Javier Ordonez - Using @RISK in Cost Risk Analysis

8/6/2019 Javier Ordonez - Using @RISK in Cost Risk Analysis

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Using @RISK in Cost RiskAnalysis

Javier Ordez, Ph.D.Director of Custom SolutionsPalisade Corporation


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Outline

Introduction

Background

Project Performance Record

Definitions

Cost Risk Analysis

Correlation Schedule Integration


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Introduction

Most projects are conducted in a changing environment;

this makes the schedule and cost analysis difficult in the

early stages.

Traditionally, cost and duration estimates are point

estimates. Estimation based on the most likely values.

It is necessary to study uncertainties involved in the

project

.


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Project Performance Record

Project Success (RMC Project Management) Only 28% of all projects succeed Time to market can be improved by 65%

Projects can be completed in 50% of the time

IT Projects (Chaos Report) 31% of project cancelled before completion 53% of projects will cost 189% of their original

estimate

Average time overrun is 222% Average project success is 16.2% (software

projects)


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Background: PRA Adoption in Federal &State Agencies

Federal Transit Administration (FTA) requires a riskassessment/mitigation study for any new transit project applying forfederal funding

Department of Transportation of the State of Washington (WSDOT)has a risk-based approach to validate cost estimates

OMB Capital Programming Guide, 2007: Risk Adjusted Budget andSchedule (ANSI/EIA Standard 748)

DoD Integrated Master Plan and Integrated Master SchedulePreparation and Use Guide: Schedule Risk Analysis

Risk Management Guide for DoD Acquisition (2003)


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Definitions: Project Risk & Uncertainty

Project risk is defined as the possibility that the outcomeofan uncertain eventaffects negatively or positively the costand time performance of project activities and/or theirplanned execution

Risk = Consequence x Probability of Occurrence

Uncertainty is defined as the lack of knowledge about the

parameters that characterize the system


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Project Budgeting

Typically budgets are deterministic

Simulation Approach Individual cost components are unimodal and skewed

Common use of 3 point estimate and triangular, beta,lognormal distributions

Model cost items prone to variation with suitablestatistical distributions

Generate random numbers hundred of timesaccording to specified distributions and calculate total

cost Total cost dist is used to calculate probability of cost

overrun and to establish adequate contingencies


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Triangular DistributionsDescription

Used when minimum,maximum, and most likelyvalues are known.

Used when high and lowthresholds are of equal

distance to expectedoutcome.

Easy to calculate andgenerate, but limited abilityto accurately model real-

world estimates.Examples

Product pricing

Cost to manufacture

Most likely (mode) Minimum & maximum values Shift (optional)

Inputs


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PERT DistributionsDescription

Alternative to Triangular

Same 3 parameters, butuses smooth curvedeemphasizes tails

Provides most-likely caserather than extreme values

Describes outlying impactsmore realistically

Examples

Product pricing

Manufacturing costs

Sales volumes

Raw material pricing

Most likely (mode) Minimum & maximum values Shift (optional)

Inputs


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Triangular vs. PERTDistributions

Comparison

More closely resemblesrealistic probability distribution.

Provides close fit to normal orlognormal distributions

Like the Triangular distribution,emphasizes most likely value

over minimum and maximumestimates.

Unlike Triangular, proves asmooth curve thatprogressively emphasizesvalues around (near) the mostlikely, over values around

edges. Can trust estimate for most

likely value. Even if it notexactly accurate, will be close.

Produces a curve similar toNormal, without knowingprecise parameters.

Triangular distributions are fine for symmetrical data PERT more accurately depicts normal distributions Use PERT when the min, max, and most likely are known

Key Takeaways


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Risks Events vs. Uncertainty

Probability

$ o Time

Risk Events ImpactsUncertainty


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Qualitative Risk Analysis

1 2 3 4 5

1

2

3

4

5

Likelihood

Consequence

Likelihood ScoreNot Likely 1

Low

Likelihood 2

Likely 3

High Likely 4

Near

Certainty 5

Schedule Cost Technical Score

Minimal or no impact Minimal or no impact Minimal or no impact 1

Additional activities required; able to meet

key datesBudget increase


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Binomial DistributionsDescription

RiskBinomial(n,p) = probabilityof achieving certain number ofsuccesses in nindependenttrials, where probability ofsuccess for each trial is p, andeach trial has only two possible

outcomes (success or fail)

Describes the outcome of aseries of trials that can only bea success or failure.

As the average increases, theprofile approaches the Normaldistribution. Under someconditions, you can use theNormal distribution as anapproximation.

n = number of outcomes p = probability of each outcomes occurrence

Min, max & shift (optional)

Inputs

Examples

Heads or tails in coin tosses

Occurrence of a risk event14


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RiskCompoundFunction

It uses two distributions to create a single new inputdistribution.

The first argument specifies the number of samples whichwill be drawn from the distribution entered in the secondargument.

For example, the function:

RiskCompound(RiskPoisson(5),RiskLognorm(10000,10000))

It would be used in the insurance industry where the frequency or

number of claims is described by RiskPoisson(5) and the severityof each claim is given by RiskLognorm(10000,10000).

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Contingency calculation w/o PRA The percentage figure is, most likely, arbitrarily arrived at and not

appropriate for the specific project.

There is a tendency to double count risks because some estimators

are inclined to include contingencies in their best estimate.

A percentage addition still results in a single-figure prediction of

estimated cost, implying a degree of certainty that is simply not

justified.

The percentage added indicates the potential for detrimental or

downside risk; it does not indicate any potential for cost reduction and

may therefore hide poor management of the execution of the project.

Because the percentage allows for all risk in terms of a costcontingency, it tends to direct attention away from time, performance,

and quality risks.

It does not encourage creativity in estimating practice, allowing it to

become routine and mundane, which can propagate oversights.


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Correlation and

Interdependence Variables move together

Positive vs. Inverse relationship

Predictive sampling (magnitude)

Correlation coefficient

Calculating rho r

Methodology (rank vs. data)

Impact

Comparing effect on m vs. s18


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Correlation Characteristics

Variables must relate to each other in some manner

Correlations are often calculated from actual historical data

Correlation coefficients range between -1 and 1

0 = no relationship

-1 = complete inverse correlation

1 = complete positive correlation

Variables without correlation create non-realistic situations


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Correlation Concept

Measures the degree of associationbetween 2 variables

y

x

r = -1

y

x

r = 1y

x

r = 0

y

x

r = -.8

y

x

r = .8


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Cost Correlation Issues

If correlation is ignored the total cost variance is

underestimated

Data limitations during planning stages of mostengineering projects

Correlation between variables makes use of historical

data or subjective estimation from experts

Relationship between variables are shaped by manyuncontrollable factors, and are best at subjectiveestimates based on experience and judgment

PDF that cost estimator specifies is the marginaldistribution of that cost item; if cost items arecorrelated, the joint density function of the cost itemsneeds to be calculated


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Correlation Effects

S-Curve for Correlated and Not

Correlated Durations

0.00.10.20.30.4

0.50.60.70.80.91.0

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Date

Prob

Value

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Javier Ordonez - Using @RISK in Cost Risk Analysis