programmatic risk management workshop (handbook)
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Programmatic Risk Management:
A “not so simple” introduction to the
complex but critical process of building a
“credible” schedule
Program Planning and Controls Workshop, Denver, Colorado
October 6th and October 14th 2008
1/69
Programmatic Risk Management Work (Handbook)
Agenda
Duration Topic
20 Minutes Risk Management in Five Easy Pieces
15 Minutes Basic Statistics for programmatic risk management
15 Minutes Monte Carlo Simulation (MCS) theory
20 Minutes Mechanics of MSFT Project and Risk+
15 Minutes Programmatic Risk Ranking
15 Minutes Building a Credible schedule
20 Minutes Conclusion
120 Minutes
2/69
When we say “Risk Management”
What do we really mean?
3/69
Five Easy Pieces†:
The Essentials of
Managing
Programmatic Risk
Managing the risk to cost, schedule, and technical performance is the
basis of a successful project management method. † With apologies to Carole Eastman and Bob Rafelson for their 1970 film staring Jack Nicholson
Risk in Five Easy Pieces 4/69
Hope is Not a Strategy
A Strategy is the plan to successfully complete the project
If the project’s success factors, the processes that deliver them, the alternatives when they fail, and the measurement of this success are not defined in meaningful ways for both the customer and managers of the project – Hope is the only strategy left.
When General Custer was completely surrounded, his chief scout asked, “General what's our strategy?” Custer replied, “The first thing we need to do is make a note to ourselves – never get in this situation again.”
Hope is not a strategy!
Risk in Five Easy Pieces 5/69
No Single Point Estimate can be
correct without knowing the variance
Single Point Estimates use sample data to
calculate a single value (a statistic) that serves as a
"best guess" for an unknown (fixed or random)
population parameter
Bayesian Inference is a statistical inference where
evidence or observations are used to infer the
probability that a hypothesis may be true
Identifying underlying statistical behavior of the cost
and schedule parameters of the project is the first
step in forecasting future behavior
Without this information and the model in which it is
used any statements about cost, schedule and
completion dates are a 50/50 guesses
When estimating
cost and duration
for planning
purposes using
Point Estimates
results in the
least likely result.
A result with a
50/50 chance of
being true.
Risk in Five Easy Pieces 6/69
Without Integrating $, Time, and TPM
you’re driving in the rearview mirror
Addressing customer satisfaction means incorporating
product requirements and planned quality into the
Performance Measurement Baseline to assure the true
performance of the project is made visible.
Technical Performance (TPM)
Risk in Five Easy Pieces 7/69
Without a model for risk management, you’re driving in the dark with the headlights turn off
Risk Management means using a proven risk management process, adapting this to the project environment, and using this process for everyday decision making.
The Risk
Management
process to the right
is used by the US
DOD and differs
from the PMI
approach in how
the processes
areas are arranged.
The key is to
understand the
relationships
between these
areas.
Risk in Five Easy Pieces 8/69
Risk Communication is …
An interactive process of exchange of information and opinion among individuals, groups, and institutions; often involving multiple messages about the nature of risk or expressing concerns, opinions, or reactions to risk messages or to legal or institutional arrangements for risk management.
Bad news is not wine. It does not improve with age — Colin Powell
Risk in Five Easy Pieces 9/69
Basic Statistics for Programmatic
Risk Management
Since all point estimates are wrong, statistical estimates will be needed
to construct a credible cost and schedule model
Basic Statistics 10/69
Uncertainty and Risk are not the
same thing – don’t confuse them
Uncertainty stems from
unknown probability
distributions
– Requirements change impacts
– Budget Perturbations
– Re–work, and re–test
phenomena
– Contractual arrangements
(contract type, prime/sub
relationships, etc)
– Potential for disaster (labor
troubles, shuttle loss, satellite
“falls over”, war, hurricanes,
etc.)
