There are two strategies to mitigate the planning fallacy relative to project schedules, one relating to benchmarking activity durations, and the second advocates schedule risk analysis as the new normal. In support of this strategy, Dr. Gui introduces GPM schedule risk analysis and provides a demonstrative using NetRisk, the schedule risk analysis module now available with NetPoint.

1. Mitigating the
Planning Fallacy
(Risked Schedules─The New Normal)
Gui Ponce de Leon, PhD, PE, PMP, LEED AP
GPM Boot Camp
Newark, NJ
March 8th, 2013
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2. There are lies, there are damned lies,
and then there are deterministic schedules
Attributed to Dr. Vivek Puri, PMA’s resident simulation guru
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3. PRESENTATION OUTLINE
Mitigating the Planning Fallacy
CPM Risk Modeling & Analysis
GPM® Risk Modeling & Analysis
NetRiskTM Synopsis
Summary & Take-Aways
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4. Just What Is the Planning Fallacy?
Kahneman1 and his longtime
colleague, Tversky, coined the
term to describe plans that
Are unrealistically close to best-case
scenarios
Could be improved by consulting the
statistics of similar cases
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5. Planning Fallacy aka Optimism Bias
Bent Flyvbjerg2, the renowned Danish
planning expert, notes
The problem of optimism bias arises when
various factors combine to produce a
systematic underreporting of the level of
project uncertainty
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6. Planning Fallacy aka Optimism Bias (cont’d)
Bent Flyvbjerg futher notes
The failure to reflect the probabilistic nature of
project planning, implementation and operation
is a central cause of the poor track record for
megaproject performance
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7. Scheduling Strategies for
Mitigating the Planning Fallacy
1 Rely on an outside view as advocated
by Kahneman
The outside view does not try to forecast specific
uncertain events that may affect the activities
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8. Scheduling Strategies for
Mitigating the Planning Fallacy
2 Work with risked schedules as the
new normal
The schedule is dealt with as inherently stochastic
in nature rather than being analyzed merely for risk
(i.e., what-if exercise)
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9. Taking an Outside View
Relative to Schedules
The schedule is built using a database of historical activity
durations by project type/context
At a minimum, the database captures ‘normal’ durations
Ideally, distributional information (mean, mode, low/high) is included
Physical work durations factor production rates, e.g., steel tons/day,
concrete CY/day, large bore pipe LF/day, etc.
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10. Working with Is the
Risked Schedules New Normal
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11. THE CONCEPT OF
RISKED SCHEDULES
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12. Risked schedules are generated
in the following sequence:
A base‐case schedule portraying The baseline schedule selected
how the project would evolve with reserves schedule margin sufficient
activities at their normal durations to support the targeted
is the initial focus probability(ies) of completion
STEP 1 STEP 2 STEP 3
Following risk modeling, risk analysis trials
are conducted to investigate alternate
probabilistic and baseline scenarios

