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Estimating
-100%
0%
100%
Needs Define Design Test Deliver Support
Estimation Δ
Time
±50% ±20%

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What is Estimating?
Roughly calculate or judge the value, number, quantity, or extent of
Oxford English Dictionary
The process of finding an estimate
– a value that is used because it is derived from the best information available
– even if input data is incomplete, uncertain, or unstable
Wikipedia
The process of combining the results of experience, metrics and measurements to arrive at
an approximate judgement of time, cost or effort

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Good Estimating is Critical to Success
Good estimates enable
 Sound business decision making – is the project worth doing?
 Sound project planning – when will the project deliver? – what will it cost?
 Design optimisation – is this the optimum design and delivery strategy?
 Performance monitoring
 Cash flow management
20% into a project lifecycle
80% of the total cost
is already committed
Decisions based on early
estimates are critical
0%
100%
Define Design Test Deliver
Cost
Time
Committed Costs
Actual Costs

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Estimating Best Practice
Association for Project Management
Planning, Scheduling, Monitoring & Control [2015] 
Project Management Institute
Practice Standard for Project Estimating [2010] 
UK Government
Ministry of Defence Acquisition System Guidance: Forecasting & Estimating
NASA
Cost Estimating Handbook, v4.0 [2008] 

5. 5 of 21
Estimating Methods
Transparent
Box
Analogical
Group
Technologies
Knowledge
Based
Case Based
Reasoning
Detailed
Product
Attributes
Function
Costing
Feature
Costing
Accumulation
of Parts
Generative
Activity
Based
Reasoning
visible Black
Box
Statistical
Parametric
Neural
Network
Expert
Judgement
Source: Reference 1
Reasoning
hidden
Method
Group
Reference 2

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Estimating Methods II
Transparent Box Advantages Limitations
Analogical
Group Technologies Compares the work to one or more similar but complete projects or parts thereof
Quick
Intuitive
Case bias
Large case history required
Poor for innovations
Knowledge Based Attempts to imitate the reasoning of experts to estimate
Visible logic
Structured
Knowledge obsolescence
Large database required
Case Based Reasoning An estimate based on similar situations from a library of previous cases
Quick
Collective memory
Large reliable case base
Poor for innovations

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Estimating Methods III
Transparent Box Advantages Limitations
Detailed: Product Attributes
Function Costing A product or system is estimated directly from a specification of its performance
Integrates requirements with costs Need to allocate cost to function
Accuracy
Feature Costing The integration of CAD/CAM with cost information for cost estimation early in the
design process via feature-based modelling
Integrates with CAD/CAM
Can be automated
No feature consensus
Large database required
Detailed: Accumulation of Parts
Generative or Analytical Cost Estimation
Estimating by aggregating the processes involved to create the product
Can be accurate
Detail useful for negotiation
Time consuming
Detailed data may not be available
Activity Based or ABC (Activity Based Costing)
As Generative, but the overheads are allocated where they are incurred
Allocates costs to sources
String indication of profitability
Time consuming
Detailed data may not be available
Allocation of overheads

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Estimating Methods IV
Black Box Advantages Limitations
Expert Judgement Domain expert uses previous similar experience
Delphi – several experts independent estimates rationalised to single result
Mini-Delphi – two experts independently estimate and then agree a single result
Quick
Flexible
Bias
Unstructured
Repeatability
Statistical
Parametric Uses Cost Estimating Relationships (CERs) and algorithms or logic
CERs are extracted from historical data for similar systems and define correlations
between cost drivers and other system parameters such as size or performance.
As defined in the Parametric Cost Estimating Handbook
Clear influences
Objective
Repeatable
Large database required to define CERs
Simplistic
Missing parameters
Neural Network Learns the impact of attributes on cost by automatic analysis of historical data
Accurate
Updateable
Hidden logic
Complex
Large database required

