contingency webinar march 2014 pmi
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Contingency Webinar March 2014 PMITRANSCRIPT

Contingency Assessment Methods & Trends
Dr Stephen Grey, Associate Director, BroadleafMarch 19, 2014

Contents
Overview
Monte Carlo simulation
Context
How we got here
Two big problems
Alternative approach
2

Overview
Quantitative evaluation of project cost contingency has become stuck in outdated methods
Common practice is not good practice
Project size and complexity took off too fast for computing power and availability to keep up
Manual methods became automated rather than better methods being taken up when computing became more accessible
We can now adopt better practice and make quantitative analysis more efficient and realistic

Monte Carlo simulationVery brief summary
Distributions represent values and probabilities represent events
Thousands of examples of possible outcomes sampling distributions and calculating outcomes
Interpret the results as an indication of what could happen in reality
Cost
No effect
33%
67%
$4MEvent
Model
Impossible
Risky Manageable
Safe

Context
5
Estimate $X $C+
QuantitativeRiskAssessment
No standards
MethodMethodMethodMethod? ?
Entrainedthinking

How did we get here?
6
1960
1970
1980
1990
2000
2010
Projects used to organise workCritical Path Method
PROJECTS COMPUTING
Bureau facilities  specialist systems designers and programmers
Work Breakdown StructureCPM on large jobs
Some users coding overnight turnaroundLaborious computer CPM input
Projects become more complicatedespecially in IT and communications
Personal computing for someIndividual access to CPM and MCS
Projects become bigger Personal computing everywhereMCS widespread

Methods of assessing contingency
Model structures Risk events Line items Risk factors
Calculation Manual Computer assisted Monte Carlo simulation with distributions
7

Risk event models
No effect
Probability = 33%
Probability = 67%
$4M$2M $8MONE EVENT
Expected value = 33% x $4MExpected value = 33% x Mean

Risk event models
ALL THE RISKS
RISK Probability Impact PxIRisk1 P1 I1 P1 xI1Risk2 P2 I2 P2 xI2Risk3 P3 I3 P3 xI3Risk4 P4 I4 P4 xI4
Riskn Pn In Pn xInTotal Pi xIiM
onte
Car
lo s
imul
atio
n

Line item models
WBS ITEM Labour Materials Total ContingencyItem 1 $ $ $ $Item 2 $ $ $ $Item 3 $ $ $ $Item 4 $ $ $ $ Item N $ $ $ $Total $ $ M
onte
Car
lo s
imul
atio
n

ProjectestimatesummaryLabour Facilities Super
visionMaterials Sub
contractsServices Expenses Total
Earthworks 1234567 1234567 1234567 1234567 1234567 1234567 1234567 ?
Concrete 1234567 1234567 1234567 1234567 1234567 1234567 1234567 ?
1234567 1234567 1234567 1234567 1234567 1234567 1234567 ?
Overheads 1234567 1234567 1234567 1234567 1234567 1234567 1234567 ?
ProjectTotal ? ? ? ? ? ? ? ?
Risk factors model
Uncertainty about quantities of concrete
(m3)
Uncertainty about rates for cost of concrete ($/m3)
x x
The only practical way to evaluate a risk factor modelis to use Monte Carlo simulation on a computer
Cost estimating relationships (e.g. Cost = Quantity x Unit rate)

How timing affected what we do now
1960
1970
1980
1990
2000
2010
RISK EVENTS LINE ITEMS RISK FACTORS
All we have knownMust be rightDe facto standard
World BankPouliquen et al
Six monthsto build
simulation

Why the risk factor approach?Problems with risk event models
Many risks are not really events but variations from the estimating assumptions vary continuously across a range Staff productivity Rates for office space How long the work will go on and incur overheads
Some risks affect more than one part of the cost
One part of the cost will be affected by more than one risk
Two or more risks will often interact
Trying to represent uncertainties as discrete separate events is inefficient and confusing

Risk factors model process plant construction
COSTS
RISK FACTORS
RISKS
Concrete quantity
Concrete $Steel $
Steel quantity
One riskaffecting
two factors Two risksaffecting
one factor
Vibrationcharacteristics
Geotechnicalcharacteristics
Other factor(s) Other factor(s)
Other risks

Risk factors model IT system roll out
COSTS
RISK FACTORS
RISKS
Unit cost of licenses
License costs $
Professional services $
Roll out effort
One riskaffecting
two factors Two risksaffecting
one factor
Decision onoperating system
Commercialarrangementsfor licenses
Other factor(s) Other factor(s)
Other risks