13. RISKED SCHEDULES
The baseline schedule is risk assessed
periodically and when revised to reflect scope of
remaining work and current risks
Going from deterministic to probabilistic
planning/scheduling and back is a seamless
exercise throughout the project life cycle
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14. 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
1958 Development and implementation of Schedule Risk
PERT by the US Navy Special Projects Office
in the 20th Century
1962 Robert McNamara endorses use of PERT/COST
(forerunner to earned value) throughout the US DOD3
1963 First application of Monte Carlo simulation
to network-based schedules by Van Slyke4
1966 Pritsker develops GERT for NASA as a
method to analyze stochastic activity networks5
1986-87 Risk management becomes a separate
knowledge area in the PMBOK in the 1986-87 update6
1990s Simulation morphs
into schedule risk analysis
1995 Primavera releases Monte Carlo version 3.0 offering
“enhanced risk analysis software for project management”
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15. 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
2000 Risk analysis is added to Pertmaster and Schedule Risk
Pertmaster supports full integration with Primavera
in the 2000s
2004 The Third Edition of the PMBOK adds the ‘risk register’
as a primary output of the Identify Risks Process
2008 Oracle acquires Primavera, and the
Pertmaster software is renamed OPRA
2011 Schedule risk analysis is intrinsic to
scheduling excellence in the Planning &
Scheduling Excellence Guide7
2012 Schedule risk analysis is codified as
one of the nine scheduling best practices in
the GAO Schedule Assessment Guide8
2012 AACE International releases RP 64 on
CPM Schedule Risk Modeling and Analysis9
2013 GPM® Risk is introduced at the
NetPoint® User Conference in New Orleans
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16. Emerging Consensus on
CPM Risk Analysis
A schedule risk analysis
(SRA) is conducted to
determine
the likelihood of completion dates
schedule contingency
needed for an acceptable
level of certainty
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17. Emerging Consensus on
CPM Risk Analysis (cont’d)
The baseline schedule
includes contingency
aka schedule margin
to account for the
occurrence of risks
Schedule margin supports
the targeted likelihood of
meeting completion dates
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18. Emerging Consensus on
CPM Risk Analysis (cont’d)
An SRA is performed
on the schedule
periodically as the
schedule is updated
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19. CPM Risk Modeling & Analysis
Minimal use of constraints on activities
PDM logic ties are used in only limited and
well-understood circumstances
Modeling accepts ‘existence risks’ and
‘branching risks’
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20. CPM Risk Modeling & Analysis
Risk drivers occur with the same probability
on impacted activities
In each realization, all activities are on early
dates, but for perhaps SNE dates
Cruciality is combined with criticality
Weather risks are modeled
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21. INTRODUCING GPM RISK
MODELING & ANAYSIS
1 GPM planned dates are favored over SNE constraint dates
2 Activities and activity nodes are encoded with stochastic rules
3 Any risk that, if occurring, impacts multiple activities, may occur
with a different probability and impact on each activity
4 Risks resulting from common, contemporaneous decisions to
start activities on dates later than early dates are modeled
5 Criticality and cruciality are combined to measure importance
6 Adverse weather and weather-event risks are modeled
7 GPM algorithms are extended for stochastic networks
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22. 1 Planned Dates ILO SNE
Constraints
In CPM modeling, the consensus is to limit
SNE dates to external dependencies10
Because GPM is not fixated on early dates
SNE constraints can be replaced in simulation with
GPM planned dates
Unlike a constraint date, a planned date may shift to an
earlier date in a realization if predecessors on logic chains
leading to the planned-date activity are sampled at the
right mix of lower durations
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23. 1 Planned Dates ILO SNE
Constraints (cont’d)
In CPM risk, to mitigate SNE constraints,
analysts may
Pursue trial simulations with alternate, earlier SNE
dates, or
Replace the SNE date with a variable-duration activity
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24. Schedule Demonstrative,
Base-Case Scenario
Constraint Date
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25. Simulation Trial, Planned Date
ILO SNE Date11
Not Risked
Planned Date
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26. Planned Dates vs. SNE Constraints Results
01/09/2012
1
0.9
10/03/2012
12/20/2011
0.8
10/08/2012
0.7
0.6
Num. of Iterations
SNE Constraint Planned Date
0.5
If the SNE date could be 01/09/2012
0.4
moved up to 01/06/2012,
the P80 date improves to
9/27/2012
0.3
0.2
0.1
0
08/26/2012 09/09/2012 09/23/2012 10/07/2012 10/21/2012
Date
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27. 2 Stochastic Activity
Modeling in GPM Risk
GPM activities are diagrammed with start
and finish nodes
Activity nodes are encoded with stochastic rules:
An ‘or’ node is realized when the sampled predecessor
is realized
An ‘any’ node is realized when any merging predecessor
is realized
An ‘if’ node is realized if its predecessor is realized
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28. 2 Stochastic Activities
in GPM Risk
Stochastic activities occur based on a probability
of occurrence and, if occurring, are of uncertain
duration and may symbolize:
Delay risks
Branching risks
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29. 3 GPM Prime Risks vs.
CPM Risk Drivers
In GPM risk, an occurrence risk that may
impact multiple activities may occur with a
different probability and impact on each
associated activity/group of activities
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30. 3 GPM Prime Risks vs.
CPM Risk Drivers (cont’d)
In CPM, a risk driver is a particular case of a
prime risk because, if occurring, occurs with the
same probability of occurrence and the same
percentage impact for all associated activities
The risk driver approach restricts the risk to always
occur/not occur in a realization for all of the associated
activities/group of activities
This is not always true, to wit: if bad soil is hit when
excavating the SW part of a building, the bad soil risk may
not occur when excavating the NE part
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31. 4 Float Consumption12
Risks in GPM Risk
Floating: event that occurs randomly and
that involves
delaying the start of an eligible activity
within its float then existing when the activity is scheduled
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32. 4 Float Consumption12
Risks in GPM Risk
Pacing: event that occurs randomly and
that involves
delaying the start of a pacing-eligible activity
within its float then existing when the activity is scheduled
provided the ratio then-existing float/deterministic float
exceeds a threshold
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33. 4 Float Consumption12
Risks in GPM Risk (cont’d)
Modeling can control how often an eligible
activity actually floats or paces during a
realization by defining a likelihood factor
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34. Float Consumption Risks
A floating or pacing risk occurs whenever an
activity that floated or paced and that falls on the
longest path would not otherwise have been
critical but for the floating or pacing decision
The floating or pacing decision in effect caused a
critical path delay
Floating/pacing decisions rely on predicted vs.
actual durations
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35. Float Consumption Risks (cont’d)
Floating/pacing risks cannot be modeled
with CPM for two fundamental reasons:
The CPM scheduling algorithm defaults all activities to
their earliest possible dates
No activity has (total) float in a CPM schedule
during the forward pass calculations, which
means that, unlike GPM, float does not exist in
CPM when the activity is scheduled
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36. Simulation Trial, Floating & Pacing Risks
Pacing
Floating
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37. Demonstrating the CPM ‘Optimism Bias’
100%
09/28/2012
90%
09/23/2012 10/08/2012
80%
Includes early‐dates
70% and merge bias
Unbiased
60%
forecast
Num. of Iterations
50% 46%
40%
32% CPM Optimism Bias
30%
Includes early‐ Exclusion of floating & pacing events in risk
dates bias modeling combines to produce a systemic
20%
overestimation of the true probability of
accomplishing targeted completion dates
10%
PERT Early Dates Floating & Pacing
0%
08/11/2012 08/25/2012 09/08/2012 09/22/2012 10/06/2012 10/20/2012
Date
©2012-2013 Permission is granted to PMA Technologies 37