– Probability that if a discrete
event occurs it will invoke a
project delay
Risk stems from known
probability distributions
– Cost estimating methodology
risk resulting from improper
models of cost
– Cost factors such as inflation,
labor rates, labor rate
burdens, etc
– Configuration risk (variation in
the technical inputs)
– Schedule and technical risk
coupling
– Correlation between risk
distributions
Basic Statistics 11/69
There are 2 types of Uncertainty
encountered in cost and schedule
Static uncertainty is natural variation and foreseen risks
– Uncertainty about the value of a parameter
Dynamic uncertainty is unforeseen uncertainty and “chaos”
– Stochastic changes in the underlying environment
– System time delays, interactions between the network elements, positive and negative feedback loops
– Internal dependencies
Basic Statistics
12/69
The Multiple Sources of Schedule Uncertainty
and Sorting Them Out is the Role of Planning
Unknown interactions drive
uncertainty
Dynamic uncertainty can be
addressed by flexibility in the
schedule – On ramps
– Off ramps
– Alternative paths
– Schedule “crashing” opportunities
Modeling of this dynamic
uncertainty requires simulation
rather than static PERT based path
assessment – Changes in critical path are
dependent on time and state of the
network
– The result is a stochastic network
Basic Statistics 13/69
Statistics at a Glance
Probability distribution – A function that describes the probabilities of possible outcomes in a "sample space.”
Random variable – variable a function of the result of a statistical experiment in which each outcome has a definite probability of occurrence.
Determinism – a theory that phenomena are causally determined by preceding events or natural laws.
Standard deviation (sigma value) – An index that characterizes the dispersion among the values in a population.
Bias –The expected deviation of the expected value of a statistical estimate from the quantity it estimates.
Correlation – A measure of the joint impact of two variables upon each other that reflects the simultaneous variation of quantities.
Percentile – A value on a scale of 100 indicating the percent of a distribution that is equal to or below it.
Monte Carlo sampling – A modeling technique that employs random sampling to simulate a population being studied.
Basic Statistics 14/69
Statistics Versus Probability
In building a risk tolerant
schedule, we’re interested in the
probability of a successful
outcome – “What is the probability of making a
desired completion date?”
But the underlying statistics of the
tasks influence this probability
The statistics of the tasks, their
arrangement in a network of tasks
and correlation define how this
probability based estimated
developed.
Basic Statistics 15/69
Each path and each task along that path has a
probability distribution
Any path could be critical depending on the convolution of the
underlying task completion time probability distribution functions
The independence or
dependency of each task
with others in the network,
greatly influences the
outcome of the total project
duration
Understanding this
dependence is critical to
assessing the credibility of
the plan as well as the total
completion time of that plan
Basic Statistics 16/69
Probability Distribution Functions are the Life
Blood of good planning
Probability of
occurrence as a
function of the
number of
samples
“The number of
times a task
duration appears
in a Monte Carlo
simulation”
Basic Statistics 17/69
Statistics of a Triangle Distribution
Triangle
distributions are
useful when
there is limited
information
about the
characteristics of
the random
variables are all
that is available.
This is common
in project cost
and schedule
estimates. Mode = 2000 hrs
Median = 3415 hrs
Mean = 3879 hrs
Minimum
1000 hrs
Maximum
6830 hrs
50% of all possible values are under
this area of the curve. This is the
definition of the median
Basic Statistics 18/69
Basics of Monte Carlo Simulation
Far better an approximate answer to the right question, which is often
vague, than an exact answer to the wrong question, which can always
be made precise. — John W. Tukey, 1962 Basics of Monte Carlo
19/69
Monte Carlo Simulation
Yes Monte Carlo is named after the
country full of casinos located on
the French Rivera
Advantages of Monte Carlo over
PERT is that Monte Carlo…
– Examines all paths, not just the critical
path
– Provides an accurate (true) estimate of
completion
• Overall duration distribution
• Confidence interval (accuracy range)
– Sensitivity analysis of interacting tasks
– Varied activity distribution types – not restricted to Beta
– Schedule logic can include branching – both probabilistic and conditional
– When resource loaded schedules are used – provides integrated cost and schedule
probabilistic model
Basics of Monte Carlo 20/69
First let’s be convinced that PERT
has limited usefulness
The original paper (Malcolm 1959) states
– The method is “the best that could be done in a real
situation within tight time constraints.”