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Estimating Psychology
Motivational Bias an estimate that serves the estimator more than it is objective
e.g. making a plan fit the desired target
Optimism Bias considering only positive previous experiences
people always under-estimate, even if aware (wishful thinking)
Cognitive Bias using heuristics to estimate sometimes leads to poor estimates
Heuristics, or mental shortcuts, often work but can lead to cognitive bias
e.g. Anchor heuristic: estimates are biased towards an early erroneous estimate
Availability heuristic: poor estimates as estimators remember a limited data set
Biases of different estimators can reinforce each other
Rule of Pi the actual time to do the work will be the time estimated multiplied by π
Student Syndrome only starting work towards the end of the estimated period

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Expert Judgement: for both Top-Down & Bottom-up
High levels of innovation: no database of past estimates & results
Often little detail about the task being estimated & little time to do estimating
Mitigate expert judgement weaknesses
 Template to gather estimates – consistency between estimators & reduced bias
 Get buy-in by using experts from delivery team to estimate
 Use experts with domain knowledge
 Mini Delphi further reduces bias
 Give separate estimators the same information
 Estimate for normal conditions – work hours, seniority of staff
 Don’t allow for estimation uncertainty in individual estimates

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Managing Estimation Uncertainty
Concurrency
Plans often feature concurrent tasks
 with minimal float, and
 single point (deterministic) estimates
Higher concurrency means greater
impact when some tasks finish late
Range Estimates
Analysis shows deterministic outcomes
have very low probability
Range estimates are more realistic
Even better, 3 points:
minimum, most likely, maximum
Dates and costs then become ranges
Difference in cost between outcomes
can be used to define case-by-case
estimation uncertainty contingency
Typical plan analysis:
Probability of achieving the deterministic cost
Reference 3

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Potential Weakness of 3 Point Estimates
Outcomes may be pessimistic if most likely estimates are not 50% likely
Typical 3 point estimate
Probability of Most Likely estimate PML
PML = 2/(10-4) = 1/3 or 33% …NOT 50%!
Avoid this weakness by asking for the probability of the Most Likely estimate
Example: for PML = 70%
Create double triangle distribution
and calculate points A & B:
0.7 = 0.5*(6-4)*A so A = 0.7
0.3 = 0.5*(10-6)*B so B = 0.15
Area either side is not equal and will vary task by task as defined by the 4th point
In practice need only do this for 10 to 20 most critical tasks – found by sensitivity analysis

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Selective 4 Point Estimation: Simulation Results
Result for 3 point estimates alone
Result including selected 4 point estimates:
Higher probability of deterministic outcome
Smaller, more realistic risk budget
Tornado Graph:
Sensitivity analysis to
select tasks to estimate
with 4 points
Risk Budget:
£21,125
Risk Budget:
£13,696

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Estimation Uncertainty Contingency
One strategy:
Estimation uncertainty ‘project risk pot’ = 95% cost – deterministic cost
The business Risk Appetite can inform what probability to use, e.g.:
10% Team Target (likely risks do not occur)
50% Best Estimate (as many risks occur as not)
90% ‘Safe’ Estimate (several unlikely major risks occur)
Reward using less of this risk pot, but recognise that a proportion is likely to be required
This encourages behaviour that enhances results whilst recognising estimation uncertainty
and setting realistic expectations
PMs use project risk pot to drive delivery to the deterministic end date
Drives the right behaviour in the team – to deliver on their Most Likely estimates
Selective 4 point estimating maintains competitive pricing

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Other Methods
Price to Win estimate based on supposition or knowledge of the budget
Do tailor the scope of work to suit a budget
Don’t force an estimate to fit a budget
Parkinson 'Work expands to fill the time available'
equates the estimate to available resources
These are not estimating methods – they are price and cost management methods

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Estimating Process
Contingency
Zero Risk
(Deterministic)
Cost Held at project & programme level
Project
Risk Pot
Estimation
Uncertainty
Top-Down
Estimate
Rationalise
Estimates
PDP
Project
Delivery
Process
Product
Breakdown
Structure
Work
Breakdown
Structure
Work Packages
& Tasks
Bottom-up
3pt Estimates
I
N
F
O
R
M
Need
Strategy Opportunities
Enhancement
Tasks
Secondary
Risks
Inform / Offset
Threats
Mitigation
Tasks
Risk Register
Held at portfolio level
If cost effective
Contingency
For project risks other
than estimation
uncertainty

