamental Flaw of Triangle and Trigen is when the distribution is skewed

Most Likely =5, Min =4, Max =15Most Likely = 5Mean = 8P50= 7.58

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Most Likely = 5Mean = 6.5P50= 6.16

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AACE International Recommended Practice No. 41R-08

RISK ANALYSIS AND CONTINGENCY DETERMINATION USING RANGE ESTIMATING

• Monte Carlo software for risk analysis requires identification of a probability density function (PDF) for each critical item. In rare instances the behavior of a critical item is known to conform to a specific type of PDF such as a lognormal or beta distribution, which reflects items that may skew heavily to one side of a distribution. However in most instances it is unlikely that the actual type of PDF that truly represents the item is known. Thus a reasonable approximation is to use either

- Triangular Distribution

- Double Triangular Distribution

In most cases, the double triangular distribution is a better approximation since it can be made to conform to the implicit skew of the project team’s probability assessment. The double triangle allows the risk analyst to use the probabilities which the project team believes are reasonable rather than letting the triangular distribution dictate a probability which, more often than not, is invalid.

Double Triangle Method

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a c b

Area = Underrun Probability

Area = Overrun ProbabilityP

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Random Variable x

Most Likely = 5Mean = 6.5P50= 5.0

Double Triangle Method

F1= 2*Urun/MinF2= 2*(1-Urun)/MaxRF=1+RiskGeneral(Min,Max,{0,0},F1:F2,RiskStatic(0))

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Min Max Urun F1 F2 RF

-10% 20% 30% 6 7 1.00

Min ML Max

UrunPro

babi

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F1

F2

1-Urun

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Schedule Risk

Quantitative Schedule Risk AnalysisHusky uses Primavera P6 to schedule all

projects greater than $5MM. Oracle Primavera Risk, formerly known as Pertmaster, is used for quantitative schedule risk analysis (QSRA)

To bring in the cost element of schedule delay, the results of the QSRA were brought into the cost model as a set of percentiles “days delay” from P0 to P100.

Use of the @Risk Fit Manager was used to fit the best curve to this profile, with options set to update the curve each simulation. This simplified the update process each time the schedule model changed.

The schedule @Risk function was then multiplied by another @Risk function that represented the uncertainty in “cost per day” to give an effective cost for schedule slippage

Schedule Cost Risk

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=RiskLoglogistic(-1063,1088.2,38.361, RiskFit("Schedule","RMSErr"), RiskName("Schedule"), RiskStatic(0))

27/06/2013

0% 25-Feb-13 -1225% 07-May-13 -51

10% 23-May-13 -3515% 04-Jun-13 -2320% 13-Jun-13 -1425% 21-Jun-13 -630% 28-Jun-13 135% 05-Jul-13 840% 11-Jul-13 1445% 17-Jul-13 2050% 22-Jul-13 2555% 28-Jul-13 3160% 02-Aug-13 3665% 09-Aug-13 4370% 16-Aug-13 5075% 23-Aug-13 5780% 01-Sep-13 6685% 11-Sep-13 7690% 24-Sep-13 8995% 12-Oct-13 107

100% 24-Dec-13 180

Use of a Systemic Risk tool

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Systemic RiskSystemic risk drivers such as the level of project scope definition affect

individual, disaggregated estimate line items in a way that is hard to see and predict.

• Best practice is to address systemic risk drivers using empirical knowledge (from historical data) to produce stochastic models that link known risk drivers (e.g. scope definition) to bottom line project cost growth.

• It was decided that this approach would be best suited for the Process Plant, which formed the major part of the cost estimate.

• Conquest Consulting Group , who have wide experience in the use of such tools, were engaged to construct a parametric model which used a series of questionnaires to the project team based on :

- Contractor Organisation- Contractor Experience- Project Planning- Execution Strategy- Scope Definition

• Fit Manager was again used to integrate the results from the parametric model into the overall model

Systemic Risk Questionnaire

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Case Description:Date:

2 Enter Contractor Bid Value 1,079,000 ($ thousands) Currency Canadian$Enter Execution Schedule Bid Duration 39.8 (months)