– The time constraint was One Month
The PERT time made the assumption that the
standard deviation was about 1/6 of the range
(b–a), resulting in the PERT formula.
It has been shown that the PERT mean and
standard deviation formulas are poor
approximations for most Beta distributions
(Keefer 1983 and Keefer 1993).
– Errors up to 40% are possible for the PERT mean
– Errors up to 550% are possible for the PERT
standard deviation
Basics of Monte Carlo 21/69
Critical Path and Mostly Likelies
Critical Path’s are Deterministic – At least one path exists through
the network
– The critical path is identified by
adding the “single point” estimates
– The critical predicts the completion
date only if everything goes
according to plan (we all know this
of course)
Schedule execution is Probabilistic
– There is a likelihood that some durations will comprise a path that is off the critical path
– The single number for the estimate – the “single point estimate” is in fact a most likely estimate
– The completion date is not the most likely date, but is a confidence interval in the probability distribution function resulting from the convolution of all the distributions along all the paths to the completion of the project
Basics of Monte Carlo 22/69
Deterministic PERT Uses Three Point
Estimates In A Static Manner
Durations are defined as three point estimates
– These estimates are very subjective if captured individually by asking…
– “What is the Minimum, Maximum, and Most Likely”
Critical path is defined from these
estimates is the algebraic addition of
three point estimates
Project duration is based on the
algebraic addition of the times along
the critical path
This approach has some serious
problems from the outset
– Durations must be independent
– Most likely is not the same as the
average
Basics of Monte Carlo 23/69
Foundation of Monte Carlo Theory
George Louis Leclerc, Comte de Buffon,
asked what was the probability that the
needle would fall across one of the lines,
marked in green.
That outcome occurs only if: sinA l
Basics of Monte Carlo 24/69
Mechanics of Risk+ integrated with
Microsoft Project
Any credible schedule is a credible model of its dynamic behavior. This
starts with a Monte Carlo model of the schedule’s network of tasks
Mechanics of Risk+ 25/69
The Simplest Risk+ elements
Task to “watch”
(Number3)
Most Likely
(Duration3)
Pessimistic
(Duration2)
Optimistic
(Duration1)
Distribution
(Number1)
Mechanics of Risk+ 26/69
The output of Risk+
The height of each box indicates how often the project complete in a given interval during the run
The S–Curve shows the cumulative probability of completing on or before a given date.
The standard deviation of the completion date and the 95% confidence interval of the expected completion date are in the same units as the “most likely remaining duration” field in the schedule
Date: 9/26/2005 2:14:02 PMSamples: 500Unique ID: 10Name: Task 10
Completion Std Deviation: 4.83 days95% Confidence Interval: 0.42 daysEach bar represents 2 days
Completion Date
Fre
qu
en
cy
Cu
mu
lative
Pro
ba
bili
ty
3/1/062/10/06 3/17/06
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16 Completion Probability Table
Prob ProbDate Date0.05 2/17/060.10 2/21/060.15 2/22/060.20 2/22/060.25 2/23/060.30 2/24/060.35 2/27/060.40 2/27/060.45 2/28/060.50 3/1/06
0.55 3/1/060.60 3/2/060.65 3/3/060.70 3/3/060.75 3/6/060.80 3/7/060.85 3/8/060.90 3/9/060.95 3/13/061.00 3/17/06
Task to “watch”
80% confidence
that task will
complete by
3/7/06
Mechanics of Risk+ 27/69
A Well Formed Risk+ Schedule
For Risk+ to provide useful information, the underlying schedule must
be well formed on some simple way.