Why the risk factor approach?Problems with line item models
Separate lines are often not independent of one another Steel cost uncertainty will affect every item with steel in it Salary cost uncertainty will affect every item with staff costs
In reality line item uncertainties are correlated
In principle, it is technically feasible to model the correlations realistically in a line item structure but its not practicable
Line item models often either understate the uncertainty in the total cost, because they ignore correlation, or lack credibility due to relying on correlations that cannot be justified

Effect of missing correlation
Realisticcorrelation
Correlationmissing
Target cost
Like
lihoo
d of
aris
ing
False sense of accuracy and confidence

Why does missing correlation matter?
Target cost
Ris
k of
exc
eedi
ng ta
rget
Required level of confidence
Understatement of funding requirements

Risk factorsKey points
Based on cost estimating relationships that people already understand (other effects and relationships where required)
Draw together the effects of risks that overlap
Represent explicitly the relationships that cause correlations
Represent the interactions between uncertainties
Capture the continuous nature of variations
Relatively simple models that are realistic and easy to understand (typically 2040 factors)
Evaluated using Monte Carlo simulation

Early example of risk factors modelPouliquen, L.Y., 1970, Risk Analysis in Project Appraisal, World Bank Staff Occasional Papers, No. 11, Johns Hopkins Press, Baltimore
Ris
k fa
ctor
s
Key
fact
orsTotal
Distribution parameters

Assessing the range on one risk factorAvoid bias, document analysis
Assumptions embodied in base estimate (key ones)
Sources of uncertainty
Pessimistic and optimistic scenarios
Range of possible outcomes (P10, Most Likely, P90)

Example of a risk factor assessmentRate for office space for project team ($/m2/mth)
Assumptions Rate based on informal enquiries with property agents X square metres per person plus 10% allowance Lease starting mid next year and running for 24mth
Sources of uncertainty Market demand in the 23mth before lease starts Ability to obtain suitable space matched to team size, which
is still uncertain, affecting space utilisation (wasted space)

Example of a risk factor assessmentRate for office space for project team ($/m2/mth)
Pessimistic scenario Market heats up Have to lease bigger space than bare minimum required, up
to 25% more than strictly required for team size
Optimistic scenario Market cools Find space that closely matches team size

Example of a risk factor assessmentRate for office space for project team ($/m2/mth)
Pessimistic estimate (P90) Market rises ~15% Wasted space adds ~25% Net effect ~1.15 x ~1.25 = ~1.44, say 50% above estimate
Optimistic estimate (P10) Market eases ~10% Save about half the 10% allowance for wasted space Net effect ~0.9 x ~0.95 = ~0.86, say 15% below estimate
Most likely outcome As estimated

Example of a risk factor assessmentRate for office space for project team ($/m2/mth)
100%85% 150%
Office space rate uncertainty
Distribution sampledand multiplied intobase cost of office space
P10
P90

Total cost of office space
Rate uncertainty
Team size uncertainty
Base estimate costof office space $X
X
Multiply
Simulated costof office space
Add to othersimulatedproject costsin the model
Monte Carlo simulation
Sample Calculate Store

Risk factor example (partial)Small IT development project
Professional services $
Software scale
Team productivity
Professional services rates
Duration
Overhead rates ($/mth)
Overhead $
Market rates for installation licenses
Number of users
Design decision
Option 1
Option 2
Installation license cost $

Electrical & instrumentationSteel, mechanical & piping
ConcreteEarthworks
Risk factor example (partial)Mineral processing plant construction
Earthworks direct labour $
Earthworks quantity
Labour productivity
Labour rates
Lump sum valueDesign decision
Option 1
Option 2
Major equipment item cost $
Bulk material rates $/m3
Bulk material cost $

Summary
Some methods in common use today were selected when there was no alternative to pencil and paper They are not well suited to modelling project cost risk Simple methods became locked in to the way we work They have a role as part of the modelling toolkit but only part
Risk factor models were used over 40 years ago Takeup limited by access to computers Benefits identified at the outset are still relevant Rediscovered or redeveloped recently Simple, powerful and easy to understand

Questions and comments
Stephen Grey  [email protected]
Reference to the World Bank paperhttp://documents.worldbank.org/curated/en/1970/01/6751137/riskanalysisprojectappraisal
Project Risk Management Guidelines: Managing Risk with ISO 31000 and IEC 62198
Dale Cooper, Pauline Bosnich, Stephen Grey, Grant Purdy, Geoffrey Raymond, Phil Walker, Mike Wood
ISBN: 9781118820315