Upload
trinhhanh
View
215
Download
2
Embed Size (px)
Citation preview
10/09/2013 1
BAE Systems
Risk Opportunity & Uncertainty Modelling ACostE North West Region 4th September 2013
© BAE SYSTEMS PLC 2011 All Rights Reserved
The copyright in this document, which contains information of a proprietary nature, is vested in BAE SYSTEMS Public Limited Company. The contents
of this document may not be used for purposes other than that for which it has been supplied and may not be reproduced, either wholly or in part, in any
way whatsoever, nor may it be used by, or it contents divulged to, any person whatsoever without the prior written permission of BAE SYSTEMS Public
Limited Company
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 2
Agenda
Part 1
Risk Opportunity and Uncertainty – Definitions
Brief Overview of Monte Carlo Analysis
Shortfalls in Monte Carlo
Part 2
Cost & Schedule Integration
An Overview of the “Slipping and Sliding” Technique
Summary
2
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 3
Risk Opportunity & Uncertainty - Definitions
Variability within a project cost or schedule can be summarised as either:
• Uncertainty
– Something that will happen but the exact values/parameters are not known
– E.g. Normal car journey from home to work
• Risk or Opportunity
– Things which may or may not happen
– Risk
• An event or a series of events which, on occurring, would damage a
project or business objective in terms of performance, functionality,
time of delivery, customer acceptance, or cost.
• E.g. An accident on the route ahead causes a hold-up on the journey
– Opportunity
• An event or series of events which, on occurring, would offer benefit to
the project or business in terms of performance, functionality, time of
delivery, customer acceptance, or cost
• E.g. An accident behind us reduces congestion on the journey ahead
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 4
ROU Bottom Up Evaluation
In order to do a Bottom-Up assessment of Risk Opportunity and Uncertainty
(ROU), it is generally necessary to:
– Define a work package in an appropriate level of detail (tasks)
– Assign a range of likely cost and/or schedule outcomes for each task
– Link tasks that have an underlying relationship in terms of outcome in terms
of cost (or time)
e.g. Design overrun leads to increasing cost of Manufacture or Construction
i.e. Partial Correlation – often overlooked in Bottom-up ROU because
it is not properly understood
– Add the tasks together
– Review the range of outcomes
(Failure to correlate tasks appropriately will result in too narrow an output
range)
– Make recommendations based on the range of outcomes possible
This approach generally requires the use of a Monte Carlo Simulation toolset
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 5
Monte Carlo Simulation – What is it?
A Structured Approach to a Chance Encounter!
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 6
Monte Carlo Simulation
What is it? Why do we use it?
• A method of aggregating multiple distributions from independent variables in
a manner which maintains statistical correctness
• Usually not practical to combine distributions manually:
Total Probability =1
Opt = 1 Pess = 6
Total Probability =1
Opt = 1 Pess = 6
Opt = 2 Pess = 12
Total Probability =1
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 7
Monte Carlo Simulation
Why is that wrong?
• Consider throwing two conventional dice. Each has a uniform distribution.
Add together the “scores”
Die 1 2 3 4 5 6
1 2 3 4 5 6 7
2 3 4 5 6 7 8
3 4 5 6 7 8 9
4 5 6 7 8 9 10
5 6 7 8 9 10 11
6 7 8 9 10 11 12
0
1
2
3
4
5
6
7
1 2 3 4 5 6 7 8 9 10 11 12Score
Oc
cu
rre
nc
es
What is it doing?