Mechanics of Risk+ 28/69
A Well formed Risk+ Schedule
A good critical path network
– No constraint dates
– Lowest level tasks have predecessors and
successors
– 80% of relationships are finish to start
Identify risk tasks
– These are “reporting tasks”
– Identify the preview task to watch during
simulation runs
Defining the probability distribution profile for each task
– Bulk assignment is an easy way to start
– A – F ranking is another approach
– Individual risk profile assignments is best but tedious
Mechanics of Risk+ 29/69
Analyzing the Risk+ Simulation
Risk+ generates one or more of the following outputs:
– Earliest, expected, and latest completion date for each reporting task
– Graphical and tabular displays of the completion date distribution for each reporting task
– The standard deviation and confidence interval for the completion date distribution for each reporting task
– The criticality index (percentage of time on the critical path) for each task
– The duration mean and standard deviation for each task
– Minimum, expected, and maximum cost for the total project
– Graphical and tabular displays of cost distribution for the total project
– The standard deviation and confidence interval for cost at the total project level
Mechanics of Risk+ 30/69
Programmatic Risk Ranking
The variance in task duration must be defined in some systematic way.
Capturing three point values is the least desirable.
Programmatic Risk Ranking 31/69
Thinking about risk ranking
These classifications can be used to avoid
asking the “3 point” question for each task
This information will be maintained in the IMS
When updates are made the percentage
change can be applied across all tasks
Classification Uncertainty Overrun
A Routine, been done before Low 0% to 2%
B Routine, but possible difficulties Medium to Low 2% to 5%
C Development, with little technical difficulty Medium 5% to 10%
D Development, but some technical difficulty Medium High 10% to 15%
E Significant effort, technical challenge High 15% to 25%
F No experience in this area Very High 25% to 50%
Programmatic Risk Ranking 32/69
Steps in characterizing uncertainty
Use an “envelope” method to characterize the
minimum, maximum and “most likely”
Fit this data to a statistical distribution
Use conservative assumptions
Apply greater uncertainty to less mature
technologies
Confirm analysis matches intuition
Remember Sir Francis Bacon’s quote
about beginning with uncertainty and
ending with certainty.
If we start with a what we think is a
valid number we will tend to continue
with that valid number.
When in fact we should speak only in
terms of confidence intervals and
probabilities of success.
Programmatic Risk Ranking 33/69
Sobering observations about 3 point
estimates when asking engineers
In 1979, Tversky and Kahneman proposed an alternative to Utility theory. Prospect theory asserts that people make predictably irrational decisions.
The way that a choice of decisions is presented can sway a person to choose the less rational decision from a set of options.
Once a problem is clearly and reasonably presented, rarely does a person think outside the bounds of the frame.
Source:
– “The Causes of Risk Taking By Project Managers,” Proceedings of the Project Management Institute Annual Seminars & Symposium November 1–10, 2001 • Nashville, Tenn
– Tversky, Amos, and Daniel Kahneman. 1981. The Framing of Decisions and the Psychology of Choice. Science 211 (January 30): 453–458
Programmatic Risk Ranking 34/69
Building a Credible Schedule
A credible schedule contains a well formed network, explicit risk
mitigations, proper margin for these risks, and a clear and concise
critical path(s). All of this is prologue to analyzing the schedule. Building a Credible Schedule
35/69
Good schedules have a contingency plans
The schedule contingency
needed to make the plan credible
can be derived from the Risk+
analysis
The schedule contingency is the
amount of time added (or
subtracted) from the baseline
schedule necessary to achieve
the desired probability of an under
run or over run.
The schedule contingency can be determined through
– Monte Carlo simulations (Risk+)
– Best judgment from previous experience
– Percentage factors based on historical experience
– Correlation analysis for dependency impacts
Is This Our
Contingency
Plan ?
