Cost and schedule risk modelling - Range analysis
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Transcript of Cost and schedule risk modelling - Range analysis
Creating value from uncertaintywww.Broadleaf.com.au
Cost and schedule risk assessment
PMI Australia Conference 2014
Presented by Dr Stephen Grey
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ThemeA lot of quantitative risk assessment is unnecessarily cumbersome and soaks up effort without adding value to a project.
The approaches used often make it very hard to think clearly and realistically about a project.
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Modelling
Reality Belief
Model
Project
∑
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Describing a model
Assumptions
Sources of uncertaintyDependencies
Cost
The requirement
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Requirement
Project ∑
Target
Risk of exceeding target
Inputs $Duration
The candidates
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Three methodsRisk events• List risks• Probability and impact (range of variation)• Add them upLine items• Line items from estimate (summary)• Range of variation in line item• Add them upRisk factors• Cost or duration estimating relationships• Uncertainty in inputs to relationships (ranges)• Simulate the effect on the base case
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Similarities
Rela onships in the model ≡ Rela onship in reality?
Is the link between inputs and reality easily understood?
Almost any attention to project risk will be beneficial
Some are more cost‐effective than others
Model
Risk event model
Risk Probability Impact P x IRisk 1 P1 I1 P1 x I1Risk 2 P2 I2 P2 x I2Risk 3 P3 I3 P3 x I3Risk 4 P4 I4 P4 x I4… … …
Risk n Pn In Pn x InTotal ΣPi x Ii
Mon
te Carlo simulation
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Modelling based on risk events
∑
Target
Risk of exceeding target
Risk Probability Impact P x IRisk 1 P1 I1 P1 x I1Risk 2 P2 I2 P2 x I2Risk 3 P3 I3 P3 x I3Risk 4 P4 I4 P4 x I4… … …
Risk n Pn In Pn x InTotal ΣPi x Ii
Mon
te Carlo sim
ulation
Line item modelling
WBS Item Labour Materials Total Contingency
Annnn $ $ $ $
Bnnnn $ $ $ $
Cnnnn $ $ $ $
Dnnnn $ $ $ $
… … … … …
Znnnn $ $ $ $
Total Σ$ Σ$ Mon
te Carlo sim
ulation
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Line item modelling
∑
Target
Risk of exceeding target
WBS Item Labour Materials Total Contingency
Annnn $ $ $ $
Bnnnn $ $ $ $
Cnnnn $ $ $ $
Dnnnn $ $ $ $
… … … … …
Znnnn $ $ $ $
Total Σ$ Σ$ Mon
te Carlo sim
ulation
Project estimate summaryLabour 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 ?
Project Total ? ? ? ? ? ? ? ?
Risk factors model
Uncertainty about quantities of concrete (m3)
Uncertainty about rates for cost of concrete ($/m3)
Cost estimating relationships (e.g. Cost = Quantity x Unit rate)
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Simulated cost = Base estimate x (1 + ΔQuantity) x (1 + ΔRate)
∑
∑
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Risk factor modelling
Target
Risk of exceeding target
Project estimate summaryLabour Facilities Super‐
visionMaterials Sub‐
contractsServices Expenses Total
Earthworks 1234567 1234567 1234567 1234567 1234567 1234567 1234567 ?
Concrete 1234567 1234567 1234567 1234567 1234567 1234567 1234567 ?
… 1234567 1234567 1234567 1234567 1234567 1234567 1234567 ?
Overheads 1234567 1234567 1234567 1234567 1234567 1234567 1234567 ?
Project Total ? ? ? ? ? ? ? ?
Uncertainty about quantities of concrete (m3)
Uncertainty about rates for cost of concrete ($/m3)
Simulated cost = Base estimate x (1 + ΔQuantity) x (1 + ΔRate)
∑
Making it work
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Risk event
No effect
Probability = 33%
Probability = 67%
Likely=$4MMin =$2M Max=$8M
Variation in costif the event occurs
No effect
Probability = 33%
Probability = 67%
Likely=$4MMin =$2M Max=$8M
Variation in costif the event occurs
Project ∑Inputs Outputs
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Risk event model
Project ∑Inputs OutputsProject ∑Inputs Outputs
Project ∑Inputs OutputsProject ∑Inputs Outputs
Project ∑Inputs OutputsProject ∑Inputs Outputs
Project ∑Inputs OutputsProject ∑Inputs Outputs
Project ∑Inputs Outputs
Target
Risk of exceeding target
Common factors, overlaps and interactions
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Line item range
Inputs Outputs∑
Cost line item
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Line item model
Target
Risk of exceeding target
Common dependencies and correlations
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Risk factor model
Inputs Outputs∑
Cost drivers
Risk factors
Riskfactor
Target
Risk of exceeding target
Risk factor example (partial)Small IT development project
Professional services $
Software scale
Team productivity
Professional services rates
Duration
Overhead rates ($/month)
Overhead $
Market rates for licenses
Number of users
Design decision
Option 1
Option 2
Installed license cost $Contingency assessment
methods & trends
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Electrical & instrumentation
Steel, mechanical & piping
Concrete
Earthworks
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 $
Contingency assessment methods & trends
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Assessing input parameters
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Describing a rangeCommon practice
Outcome1 23
1. Start in the middle and work out
2. Explain reason for choosing numbersAnchoring bias
Confirmation bias
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Describing a rangeGood practice
1. Establish context
• Assumptions
• Sources of uncertainty
• Pessimistic and optimistic scenarios
2. Start with the extremes and work in
Pre‐empt confirmation
Break anchoring
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Describing a rangeCommon practice
Outcome12 3
Assumptions
Sources of uncertainty
Scenarios
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SummaryModelling using risk factors is generally simpler and more realistic than using structures based on risk events or line item ranging
Context setting and methods to limit anchoring are required to get realistic assessments of uncertain values’ ranges
It need not be hard work!
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ContactDr Stephen GreyAssociate [email protected]+61 412 223 256
If you would like further information about this topic please contact us.
For further information visitwww.Broadleaf.com.au
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