• Looking at every possible
combination
• Not feasible in complex multi-
variable environments
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 8
Monte Carlo Simulation
Combining distributions mathematically is theoretical possible but extremely
complex and thus impractical in real terms
• For every conceivable permutation we want the product of those
distributions not the sum of them
• Monte Carlo Simulation provides an approximation shortcut to that product
S f(x) S P f(x)
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 9
Monte Carlo Simulation
Monte Carlo Simulation is a statistically valid way of adding together a number
of distributions
– Possible outcomes are defined by the user selecting an appropriate
distribution and probability of occurrence for each task
– Monte Carlo Simulation picks a value randomly from within the range of
possible outcomes defined
– The randomly generated outcomes from all the tasks can be combined
together usually by simply adding them together to create a single valid
potential outcome of the overall total (sometimes called a slice)
– This process is repeated many times to generate a distribution of
possible outcomes weighted according to the distributions chosen for
each task
The output distribution generated allows the user to interpret data using
confidence levels to define three-point estimates
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 10
Cost
Occurrences
Cost
Probability
Monte Carlo Simulation
Slice 1
Slice 3
Slice 2
-
-
-
• 10,000 Slice view
10,000 Slices
20 Slice view
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 11
ROU Bottom Up Evaluation
Baseline Task
Baseline Uncertainty & Risk
Potential Outcomes Cost or Time 0 Lik
elih
ood o
f O
ccurr
ence
Risk Register Based
Potential Outcomes 0 Cost or Time
Baseline Task + Uncertainty
Lik
elih
ood o
f O
ccurr
ence
Baseline Uncertainty
Potential Outcomes
Baseline Task
0 Cost or Time
Lik
elih
ood o
f O
ccurr
ence
S Optimistic S Pessimistic
Uncertainty Only
Model
Risk Opportunity
and Uncertainty
Combined Model
Risk and Opportunity
Only Model
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Monte Carlo Simulation
Things to do to give Monte Carlo Simulation a chance of being right
• Try to ensure that the distributions you choose are appropriate
– At least make sure that the basic distribution shape and range are right
– The majority of inputs to Schedule and Cost Monte Carlo Models are likely to be
positively skewed
• Understand the difference between the three principle Measures of Central
Tendency:
– Mean (Average), Mode (Most Likely)and Median (50% Confidence)
• Do you mean “Minimum” and “Maximum” or “Optimistic” and “Pessimistic”?
• Ask the question about the Mode (Most Likely): “In what circumstances…
– ... can the value be less than the Most Likely?”
– ... can the vale be more than the Most Likely?”
• Apply Correlation to tasks
– Very few tasks are totally independent of all others
– Consider a background correlation of between 20-30% (potentially even more for
Concept Development)
– Very few tasks are negatively correlated with others
12
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Monte Carlo Simulation: Effect of Correlation
13
The impact of negative correlation is
to push in and up
High values of one variable are associated with low
values of another variable and vice versa
Lik
eli
ho
od
of
Oc
cu
rre
nce
Range of Potential Outcomes
Take any two variables in a Monte Carlo Simulation:
Lik
eli
ho
od
of
Oc
cu
rre
nce
Range of Potential Outcomes
The impact of positive correlation is to
push down and out
High values of one variable are associated with high
values of another variable and vice versa
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Monte Carlo Simulation: The Downside
14
May or May Not Occur Will Occur
Risk &
Opportunity Register Defined
Baseline Tasks
Emergent
Baseline Task
Performance
Undefined Risks
or the
“Unknown Unknowns”
Clearly Defined
Undefined
or Unclear
Baseline
Estimate
Bottom-up
Uncertainty
Assessment
Bottom-up
Risk &
Opportunity
Assessment
Gap in Monte
Carlo Analysis
Likelihood of Task Occurrence
Task
Definition
“There are known knowns.
These are things we know
that we know.
There are known unknowns.
That is to say, there are
things that we now know we
don’t know.
But there are also unknown
unknowns. These are things
we do not know we don’t
know.”
Donald Rumsfeld
United States Secretary of Defense
“To know that we know what
we know, and that we do not
know what we do not know,
that is true knowledge.”
Confucius
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 15
Cost & Schedule Integration – Things that Dreams are made of
At the risk of being controversial, perhaps we should start by setting the cat
amongst the pigeons ….
15
Note: No real pigeons have been harmed in the making of this presentation
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 16
Cost & Schedule Integration – Stuff that Dreams are made of …
Schedule Analysis
Co
st
An
aly
sis
Let’s be honest …
if it was easy we’d all be doing it, but
we’re not
… conclusion: it isn’t easy
Do we even understand it?