Building a Credible Schedule 36/69
Schedule quality and accuracy
Accuracy range
– Similar for each estimate class
Consistent with estimate
– Level of project definition
– Purpose
– Preparation effort
Monte Carlo simulation
– Analysis of results shows quality attained versus the quality sought
(expected accuracy ranges)
Achieving specified accuracy requirements
– Select value at end points of confidence interval
– Calculate percentages from base schedule completion date, including
the contingency
Building a Credible Schedule 37/69
Technical Performance Measures
Technical Performance Measures are one method of showing risk by
done
– Specific actions taken in the IMS to move the compliance forward toward the
goal
Activities that
assessing the
increasing compliance
to the technical
performance measure
can be show in the
IMS
– These can be
Accomplishment
Criteria
Building a Credible Schedule 38/69
The Monte Carlo Process starts with
the 3 point estimates
Estimates of the task duration are still needed,
just like they are in PERT
– Three point estimates could be used
– But risk ranking and algorithmic generation of the
“spreads” is a better approach
Duration estimates must be parametric rather
than numeric values
– A geometric scale of parametric risk is one approach
Branching probabilities need to be defined
– Conditional paths through the schedule can be
evaluated using Monte Carlo tools
– This also demonstrate explicit risk mitigation
planning to answer the question “what if this
happens?”
These three
point estimates
are not the PERT
ones.
They are derived
from the ordinal
risk ranking
process.
This allows them
to be “calibrated”
for the domain,
correlated with
the technical risk
model.
Building a Credible Schedule 39/69
Expert Judgment is required to build
a Risk Management approach
Expert judgment is typically the basis of cost and
schedule estimates
– Expert judgment is usually the weakest area of process
and quantification
– Translating from English (SOW) to mathematics
(probabilistic risk model) is usually inconsistent at best and
erroneous at worst
One approach
– Plan for the “best case” and preclude a self–fulfilling
prophesy
– Budget for the “most likely” and recognize risks and
uncertainties
– Protect for the “worst case” and acknowledge the
conceivable in the risk mitigation plan
The credibility of the “best case” estimates if crucial to
the success of this approach
Building the
variance values
for the ordinal
risk rank is a
technical
process,
requiring
engineering
judgment.
Building a Credible Schedule 40/69
Guiding the Risk Factor Process requires
careful weighting of each level of risk
For tasks marked “Low” a reasonable
approach is to score the maximum 10%
greater than the minimum.
The “Most Likely” is then scored as a
geometric progression for the remaining
categories with a common ratio of 1.5
Tasks marked “Very High” are bound at
200% of minimum.
– No viable project manager would like a task
grow to three times the planned duration
without intervention
The geometric progress is somewhat
arbitrary but it should be used instead of
a linear progression
Min Most
Likely
Max
Low 1.0 1.04 1.10
Low+ 1.0 1.06 1.15
Moderate 1.0 1.09 1.24
Moderate+ 1.0 1.14 1.36
High 1.0 1.20 1.55
High+ 1.0 1.30 1.85
Very High 1.0 1.46 2.30
Very High+ 1.0 1.68 3.00
Building a Credible Schedule 41/69
Assume now we have a well formed
schedule – now what?
With all the “bone head” elements
removed, we can say we have a
well formed schedule
But the real role of Planning is to
forecast the future, provide
alternative Plan’s for this forecast
and actively engage all the
participants in the projects in the
Planning Process
For the role of
PP&C is to
move “reporting
past
performance” to
“forecasting
future
performance” it
must break the
mold of using
static models of
cost and
schedule
Building a Credible Schedule 42/69
We’re really after the management of schedule
margin as part of planning
Plan the risk alternatives that
“might” be needed
– Each mitigation has a Plan B
branch
– Keep alternatives as simple as
possible (maybe one task)
Assess probability of the alternative
occurring
Assign duration and resource
estimates to both branches
Turn off for alternative for a
“success” path assessment
Turn off primary for a “failure” path
assessment
30% Probability
of failure
70% Probability
of success
Plan B
Plan A Current Margin Future Margin
80% Confidence for completion
with current margin
Duration of Plan B Plan A + Margin
Building a Credible Schedule 43/69
Successful margin management requires the
reuse of unused durations
Programmatic Margin is added between
Development, Production and Integration
& Test phases
Risk Margin is added to the IMS where
risk alternatives are identified