38. 5 Activity Criticality
in GPM Risk
Criticality index, while measuring the likelihood
of activities falling on the stochastic longest
path, fails to account for the correlation between
activity duration and project duration
A shorter, certain-duration activity and a longer,
uncertain-duration activity may have equal criticality
because they both fall on the stochastic longest path
Williams addressed this conundrum in 1992 when he
developed his cruciality index13 that correlates sampled
activity duration and realized project duration
If an activity duration is certain, cruciality = zero
©2012-2013 Permission is granted to PMA Technologies 38

39. Activity Priority─A New Metric
To solve the dilemma between criticality and
cruciality, some CPM risk analyzers combine
the two by multiplying criticality x cruciality
This formula tends to downplay the criticality index
The comparable statistic in GPM risk is
‘activity priority’
Activity priority equals criticality index +
criticality index x cruciality index
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40. Activity Priority─A New Metric (cont’d)
Activity priority = criticality index
if cruciality has a significance confidence level below a:
Default Confidence Threshold
GPM risk treats 66% confidence level
(2:1 odds of the value of cruciality being
correct) as a default confidence threshold
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41. Criticality & Priority Indices
Demonstrative
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42. SYNOPSIS
NETRISK
NetPoint module that allows users to work with
deterministic and probabilistic GPM &
CPM schedules seamlessly
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43. Offers a full gamut of risk management
processes, including:
‘Risk Manager’ interface, which acts as a single streamlined window
that works dynamically with the canvas rather than obscuring it
Interface for defining a fully-customizable risk breakdown structure
Fully-customizable probability & impact matrix with tolerance thresholds
Risk identification through a risk register
Full range of activity and risk correlations, including floating and pacing
Automated risk removal process for sensitivity tornado analysis
Full gamut of GPM (quantitative) schedule risk analysis
Wide range of simulation data mining that is fully customizable and that
interface with MS Excel
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44. Risked Trial Runs─P80 Date Comparisons
45 43 43 43 Uncertainty Only
Very sensitive to Uncertainty + Planned Date
40 38 38 floating Uncertainty + Floating/Pacing
37 37
35 33 33 33
31
30
30
25 24
20
15 15
15
10
5
0
Project Ready for Commissioning Elevator Install Complete Perm Power Available Start Process Installation
On the floating path
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45. GPM & CPM Risk
Software Comparison
GPM Risk as Embodied in NetRisk CPM Risk as Embodied in OPRA
Schedule view relies on time-scaled LDM networks Minimalist schedule view that relies on logic GANTT charts
Planned dates can be used to model SNE constraint dates SNE constraints cannot reflect the network stochastic nature
A risk occurs with unique probability/impact per activity-risk pair A risk occurs with the same probability on all impacted activities
Longest path, sampled path & shortest path logic constructs Longest path & sampled path logic constructs
Floating and pacing risks are modeled as random risks Neither floating nor pacing risks can be modeled
Automated risk removal for risk sensitivity analysis Manual, one-by-one risk removal for risk sensitivity analysis
Multiple simulations within the same file for easy comparisons One simulation per file complicates comparison of results
©2012-2013 Permission is granted to PMA Technologies 45