We will all have heard the old saying
“Time is Money”, and there is some
truth in that
So we should be looking at
Integrated Cost and Schedule
Analysis … why aren’t we?
It needs investment:
Can we afford to?
Can we afford not to?
Integrated Cost &
Schedule Analysis
Schedule elements may require correlation
Cost elements may require correlation
Both may require cross-correlation
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
So, how can we fix it?
In line with good estimating practice, we advocate using more than one approach,
method and/or technique to evaluate the potential range of cost outcomes
17
• Top-down approach to pick up the unknowns and avoid duplication
Takes a view of the schedule risk and pro-rata the rate of spend
affected (Time costs money, resources are unlikely to be re-deployed)
Commercial/Financial uplift factors (escalation etc)
• Bottom-up approach based on authorised Risk Registers
Use of Monte Carlo Simulation - statistical technique to allow
multiple variables and probabilities to be modelled
• Balanced view of Top-down and Bottom-up approaches
By its very nature the Top-down approach (‘worst-case’ view) should
be greater than Bottom-up approach If not, or if there is a significant
difference either way, it could indicate that either one or other
approach is too immature, or overly optimistic or pessimistic
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Example of a Top-down Approach
18
What is it?
Task
Task
Task
Task
Task
Task
Task
Task
Task Task Task
Baseline Task Top-Down Variability
Cost
Schedule
Schedule Risk
Resources/Cost can be scaled in direct
proportional to the increase in programme
duration
• This implies a “standing army” effect if risks
materialise
Approach can be justified through a number of
general principles:
• Resources utilised by a proposal are scoped
on the baseline programme.
• Resources are generally internally re-deployed
within a contract
• Risks generally manifest themselves as
programme slippage
Uplift Factors
These cover issues not sufficiently covered by
the schedule risk assessment
• Examples include Escalation
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Using Monte Carlo to make a Price Recommendation
19
2.8
2.9 3
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
0%
20%
40%
60%
80%
100%
Min: 2.82119 Max: 3.86365Probability: 100%
2.8
2.9
3
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
Min: 2.82119 Max: 3.86365 Probability: 100%
Sensible Level of
Confidence for Bid
Our bottom-up approach to estimating
looks at modelling risk, opportunity and
uncertainty around the Most Likely
values
We always look to price based on
values which have a higher level of
Confidence …
Audience Quiz:
Why do we do that?
We don’t trust our own estimates?
We are so totally risk-averse?
We know that our customers will
knock us down?
The schedule may slip?
The sum of the Most Likely Values
is not the most likely value?
Monte Carlo Output is misleading?
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 20
Making A Recommendation - Interim
For internal clearance, generally prior to price formulation and submission, we
provide a high level summary of the cost build up:
… maybe we could do better…
Whatever happened to that Top-down Assessment we made?
Confidence Level £ Million Confidence
Optimistic Range 3.105 10%
Pessimistic Range 3.425 90%
Initial Recommendation 3.270 60%
Cost Build Up £ Million % of Total
Baseline Budget Request 3.050 93.3%
Technical Risk Contingency 0.138 4.2%
Management Contingency 0.082 2.5%
Total 3.270 100%
Risk Modelling
50% Confidence
level
Balancing Number
for Total
Bottom-up
Functional Budget
Request
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 21
Taking a Balanced View
Risk & Opportunity Register Based
S Optimistic Opportunities
and Uncertainties S Pessimistic Risks
and Uncertainties
Bottom-Up
Confidence Level
Baseline Uncertainty
Baseline Task
Lik
elih
ood o
f O
ccurr
ence
Potential Outcomes 0 Cost
Baseline Task Top-Down Variability
Cost
Bottom-Up Evaluation
Top-Down Evaluation
?
Schedule
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Slipping & Sliding - A Pragmatic Aid to “Judgement”
What is it?
