martin hopkinson: uncertainties are risks

© Risk Management Capability Limited 2013

RiskRisk

“Uncertainties”are Risks

Martin Hopkinson

© Risk Management Capability Limited 2013 Slide No: 2

Examples of “Uncertainties”

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A Worked Example

Take on and Deliver Staff Skills Training Programme

A large organisation is contracting out their employee skills training programme

• 80 different training courses covering wide range of skills • Training volume - 100,000 employee-days• Class sizes – 15 employees• Year 1 – Phased takeover of training• Years 2 to 10 – Deliver all training & course development

You are bidding for this contract

Annual cost of Trainers’ salaries is a key risk


A Simple model for Trainer’s Salaries

Baseline Min Mode Max SimulationStudent-days requirement 100,000 R = 100,000Students per course 15 10.0 12.5 14.0 10.0<W<14.0Trainers per course (average) 1.80 1.6 1.8 2.0 1.6<X<2.0Utilisation 75% 40% 65% 80% 40%<Y<80%Trainer demand (man days) 16,000 T = R*X / W*Y

Working days per year 230 A = 230Trainer Full Time Equivalents 69.6 F = T / A

Mean salary (£k) 25 24.0 27.0 30.0 £24.0k<Z<£30.0k

Annual salaries cost (£k) 1,739 S = F*Z


A Simple model for Trainer’s Salaries



Mean salary (£k) 25 24.0 27.0 30.0 £24.0k<Z<£30.0k



A Simple for Trainer’s Salaries



Mean salary (£k) 25 24.0 27.0 30.0 £24.0k<Z<£30.0k


Monte Carlo simulation of this cost


Cost Model Results

0%

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0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000

Annual Cost of Trainers' Salaries (£K)

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Cost Risk Curve

Baseline Cost

P50 = 2,817mBase = 1,739m


Effect of “Uncertainties” on the Curve

Baseline Min Mode MaxStudent-days requirement 100,000 Students per course 15 10.0 12.5 14.0Trainers per course (average) 1.80 1.6 1.8 2.0Utilisation 75% 40% 65% 80%Trainer demand (man days) 16,000

Working days per year 230 Trainer Full Time Equivalents 69.6

Mean salary (£k) 25 24.0 27.0 30.0

Annual salaries cost (£k) 1,739

1. Mode points may not equal the baseline value

2. Probability density functions may not be symmetrical


Baseline

75%

Utilisation Probability Density FunctionP

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65%

80% Min Mode Max

The baseline is often optimistically biased – it can be dangerous to anchor three-point estimates around a baseline estimate


Effect of “Uncertainties” on the Curve

Baseline Min Mode MaxStudent-days requirement 100,000 Students per course 15 10.0 12.5 14.0Trainers per course (average) 1.80 1.6 1.8 2.0Utilisation 75% 40% 65% 80%Trainer demand (man days) 16,000


Mean salary (£k) 25 24.0 27.0 30.0


1. The baseline is sometimes not a plausible forecast

2. The baseline is then outside the PDF


Class Size Probability Density Function

Baseline

15%

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Key point – Never use Baseline +/- x% to produce three-point estimates !


Can “Uncertainties” be Prioritised?

Risk Model Variables Cruciality

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

Utilisation

Students per course

Mean salary (£k)

Trainers per course


Can “Uncertainties” be Managed?

Utilisation (Min = 40%, Max = 80%)

Sources of uncertainty:

• Timetabling capability• Flexibility of client to work with optimised timetable• Quality of Training course materials • Ability of trainers to teach different skills

1. These factors can be managed 2. Some should be addressed contractually


Can Other “Uncertainties” be Managed?

Construct walls (duration)

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Yes! Yes!

Yes! Yes!


“Uncertainties” and Risk Management

1.Are a factor that contributes to overall risk 2.Can be described by identifying context,

sources of uncertainty and effects3.Can be prioritised4.Can often be managed pro-actively

“Uncertainties”:

Significant “uncertainties” are Risks

Variability RisksRef - Prioritising Project Risks (APM 2008)


0.9 V HI 0.045 0.09 0.18 0.36 0.72 0.72 0.36 0.18 0.09 0.045 0.9 V HI

0.7 HI 0.035 0.07 0.14 0.28 0.56 0.56 0.28 0.14 0.07 0.035 0.7 HI

0.5 MED 0.025 0.05 0.10 0.20 0.40 0.40 0.20 0.10 0.05 0.025 0.5 MED

0.3 LO 0.015 0.03 0.06 0.12 0.24 0.24 0.12 0.06 0.03 0.015 0.3 LO

0.1 V LO 0.005 0.01 0.02 0.04 0.08 0.08 0.04 0.02 0.01 0.005 0.1 V LO

V LO LO MED HI V HI VHI HI MED LO V LO

0.05 0.1 0.2 0.4 0.8 0.8 0.4 0.2 0.1 0.05

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Impact Benefit

Threats OpportunitiesArrow of Attention

Risk Registers

Could you include variability risks in risk registers?

Yes – good idea! … but there is a long-standing barrier

The “PIG”

Inability to accommodate variability risks is one of the Top 10 reasons why the PIG is a poor technique


Probability – Impact Picture (APM 2008)

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-4,000,000 -3,000,000 -2,000,000 -1,000,000 - 1,000,000 2,000,000 3,000,000 4,000,000

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Event-orientated Risk Management

Some penalties that you might be paying:

1. Neglecting to manage all sources of risk

2. Labeling “100% probability” risks as issues

3. Lazy estimating of “uncertainties” in risk models

4. Failure to model compound or complex risk effects

5. Estimates biased by force fitting of risks to the PIG


The PRAM Guide Process and Composite Risks


PRAM Guide Process

Project Delivery

Top-down Multi-pass process


Worked Example – Training ContractC

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Management TrainersCourse development IT

First Pass Cost Risk Model:

Focus for next pass of the risk management process is identified as being cost of Trainers

Prioritisation12 34

Four Composite Risks

Cost (£)


Second Pass of the Process

Simple model for Trainers’ Salaries Baseline Min Mode Max

Student-days requirement 100,000 Students per course 15 10.0 12.5 14.0Trainers per course (average) 1.80 1.6 1.8 2.0Utilisation 75% 40% 65% 80%Trainer demand (man days) 16,000


Mean salary (£k) 25 24.0 27.0 30.0


These are still composite risks

1. The highest priority risk is Utilisation2. Next pass of this model might include more detail within Utilisation risk


PRAM Guide Process

Top-down Multi-pass process

Composite Risks are often modelled as variability risks

Composite Risks are not often modelled as event risks


Event-orientated Risk Management

A summary of penalties that you might be paying:

6. Inability to adopt best practice multi-pass approach

7. Incoherent quantitative risk models

1. Neglecting to manage all sources of risk

2. Labeling “100% probability” risks as issues

3. Lazy estimating of “uncertainties” in risk models

4. Failure to model complex risk effects

5. Estimates biased by force fitting of risks to the PIG


What does Uncertainty Mean?

Uncertainty – Lack of Certainty

A Variability Risk

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Lack of certainty about outcome over a continuous range

An Event Risk

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Lack of certainty about occurrence and lack of certainty about effect

Uncertainty is a property of all risks

Significant “uncertainties” are risks

martin hopkinson: uncertainties are risks

Business

course trainers

different training courses

year trainer

employeedays class sizes

annual salaries cost

employees year

time equivalents

large organisation