Risk adjusted engineering management

The Risk Adjusted Product Roadmap, starts with a Risk Adjusted
Engineering Estimates and the resulting Rough Order of Magnitude
Estimate
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2020

 Uncertainty is always
present.
 Risk is the
unavoidable outcome
of uncertainty.
 Risks are applicable
to all elements, of all
projects, at all stages
of the project.
 Agile projects are not
exempt from risks.
Managing in the presence of uncertainty requires making estimates of
the probabilities of the risk and the probable outcomes of the choices
made by the decision makers on how to handle each risk.
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2020 2

Uncertainty Creates Risk
3
Uncertainty
Irreducible
(Aleatory)
Reducible
(Epistemic)
Natural Variability
Ambiguity
Ontological
Uncertainty
Probabilistic Events
Probabilistic
Impacts
Periods of Exposure

Integrated Software Development
Risk Management
4
Technical Risk Management
Tracking and Controlling
Performance Deviations
Deliberating and
recommending a decision
alternative
Risk analysis of decision
alternatives, performing
trade studies and ranking
Proposing and/or identifying
decision alternatives
Formulation of objectives
Hierarchy and Technical
Performance Measures
Stakeholder
expectations,
requirements
definition and
management
Design solutions,
technical planning
Design solution,
technical planning,
and decision
analysis
Technical planning
and decision
analysis
Decision analysis,
lessons learned,
knowledge
management
Identify
Analyze
Plan
Track
Control
Decide and
implement
decision
alternatives
Communicate

Epistemic Uncertainty and Aleatory
Variability are both risk drivers†
5
Epistemic Uncertainty
 Epistemic uncertainty is the
scientific uncertainty due to limited
data and knowledge in the model
of the process
 Epistemic uncertainty can, in
principle, be eliminated with
sufficient study
 Epistemic (or internal) uncertainty
reflects the possibility of errors in
our general knowledge.
Aleatory Variability
 Aleatory uncertainties arise from
the inherent randomness of a
variable and are characterized by a
Probability Density Function
 The knowledge of experts cannot
be expected to reduce aleatory
uncertainty although their
knowledge may be useful in
quantifying the uncertainty
Randomness With Knowable Probabilities Randomness With Unknowable Probabilities
The probability of occurrence can be defined
through a variety of methods. The outcome is
a probability of occurrence of the event
A Probability Density Function (PDF) generates
a collection of random variables used to
model durations and costs
† Uncertainty in Probabilistic Risk Assessment: A Review, A.R. Daneshkhan

Top Level ROM Development
Process
6

Modeling the Risk Adjusted ROM
 Capture Most Likely effort estimates for Aleatory
uncertainties in the Engineering Estimate
template
• Capture upper and lower bounds of this Most Likely
value from past performance, parametric estimates, or
similar data sources
• Subject Matter Expertise provide past experience
 Place this information in Crystal Ball
 Define upper and lower bounds of variances
 Produce confidence data for Estimate
 Adjust scope to meet customer expectation in
the presence of uncertainty
7

Modeling Epistemic Uncertainties
in the ROM before submission
8

A Project Start, Model Product
Roadmap and Release Plan
 Using the IMS with
• Work Packages containing Features
• Product Roadmap
• Release Plan
 Model aleatory and epistemic uncertainties and
their impact on
• Cost
• Schedule
• Technical performance
9

Aleatory Uncertainty in Work
Duration drives cost
10
Resource loaded IMS
with variable durations
and productivity of
deliverables driven by
naturally occurring
uncertainties.
These uncertainties
create probabilistic
outcomes for cost and
schedule

Modeling Aleatory Uncertainty and
resulting risk on project execution
 Naturally occurring variances create risk of being
late and over budget
• Modeling from past performance (Reference Class
Forecasting), parametric data.
• Engineering judgement the least desirable basis of modeling
‒ optimism and confirmation biases
 Modeling these uncertainties (aleatory or irreducible)
uncertainties done with Monet Carlo Simulation tool
• Example is with RiskyProjecttm
• Other tools available to IS&GS with similar outcomes
 Uncertainties change as project progresses
• Updated simulation needed as project progresses
11

Modeling Epistemic Uncertainty
during project execution
 Event based uncertainties drive cost, schedule,
and technical risk
 Capture event based risks in a risk register
 Define pre-mitigation probability of occurrence
and probabilistic impacts
 Define mitigations and cost of mitigation
 Define residual cost after mitigation
 Produce pre‒ and post‒ mitigation assessment
of Risk Management plan
 Update Risk Management Plan at the end of
every release
12

Model Aleatory and Epistemic
Risk in the IMS
 Risk Register contains naturally variable
uncertainties and event based uncertainties for
the work in a single location
13

The Final Notion of Risk
The notion that the causes for risks clearly lie in our incomplete
knowledge of the subject matter, and if a project establishes all
possible causes of risks they can be managed away.
This puts the focus on discovering & dealing with Epistemic Risks.
Aleatory Risks can be easily modeled with Reference Class
Forecasting using past performance.
The reduction of Epistemic Risk is the primary beneficial outcome of
Agile Software Development.
The Aleatory Risk cannot be reduced. Only margin will protect the
delivery data and cost
And of course that is simply not possible!
Rapid Feedback provides visibility to emerging risks
14

But, Beware the Black Swan

Risk adjusted engineering management

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