Margin that is not used in the IMS for risk
mitigation will be moved to the next
sequence of risk alternatives
– This enables us to buy back schedule margin
for activities further downstream
– This enables us to control the ripple effect of
schedule shifts on Margin activities
5 Days Margin
5 Days Margin
Plan B
Plan A
Plan B
Plan AFirst Identified Risk Alternative in IMS
Second Identified Risk
Alternative in IMS
3 Days Margin Used
Downstream
Activities shifted to
left 2 daysDuration of Plan B < Plan A + Margin
2 days will be added
to this margin task
to bring schedule
back on track
Building a Credible Schedule 44/69
Simulation Considerations
Schedule logic and constraints – Simplify logic – model only paths which, by
inspection, may have a significant bearing on the final result
– Correlate similar activities
– No open ends
– Use only finish–to–start relationships with no lags
– Model relationships other than finish–to–start as activities with base durations equal to the lag value
– Eliminate all date constraints
– Consider using branching for known alternatives
Building a Credible Schedule 45/69
The contents of the schedule
Constraints
Lead/Lag
Task relationships
Durations
Network topology
Building a Credible Schedule 46/69
Simulation Considerations
Selection of Probability Distributions
– Develop schedule simulation inputs
concurrently with the cost estimate
• Early in process – use same subject matter
experts
• Convert confidence intervals into probability
duration distributions
– Number of distributions vary depending on
software
– Difficult to develop inputs required for
distributions
– Beta and Lognormal better than triangular;
avoid exclusive use of Normal distribution
Building a Credible Schedule 47/69
Sensitivity Analysis describes which
tasks drive the completion times
Concentrates on inputs most likely
to improve quality (accuracy)
Identifies most promising
opportunities where additional
work will help to narrow input
ranges
Methods
– Run multiple simulations
– Use criticality index
– “Tornado” or Pareto graph
Building a Credible Schedule 48/69
What we get in the end is a Credible
Model of the schedule
Concept generator from Ramon
Lull’s Ars Magna (C. 1300)
All models are wrong. Some
models are useful.
– George Box (1919 – )
Building a Credible Schedule 49/69
Conclusion
At this point there is too much information. Processing this information
will take time, patience, and most of all practice with the tools and the
results they produce. Conclusion 50/69
Conclusions
Project schedule status must be
assessed in terms of a critical path
through the schedule network
Because the actual durations of
each task in the network are
uncertain (they are random
variables following a probability
distribution function), the project
schedule duration must be
modeled statistically
Conclusion 51/69
Conclusions
Quality (accuracy) is measured at the end points of achieved confidence interval (suggest 80% level)
Simulation results depend on:
– Accuracy and care taken with base schedule logic
– Use of subject matter experts to establish inputs
– Selection of appropriate distribution types
– Through analysis of multiple critical paths
– Understanding which activities and paths have the greatest potential impact
Conclusion 52/69
Conclusions
Cost and schedule estimates are made up of many independent elements. – When each element is planned as best case – e.g. a
probability of achievement of 10%
– The probability of achieving best case for a two–element estimate is 1%
– For three elements, 0.01%
– For many elements, infinitesimal
– In effect, it is zero.
In the beginning no attempt should be made to distinguish between risk and uncertainty – Risk involves uncertainty but it is indeed more
– For initial purposes it is unimportant
– The effect is combined into one statistical factor called “risk,” which can be described by a single probability distribution function
Conclusion 53/69
What are we really after in the end?
As the program
proceeds so
does:
– Increasing
accuracy
– Reduced
schedule risk
– Increasing
visual
confirmation
that success
can be reached
Current Estimate Accuracy
Conclusion 54/69
Points to remember
Good project management is good risk
management
Risk management is how adults manage
projects
The only thing we manage is project risk
Risks impact objectives
Risks come from the decisions we make while
trying to achieve the objectives
Risks require a factual condition and have
potential negative consequences that must be
mitigated in the schedule
Conclusion 55/69
Usage is needed before
understanding is acquired
Here and elsewhere, we shall not
obtain the best insights into things
until we actually see them growing
from the beginning.