3Rating

Overall Contractor Capabilities 3

Contractor Organizational Structure 3

Alignment of JV Partners 3

Key Project Personnel Proposed 4

Contractor Ramp-up Strategy and Capability 3

Contractor Resource Capability and Availability 4

Alignment with Subcontractors and Suppliers 3

Contractor Organization Evaluation 3.3

Ratings: 0=N/A, 1=Greatly Exceeds, 2 = Exceeds, 3 = Meets, 4 = Does Not Meet, 5 = Fails (EXPECTATIONS)

added note:

Does not meet expectations with regard to the key personnel proposed for the project (years of experience, applicable experience, guarantee of staying with the project, etc.)added note:

Meets expectations with regard to the proposed ramp-up strategy and capability to adequately resource the project in a timely fashion.

added note:

added note:

added note:

added note:

Meets expectations with regard to the proposed organization structure to support the project.

BID MATURITY WORKSHEET

SNAM9/30/2010 & 10/22/2010

Meets expectations with regard to overall capabilities (including financial strength, reputation, experience, resources, etc.).

Contractor Organization Evaluation

Meets expectations with regard to alignment of the contractor with sub-contractors and suppliers.

added note:

Meets expectations with regard to clear and documented alignment of JV Partners.

Does not meet expectations with regard to resource capabilities and availability (such as number of in-house resources and capability to contract resources).

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Specific Risk Input

Probability and ImpactSpecific Risks consist of both a probability of occurrence, and an impact should they occur• Risk Probability modeled as simple binomial to simulate Yes/No• Risk Impact modeled as a PDF to give uncertainty of the impact• Risk outcome modeled as a product of Probability (0 or 1) and Impact

ProblemsPrior to release of @Risk 5.0 there were inherent problems using this method in that Tornado Diagram showed both

Probability and Impact as two separate risk inputs

RiskMakeInput@Risk5.0 onwards allowed for use of RiskMakeInput to combine the two together.Use extract from Risk Register to directly map risks into the model

Risk Amount = RiskMakeInput( (RiskBinomial(1, Prob,RiskStatic(0))* RiskUniform( Min,Max)),RiskName(Description))

RiskID Description Score Prob Min $ Max $ Risk amount $FFH-013 Construction in advance of sufficient engineering A3 5% 2,000,000 3,500,000 0

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Risk Results

Summary ResultsIndividual Risk models were established on separate MSExcel sheets and outputs

summarised on tabular results sheetExtensive use made of the @Risk function RiskPercentile to provide tabular output that

could be copied and pasted direct into reports

Note the values in the above table are for a fictitious project

$MM Estimate Risked Value 50% 70% 90% P50 P70 P90Process Plant $918.3 $918.3 $1,038.2 $1,107.7 $1,209.4 13.1% 20.6% 31.7%SubSurface Development $1.6 $1.6 $1.8 $2.0 $2.3 16.4% 27.2% 42.9%Drilling & Completions $193.8 $193.8 $208.4 $217.0 $229.3 7.6% 12.0% 18.3%Infrastructure $124.4 $124.4 $133.4 $137.4 $143.3 7.2% 10.4% 15.2%Midstream $30.2 $30.2 $33.2 $34.6 $36.6 9.9% 14.6% 21.2%Business & System Management $11.3 $11.3 $12.8 $13.2 $13.9 13.2% 16.8% 22.7%Ow ners Costs $156.6 $156.6 $171.7 $176.4 $183.2 9.6% 12.6% 17.0%Additional Specif ic Risk $.0 $.0 $22.3 $28.2 $38.1TOTAL PROGRAM $1,436.2 $1,436.2 $1,623.6 $1,693.4 $1,798.6 13.0% 17.9% 25.2%Management Reserve $.0 $.0 $17.9 $30.7 $59.6Overall Total $1,436.2 $1,436.2 $1,648.2 $1,717.8 $1,825.1 14.8% 19.6% 27.1%

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Summary

The cost risk model built in @Risk used a variety of techniques to both represent uncertainty from ranging workshops and from other studies. Highlights include:

• Use of the RiskGeneral function to mimic the Double Triangular Distribution recommended by AACE for range estimating

• Use of the Fit Manager to replicate output from other models as input to the overall risk model, in particular:- Results from a quantitative schedule risk analysis- Results from systemic parametric risk analysis- Automatic recalculation of the curve fit on change of input data

• Use of RiskPercentile and other output functions to provide templated report formats that can be copied and pasted direct into presentational materials

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Any Questions