• Slipping & Sliding is a simple technique we developed “on the fly” in support of
an Estimating Practitioner Training Course on Risk Opportunity & Uncertainty
• Trainees wanted more guidance on making that “Judgement Call”
• And thus, a training exercise was born that we called “Slipping & Sliding” for
reasons that will become abundantly clear
The rational is:
• We assume that we have made an honest and reasonable assessment of the
Top-down approach to cost variability based on a conservative view of the
schedule risk (higher Confidence Level)
– We know that the approach is inherently pessimistic
• We assume that we have made an honest and reasonable assessment of the
Bottom-up approach to Risk, Opportunity and Uncertainty using Monte Carlo
– We know that it does not include any explicit provision for the Rumsfeld Factor,
“Unknown Unknowns”
– The approach is inherently optimistic
• Reality lies somewhere between the optimistic and pessimistic view of life
22
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Slipping & Sliding - A Pragmatic Aid to “Judgement”
23
2500 2700 2900 3100 3300 3500 3700 3900 4100
100
90
80
70
60
50
40
30
20
10
0
Bottom-up
Uncertainty Bottom-up Risk, Opportunity & Uncertainty
(for statistical validity)
Top-down Assessment (80%SRA)
Functional Requests
Baseline Programme
0 200 400
Bottom-up
Risk &
Opportunity
First Pass: 70% Confidence on Uncertainty
50% Confidence on Risk & Opportunity
So, in this particular case:
Uncertainty @ 70% Confidence Level
+ Risk/Opportunity @ 50% Confidence Level
Risk Opportunity & Uncertainty @ 60%
Confidence level
… which all fits nicely inside the Top-down
assessment, which is inherently pessimistic
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Slipping & Sliding - A Pragmatic Aid to “Judgement”
24
2500 2700 2900 3100 3300 3500 3700 3900 4100
100
90
80
70
60
50
40
30
20
10
0
Bottom-up
Uncertainty
Top-down Assessment (80%SRA)
Functional Requests
Baseline Programme
0 200 400
Bottom-up
Risk &
Opportunity
Bottom-up Risk, Opportunity & Uncertainty
(for statistical validity)
So, in this particular case:
Our Bottom-up Risk Opportunity &
Uncertainty @ 60% Confidence level
… May only be equivalent to around the 40%
Confidence Level of our Top-down Approach
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Slipping & Sliding - A Pragmatic Aid to “Judgement”
25
2500 2700 2900 3100 3300 3500 3700 3900 4100
100
90
80
70
60
50
40
30
20
10
0
Bottom-up
Uncertainty
Top-down Assessment (80%SRA)
Functional Requests
Baseline Programme
0 200 400
Bottom-up
Risk &
Opportunity
Bottom-up Risk, Opportunity & Uncertainty
(for statistical validity)
… whereas our Top-down Approach @ 50%
Confidence level
… may be equivalent to around the 72%
Confidence Level on our Bottom-up
Approach
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Slipping & Sliding - A Pragmatic Aid to “Judgement”
26
2500 2700 2900 3100 3300 3500 3700 3900 4100
100
90
80
70
60
50
40
30
20
10
0
Bottom-up
Uncertainty
Top-down Assessment (80%SRA)
Functional Requests
Baseline Programme
0 200 400
Bottom-up
Risk &
Opportunity
Bottom-up Risk, Opportunity & Uncertainty
(for statistical validity)
Adjustment for inherent
Optimism Bias
in the bottom-up approach } Ratio of Top-down to Bottom-up Approaches:
Greater than but close to one
Conclusion?
The two approaches are reasonably
consistent with one another
Case 1
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Slipping & Sliding - A Pragmatic Aid to “Judgement”
27
2500 2700 2900 3100 3300 3500 3700 3900 4100
100
90
80
70
60
50
40
30
20
10
0
Bottom-up
Uncertainty
Top-down Assessment (80%SRA)
Functional Requests
Baseline Programme
0 200 400
Bottom-up
Risk &
Opportunity
Bottom-up Risk, Opportunity & Uncertainty
(for statistical validity)
Ratio of Top-down to Bottom-up
Approaches:
Significantly greater than one
Conclusion?