— Aristotle
Conclusion 56/69
The End
This is actually the beginning, since building a risk tolerant, credible,
robust schedule requires constant “execution” of the plan.
A planning algorithm from
Aristotle’s De Motu Animalium
c. 400 BC
Conclusion 57/69
Resources
1. “The Parameters of the Classical PERT: An Assessment of its Success,” Rafael Herrerias Pleguezuelo, http://www.cyta.com.ar/biblioteca/bddoc/bdlibros/pert_van/PARAMETROS.PDF
2. “Advanced Quantitative Schedule Risk Analysis,” David T. Hulett, Hulett & Associates, http://www.projectrisk.com/index.html
3. “Schedule Risk Analysis Simplified,” David T. Hulett, Hulett & Associates, http://www.projectrisk.com/index.html
4. “Project Risk Management: A Combined Analytical Hierarchy Process and Decision Tree Approach,” Prasanta Kumar Dey, Cost Engineering, Vol. 44, No. 3, March 2002.
5. “Adding Probability to Your ‘Swiss Army Knife’,” John C. Goodpasture, Proceedings of the 30th Annual Project Management Institute 1999 Seminars and Symposium, October, 1999.
6. “Modeling Uncertainty in Project Scheduling,” Patrick Leach, Proceedings of the 2005 Crystal Ball User Conference
7. “Near Critical Paths Create Violations in the PERT Assumptions of Normality,” Frank Pokladnik and Robert Hill, University of Houston, Clear Lake, http://www.sbaer.uca.edu/research/dsi/2003/procs/237–4203.pdf
Resources 58/69
Resources
8. “Teaching SuPERT,” Kenneth R. MacLeod and Paul F. Petersen, Proceedings of the Decision Sciences 2003 Annual Meeting, Washington DC, http://www.sbaer.uca.edu/research/dsi/2003/by_track_paper.html
9. “The Beginning of the Monte Carlo Method,” N. Metropolis, Los Alamos Science, Special Issue, 1987. http://www.fas.org/sgp/othergov/doe/lanl/pubs/00326866.pdf
10. “Defining a Beta Distribution Function for Construction Simulation,” Javier Fente, Kraig Knutson, Cliff Schexnayder, Proceedings of the 1999 Winter Simulation Conference.
11. “The Basics of Monte Carlo Simulation: A Tutorial,” S. Kandaswamy, Proceedings of the Project Management Institute Annual Seminars & Symposium, November, 2001.
12. “The Mother of All Guesses: A User Friendly Guide to Statistical Estimation,” Francois Melese and David Rose, Armed Forces Comptroller, 1998, http://www.nps.navy.mil/drmi/graphics/StatGuide–web.pdf
13. “Inverse Statistical Estimation via Order Statistics: A Resolution of the Ill–Posed Inverse problem of PERT Scheduling,” William F. Pickard, Inverse Problems 20, pp. 1565–1581, 2004
Resources 59/69
Resources
14. “Schedule Risk Analysis: Why It Is Important and How to Do It,
“Stephen A. Book, Proceedings of the Ground Systems Architecture
Workshop (GSAW 2002), Aerospace Corporation, March 2002,
http://sunset.usc.edu/GSAW/gsaw2002/s11a/book.pdf
15. “Evaluation of the Risk Analysis and Cost Management (RACM)
Model,” Matthew S. Goldberg, Institute for Defense Analysis, 1998.
