Quantitative Risk Analysis
How to Spot Bad Practice
Peter Wood
Peter Wood Associates
Decision Support Services
+44 1244 332610
+44 7769 675435
peter@peterwoodassociates.co.uk
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The Purpose of Risk Analysis

To predict the possible impact of uncertainty on
an expected outcome, and to help understanding
of the contributory factors.

It’s not about accuracy but is aiming to expose the
degree of inaccuracy and its causes.

The goal should be better decision-making, based
on a realistic view of possible outcomes.
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Bad Practice – the Tell-Tale Signs
 Baseline
data unreliable.
 Inappropriate risk logs.
 Mediocre modelling.
 Outputs misleading.
 Decision-makers not objective.
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Baseline Data Unreliable
 Use
of data designed for other purposes
 Project schedules with 100’s of activities
developed for resourcing / costing.
 Cost plans at component level.
 Estimates
credited with unrealistic accuracy.
 Uncertainty, assumptions and omissions are
un-quantified (or at best given default values)
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Detailed Project Schedule
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Simplified Logic Network
overheads
award
contract
offshore
mobilisation
establish
crushing
plant
construct
dry gut dam
construct
culvert
major earthworks
/ fill dry gut
complete
airfield
pavement
jetty
construction
1st
del
commission
& prepare for
operations
procure
plant
1st
order
establish
shipping
yard
additional
surveys
establish
water
supply
construct
access
road
earthworks
to terminal
area
airport
available
O&M
complete airport
facilities & terminal
design
access road
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Risk Logs Unsuitable

Too many risks!
 Duplicated / generic / trivial
No consolidation, so root causes and key
issues not recognised.
 Evaluation assumes that future mitigating
actions will be applied and will be effective
(and often at no cost!).
 Uncertainty not seen or treated as risk.

(Risk = an event that may impact the project)
(Uncertainty = anything we’re not sure about)
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Combined Impact
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Mediocre Modelling
Software limitations.
 Inconsistent approach.
 Poor spreadsheet practice

 Input factors hidden in calculation cells.
 Not designed to allow analytical testing.
Time and cost not integrated.
 Risks converted to subjective uncertainty.
 Reliance on ‘patent’ distributions.
 Assumption of independence.

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Independence V Correlation
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Independence V Correlation
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Distributions - comparison
uniform, triangular, pert
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Distribution results
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Distribution results
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“Natural” risk modelling
Simplified logical representation.
 Uncertainty ranges represent estimating tolerance.
 Allows compound variables and correlation to be
expressed where appropriate.
 Risks represent events.

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Outputs Misleading
Spurious accuracy.
 Absence of interpretation.
 Focus on narrow band within possibilities
eg 50 – 80% confidence levels


Uncertainty, assumptions and omissions still
un-quantified.
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Decision-Makers Not Objective
Don’t trust risk analysis.
 Lack of challenge - limited understanding?
 Hidden agendas.
 Content to consider a narrow band of results.

 Below 50% never happens?
 Above 80% too bad to think about?

Conspiracy of optimism.
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The Goal
A realistic view of possible outcomes
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The Actuality




Significant risks excluded & assumptions made.
Uncertainty suppressed.
Related risks and uncertainty not recognised.
Risk evaluation that hopes for the best.
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Good Practice Indicators






Seniors demand realistic predictions, want to understand
influential factors and are involved throughout.
Outputs tell the whole story, with key risk drivers.
Models are scrutinised and stand up to challenge.
Intelligent thought is applied to risk logs prior to modelling,
and evaluation is based on the current position.
Technicians, planners and estimators express uncertainty
alongside their ‘best-guesses’ as a matter of course.
Baseline information is distilled to the right level of detail.
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Quantitative Risk Analysis
How to Spot Bad Practice
Peter Wood
Peter Wood Associates
Decision Support Services
+44 1244 332610
+44 7769 675435
peter@peterwoodassociates.co.uk
© Peter Wood Associates
Decision Support Services
21