46. NetRisk & OPRA Distribution Functions
100%
PERT Early Dates
09/28/2012
Floating & Pacing OPRA
90%
09/23/2012
80%
Includes early‐dates 10/08/2012
70% and merge bias
Unbiased
60% forecast
Num. of Iterations
50% 46%
40%
32%
30% NetRisk discretizes continuous
Includes early‐
dates bias distributions by dividing the range
20% using proper (mathematical) rounding
rules, which explains the slight
10% difference in the distribution curves
0%
08/11/2012 08/25/2012 09/08/2012 09/22/2012 10/06/2012 10/20/2012
Date
©2012-2013 Permission is granted to PMA Technologies 46

47. NetRisk & OPRA Criticality
Tornado Diagrams
Install/Connect Process Equipment 100%
100%
SD Set 100%
100%
DD Set 71%
77%
MEP Process Equip 71%
77%
Substation Shops 52%
58%
Comp. CD Set 52%
58%
Permit Set 52%
58%
Subtation Installation 52%
58%
Substation Fab/Delivery 52%
58%
Gather Equip Quotes 32%
27%
Equipment Procurement 32%
27%
BOD Process Equip 32%
27%
Bid/Award Steel 20%
20% As a check, an OPRA simulation with
Steel, Joists, Decking 20%
20% 1000 iterations was run, which
Power/Lighting/Low Voltage 20%
21% showed Criticality indices within 2%
20%
SOG, Pour & Seal Decks 21% points of those calculated by NetRisk.
Shops, R & A, Delivery 20%
20%
Comp Exc, FDN 0%
1%
Start Exc, FDN 0%
1%
0% NetRisk
Piping/HVAC/FS Rough‐In 1% OPRA
FDN Permit 0%
1%
0% 20% 40% 60% 80% 100%
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48. TAKE-AWAYS
1 Any project or contract schedule that is not risked through its
life cycle does not conform to scheduling best practices
2 Any schedule that does not expressly reserve reasonable
schedule margin does not conform to best practices either
3 The CPM optimism bias impacts CPM risk analysis results in
that ‘p dates’ are biased early/are optimistic
4 GPM planned dates are better suited to risk modeling than
deterministic SNE constraint dates
5 Activity durations should be ranged using benchmarking
6 With schedule margin as critical path float, early completion
schedules are the new normal
7 There is a new sheriff in town!
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49. NetRisk Development Path 2013 - 2014
1 Visual Risk
2 High-priority User Requests
3 Automated Risk Removal in
Risk Sensitivity Analysis
4 Full Stochastic Network Modeling
5 Weather Risks
6 Full Interoperability with
Cost Risk Software
7 Integrated Resource
Leveling During Simulation
©2012-2013 Permission is granted to PMA Technologies 49

50. REFERENCES
1) Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus & Giroux.
2) Flyvbjerg, B. (2004). Procedures for dealing with optimism bias in transport planning and Flyvbjerg, B.
(2008). Curbing optimism bias and strategic misrepresentation in planning: reference class forecasting in
practice
3) NASA. (1962). PERT/COST Systems Design. DOD and NASA Guide
4) Van Slyke, R. (1963). Monte Carlo methods and the PERT problem.
5) Pritsker, A. (1966). GERT: Graphical evaluation and review technique.
6) Project Management Institute. (1996). Project management body of knowledge (1st ed.)
7) National Defense Industrial Association. (2011). Planning & scheduling excellence guide (PASEG)
8) United States Government Accountability Office. (2012). GAO schedule assessment guide
9) AACE International. (2012). CPM Schedule risk modeling and analysis: special considerations
10) AACE International. (2012). CPM Schedule risk modeling and analysis: special considerations (p. 7)
11) Kennedy, K. & Thrall, R. (1976). PLANET: A simulation approach to PERT (p. 324).
12) Ponce de Leon, G., Jentzen, G., Fredlund, D., Spittler, P. & Field, D. (2010). Guide to the forensic
scheduling body of knowledge Part I
13) Williams, T. (1992). Criticality in stochastic networks
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51. Ask Questions
Get Answers
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52. A budget reserve
is to contractors
as red meat is to
lions, and they will
devour it!
Attributed to Bent Flyvbjerg by
Kahneman in Thinking Fast and Slow
Photo source: http://www.vegansoapbox.com/we-are-not-lions/
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53. THANK YOU!
Gui Ponce de Leon PhD, PE, PMP, LEED AP
Inventor of GPM® & Developer of NetPoint®/NetRiskTM
Truth in Scheduling®
©2012-2013 Permission is granted to PMA Technologies 53