http://www.thedacs.com/topics/earnedvalue/racm.pdf
16. “PERT Completion Times Revisited,” Fred E. Williams, School of
Management, University of Michigan–Flint, July 2005,
http://som.umflint.edu/yener/PERT%20Completion%20Revisited.htm
17. “Overcoming Project Risk: Lessons from the PERIL Database,” Tom
Hendrick , Program Manager, Hewlett Packard, 2003,
http://www.failureproofprojects.com/Risky.pdf
18. “The Heart of Risk Management: Teaching Project Teams to Combat
Risk,” Bruce Chadbourne, 30th Annual Project Management Institute
1999 Seminara and Symposium, October 1999,
http://www.risksig.com/Articles/pmi1999/rkalt01.pdf
Resources 60/69
Resources
20. Project Risk Management Resource List, NASA Headquarters Library,
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22. “Continuous Risk Management,” Cost Analysis Symposium, April
2005,
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25. “Improved Three Point Approximation To Distribution Functions For
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E. McNeill, John W. Fowler and Gerald T. Mackulak, Department of
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26. “Analysis Of Resource–constrained Stochastic Project Networks
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Mississippi State University, May 2005,
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27. “Integrated Cost / Schedule Risk Analysis,” David T. Hulett and Bill
Campbell, Fifth European Project Management Conference, June
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28. “Risk Interrelation Management – Controlling the Snowball Effect,” Olli
Kuismanen, Tuomo Saari and Jussi Vähäkylä, Fifth European Project
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29. The Lady Tasting Tea: How Statistics Revolutionized Science in the
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30. “Triangular Approximations for Continuous Random Variables in Risk
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32. “Statistical Dependence in the risk analysis for Project Networks Using
Monte Carlo Methods,” J. Rene van Dorp and M. R. Dufy,
International Journal of Production Economics, 58, pp. 17–29, 1999.
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33. “Risk Analysis for Large Engineering Projects: Modeling Cost
Uncertainty for Ship Production Activities,” M. R. Dufy and J. Rene
van Dorp, Journal of Engineering Valuation and Cost Analysis,
Volume 2. pp. 285–301,
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34. “Risk Based Decision Support techniques for Programs and Projects,”
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35. Probabilistic Risk Assessment Procedures Guide for NASA Managers
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38. “Probabilistic PERT,” Arthur Nadas, IBM Journal of Research and
Development, 23(3), May 1979, pp. 339–347.
39. “Ranked Nodes: A Simple and effective way to model qualitative in
large–scale Bayesian Networks,” Norman Fenton and Martin Neil,
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of Computer Science, Queen Mary, University of London, February
21, 2005.
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40. “Quantify Risk to Manage Cost and Schedule,” Fred Raymond,
Acquisition Review Quarterly, Spring 1999, pp. 147–154
41. “The Causes of Risk Taking by Project Managers,” Michael Wakshull,
Proceedings of the Project Management Institute Annual Seminars &
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42. “Stochastic Project Duration Analysis Using PERT–Beta Distributions,”
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44. “The Cause of Risk Taking by Managers,” Michael N.Wakshull,
Proceedings of the Project Management Institute Annual Seminars &
Symposium November 1–10, 2001, Nashville Tennessee ,
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45. “The Framing of Decisions and the Psychology of Choice,” Tversky,
Amos, and Daniel Kahneman. 1981, Science 211 (January 30): 453–
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46. “Three Point Approximations for Continuous Random Variables,”
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47. “Better Estimation of PERT Activity Time Parameters,” Donald Keefer
and William Verdini, Management Science, 39(9), pp. 1086 – 1091.
48. “The Benefits of Integrated, Quantitative Risk Management,” Barney
B. Roberts, Futron Corporation, 12th Annual International Symposium
of the International Council on Systems Engineering, July 1–5, 2001,
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49. “Sources of Schedule Risk in Complex Systems Development,” Tyson
R. Browning, INCOSE Systems Engineering Journal, Volume 2, Issue
3, pp. 129 – 142, 14 September 1999,
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50. “Sources of Performance Risk in Complex System Development,”
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51. “Experiences in Improving Risk Management Processes Using the
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Dieter Landes, ACM SIGSOFT Software Engineering Notes ,
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Foundations of software engineering SIGSOFT '98/FSE-6, Volume 23
Issue 6, November 1998.
52. “Anchoring and Adjustment in Software Estimation,” Jorge Aranda and
Steve Easterbrook, Proceedings of the 10th European software
engineering conference held jointly with 13th ACM SIGSOFT
international symposium on Foundations of software engineering
ESEC/FSE-13
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