The two approaches are not consistent
with one another, either:
• the top-down is overly pessimistic
• or, the bottom-up is too immature
Case 2
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Slipping & Sliding - A Pragmatic Aid to “Judgement”
28
2500 2700 2900 3100 3300 3500 3700 3900 4100
100
90
80
70
60
50
40
30
20
10
0
Bottom-up
Uncertainty
Top-down Assessment (80%SRA)
Functional Requests
Baseline Programme
0 200 400
Bottom-up
Risk &
Opportunity
Bottom-up Risk, Opportunity & Uncertainty
(for statistical validity)
Ratio of Top-down to Bottom-up
Approaches:
Less than one
Case 3
Conclusion?
The two approaches are not consistent with
one another – an optimistic value cannot be
greater than a pessimistic value. Either:
• the top-down is overly optimistic
• or, the bottom-up is too immature
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document. 29
Making A Recommendation - Revisited
Following consideration of the inherent Optimism Bias in the Bottom-up Approach,
we can modify our high level summary of the cost build up:
… the inherent adjustment is held within Management Reserve
or a higher Technical Risk Contingency
Confidence Level £ Million Confidence
Optimistic Range 3.105 10%
Pessimistic Range 3.425 90%
Revised Recommendation 3.305 72%
Cost Build Up £ Million % of Total
Baseline Budget Request 3.050 92.3%
Technical Risk Contingency 0.138 4.2%
Management Contingency 0.117 3.5%
Total 3.305 100%
Risk Modelling
50% Confidence
level
Balancing Number
for Total
Bottom-up
Functional
Request
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Slipping & Sliding - A Pragmatic Aid to “Judgement”
Does it work in Practice?
• We do not claim it to be a perfect substitute for holistic Cost and Schedule Integration
• The technique is not a perfect solution – but none are
• We’re looking for reasonable accuracy not unreasonable precision
• It does not replace estimating judgement … it can guide the thought process in making a judgement
– To narrow the range or eliminate the extremes
– To reject or rework a particular approach
• It provides a degree of Quality Control in our approach to generating three-point estimates
• Proof of the Pudding as they say is in the eating …
• Let’s look at an example
30
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Example – Comparison Based on “Real” Data
Integrated Cost and Schedule
31
£0
£5,000
£10,000
£15,000
£20,000
£25,000
£30,000
- 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00
Co
st
Inclu
din
g R
isk (
£K
)
Duration (Months)
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Example – Comparison Based on “Real” Data
Integrated Cost and Schedule
£24,000
£25,000
£26,000
£27,000
£28,000
£29,000
£30,000
£31,000
60.00 65.00 70.00 75.00 80.00 85.00 90.00 95.00
Co
st
Inclu
din
g R
isk (
£K
)
Duration (Months)
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Example – Comparison Based on “Real” Data
Integrated Cost and Schedule
33
£24,000
£25,000
£26,000
£27,000
£28,000
£29,000
£30,000
£31,000
60.00 65.00 70.00 75.00 80.00 85.00 90.00 95.00
Co
st
Inclu
din
g R
isk (
£K
)
Duration (Months)
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Slipping & Sliding - A Pragmatic “Judgement” Aide
Confidence Level of the Final Agreement – a Reality Check
• Confidence levels and intervals are calculated as outputs from the Monte
Carlo modelling exercise.
• However, it should be remembered that the Confidence Level generated
through Monte Carlo Analysis is overstated
– Reality is that the true value at any Confidence Level above the Mode will be
greater than calculated, because …
– The value returned by Monte Carlo is only based on the analysis of the inputs
– It excludes any consideration of things that have not been considered –
“Unknown Unknowns”
– It assumes the baseline programme
– Failure to correlate tasks will narrow the output range too much
• The Top-down Method is inherently pessimistic in nature
– It assumes that if the one task slips, everything slips - there is no recovery
– However, it does create “headroom” for those “unknown unknowns”
• Reality is likely to be somewhere between the two views
34
Optimism Bias
Pessimism Bias
ACostE NW Region Sept 2013 10/09/2013 © Copyright BAE Systems. Any use, duplication or disclosure of information contained on
this page is subject to the restrictions on the title slide of this document.
Slipping & Sliding - A Pragmatic “Judgement” Aide
35
Thank you for listening
Any questions?