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Practical Advice & Tips
Choose estimating method case-by-case to suit project and environment
 No perfect estimating method, suitable for all environments
 Many methods rely on a database or case history of past estimates and results
 Often need a combination of methods
 Risk Management for project risks other than estimation uncertainty
Use the proposed delivery team to create the estimates in order to get buy-in
Base estimates on normal conditions: 5 day week, 8 hour day
Use consistent time units
Do a reality check on the result and rationalise bottom-up with top-down
Range estimates are more realistic than single point (deterministic estimate)
Don’t pad individual estimates as a means to handle estimation uncertainty
If project type & environment always same
– use one-size-fits-all estimation uncertainty contingency
otherwise use 3 point estimating to derive estimation uncertainty contingency

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Practical Advice & Tips II
Estimation uncertainty is a significant project risk, but not the only project risk
 Use Risk Management to handle the other types of project risk
Bottom-up estimating: avoid long tasks and tasks where progress hard to assess
Educated Guess: beware early estimates often based on little information
 Can become base against which future estimates are judged (anchor heuristic)
 Cautions delivered with that first estimate rarely remembered
Don’t confuse uncertainty with a lack of knowledge
 Large ranges generally indicate guessing
 Experience is required to estimate rather than guess
It’s still just an estimate
 Project starts late: provide estimates from time T0 rather than a fixed date
 Team members late to start: avoid ‘brick-wall’ starts where possible
 Incorrect assumptions: list assumptions, track changes & adjust plans

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Summary
Estimating is a critical competence
Be aware of impact of estimator psychology
Significant innovation in projects limits useful estimation methods
Build historical data records (estimate & result) where possible
Combine estimation methods for best results
3 point range estimates offer case-by-case estimation uncertainty contingency
4 point estimates for few most influential tasks eradicates potential 3 point weakness
One-size-fits-all estimation uncertainty contingency suitable if projects all similar
Risk Management to handle project risk other than estimation uncertainty

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1. Cost Estimation Method Selection:
Matching User Requirements and Knowledge Availability to Methods
DK Evans, Dr. JD Lanham, Dr. R Marsh, Paper for ICEC (International Cost Engineering Council), 2006
SEEDS (Systems Engineering Estimation for Decision Support),
AMRC (Aerospace Manufacturing Research Centre)
University of West of England, Bristol, UK
2. Delphi Estimation Method
Wikipedia
3. Tool used for planning and 3 & 4 point estimation uncertainty calculations:
Oracle Primavera Risk Analysis
or
Safran Risk
References

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Author Profile
In my board role I led a team of 22 Project Managers and 5 Quality Engineers, and ensured Roke’s £79m
project portfolio delivered better than budget profit. I set-up and ran a virtual PMO and created REP,
the Roke Engineering Process, also managing the engineering tools to support it.
After 4 years as an electronics engineer for Siemens, achieving Chartered Engineer,
I moved into project management for 14 years, at Siemens and Roke Manor Research.
Successfully delivering Roke’s most challenging whole lifecycle product developments
on time and under budget led to a role as Director and board member for 6 years.
In 2013 I returned to hands-on project management as Programme Director at
Cambridge Consultants, founder member of the Cambridge Science Park.
Creator of the APM corporate accredited PM Excellence Programme,
I chaired a quarterly PM forum to share best practice and built a
supportive PM community. I coached seven PMs to RPP, five to PQ,
and all passed APMP.
These investments in PM professionalism led to a turn-around and
annual improvement in project results across a 400 project portfolio
and delivered an above budget performance in five consecutive years
with profits totalling £7.9m above budget.
Passionate advocate of PM professionalism, Fellow of the APM and
the IET and author of articles published in Project and PM Today.
Professional Development
Winning Project Work
Planning
Estimating
Risk Management
Earned Value Management
Change Control
Stakeholder Management
3 Steps to Professional Project Management: Case Study
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