Mitigating the  Planning Fallacy   (Risked Schedules─The New Normal)  Gui Ponce de Leon, PhD, PE, PMP, LEED AP  GPM Boot C...
There are lies, there are damned lies,         and then there are deterministic schedules                           Attrib...
PRESENTATION OUTLINE                                                       Mitigating the Planning Fallacy                ...
Just What Is the Planning Fallacy?                                                       Kahneman1 and his longtime       ...
Planning Fallacy aka Optimism Bias                                                       Bent Flyvbjerg2, the renowned Dan...
Planning Fallacy aka Optimism Bias (cont’d)                                                       Bent Flyvbjerg futher no...
Scheduling Strategies for      Mitigating the Planning Fallacy        1   Rely on an outside view as advocated      by Kah...
Scheduling Strategies for      Mitigating the Planning Fallacy        2  Work with risked schedules as the      new normal...
Taking an Outside View      Relative to Schedules                      The schedule is built using a database of historica...
Working with           Is the                            Risked Schedules           New Normal©2012-2013 Permission is gra...
THE CONCEPT OF                                          RISKED SCHEDULES©2012-2013 Permission is granted to PMA Technologi...
Risked schedules are generated             in the following sequence:A base‐case schedule portraying                    Th...
RISKED SCHEDULES                                             The baseline schedule is risk assessed                       ...
1955            1960           1965            1970         1975          1980           1985         1990       1995   20...
1998            2000           2002            2004             2006          2008           2010       2012         2014 ...
Emerging Consensus on      CPM Risk Analysis                                                       A schedule risk analysi...
Emerging Consensus on      CPM Risk Analysis (cont’d)                                                       The baseline s...
Emerging Consensus on      CPM Risk Analysis (cont’d)                                                       An SRA is perf...
CPM Risk Modeling & Analysis                                                       Minimal use of constraints on activitie...
CPM Risk Modeling & Analysis                                                       Risk drivers occur with the same probab...
INTRODUCING GPM RISK                                            MODELING & ANAYSIS          1         GPM planned dates ar...
1              Planned Dates ILO SNE                                                          Constraints                 ...
1              Planned Dates ILO SNE                                                          Constraints (cont’d)        ...
Schedule Demonstrative,      Base-Case Scenario                                                   Constraint Date©2012-201...
Simulation Trial, Planned Date      ILO SNE Date11                                                                 Not Ris...
Planned Dates vs. SNE Constraints Results                                                                 01/09/2012      ...
2              Stochastic Activity                                                          Modeling in GPM Risk          ...
2              Stochastic Activities                                                          in GPM Risk                 ...
3              GPM Prime Risks vs.                                                          CPM Risk Drivers              ...
3              GPM Prime Risks vs.                                                          CPM Risk Drivers (cont’d)     ...
4              Float Consumption12                                                          Risks in GPM Risk             ...
4              Float Consumption12                                                          Risks in GPM Risk             ...
4              Float Consumption12                                                          Risks in GPM Risk (cont’d)    ...
Float Consumption Risks      A floating or pacing risk occurs whenever an      activity that floated or paced and that fal...
Float Consumption Risks (cont’d)      Floating/pacing risks cannot be modeled      with CPM for two fundamental reasons:  ...
Simulation Trial, Floating & Pacing Risks                                                       Pacing                    ...
Demonstrating the CPM ‘Optimism Bias’                           100%                                                      ...
5              Activity Criticality                                                          in GPM Risk                  ...
Activity Priority─A New Metric      To solve the dilemma between criticality and      cruciality, some CPM risk analyzers ...
Activity Priority─A New Metric (cont’d)                Activity priority = criticality index          if cruciality has a ...
Criticality & Priority Indices      Demonstrative©2012-2013 Permission is granted to PMA Technologies   41
SYNOPSIS                                          NETRISK                                           NetPoint module that a...
Offers a full gamut of risk management      processes, including:                            ‘Risk Manager’ interface, whi...
Risked Trial Runs─P80 Date Comparisons    45                        43                         43                         ...
GPM & CPM Risk      Software Comparison           GPM Risk as Embodied in NetRisk                                       CP...
NetRisk & OPRA Distribution Functions                           100%                                         PERT         ...
NetRisk & OPRA Criticality      Tornado Diagrams    Install/Connect  Process Equipment                                    ...
TAKE-AWAYS          1         Any project or contract schedule that is not risked through its                    life cycl...
NetRisk Development Path 2013 - 2014       1         Visual Risk                           2        High-priority User Req...
REFERENCES1) Kahneman, D. (2011).  Thinking, Fast and Slow. New York: Farrar, Straus & Giroux.    2) Flyvbjerg, B. (2004)....
Ask Questions   Get Answers ©2012-2013 Permission is granted to PMA Technologies   51
A budget reserveis to contractorsas red meat is tolions, and they willdevour it!    Attributed to Bent Flyvbjerg by Kahnem...
THANK YOU!                                                       Gui Ponce de Leon PhD, PE, PMP, LEED AP                  ...
Upcoming SlideShare
Loading in …5
×

Risk Modeling and Analysis (Mitigating the Planning Fallacy)

2,132 views

Published on

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.

Published in: Business

Risk Modeling and Analysis (Mitigating the Planning Fallacy)

  1. 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©2012-2013 Permission is granted to PMA Technologies 1
  2. 2. There are lies, there are damned lies, and then there are deterministic schedules Attributed to Dr. Vivek Puri, PMA’s resident simulation guru©2012-2013 Permission is granted to PMA Technologies 2
  3. 3. PRESENTATION OUTLINE Mitigating the Planning Fallacy CPM Risk Modeling & Analysis GPM® Risk Modeling & Analysis NetRiskTM Synopsis Summary & Take-Aways©2012-2013 Permission is granted to PMA Technologies 3
  4. 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©2012-2013 Permission is granted to PMA Technologies 4
  5. 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©2012-2013 Permission is granted to PMA Technologies 5
  6. 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©2012-2013 Permission is granted to PMA Technologies 6
  7. 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©2012-2013 Permission is granted to PMA Technologies 7
  8. 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)©2012-2013 Permission is granted to PMA Technologies 8
  9. 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.©2012-2013 Permission is granted to PMA Technologies 9
  10. 10. Working with Is the Risked Schedules New Normal©2012-2013 Permission is granted to PMA Technologies 10
  11. 11. THE CONCEPT OF RISKED SCHEDULES©2012-2013 Permission is granted to PMA Technologies 11
  12. 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. 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©2012-2013 Permission is granted to PMA Technologies 13
  14. 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”©2012-2013 Permission is granted to PMA Technologies 14
  15. 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©2012-2013 Permission is granted to PMA Technologies 15
  16. 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©2012-2013 Permission is granted to PMA Technologies 16
  17. 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©2012-2013 Permission is granted to PMA Technologies 17
  18. 18. Emerging Consensus on CPM Risk Analysis (cont’d) An SRA is performed on the schedule periodically as the schedule is updated©2012-2013 Permission is granted to PMA Technologies 18
  19. 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’©2012-2013 Permission is granted to PMA Technologies 19
  20. 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©2012-2013 Permission is granted to PMA Technologies 20
  21. 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©2012-2013 Permission is granted to PMA Technologies 21
  22. 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©2012-2013 Permission is granted to PMA Technologies 22
  23. 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©2012-2013 Permission is granted to PMA Technologies 23
  24. 24. Schedule Demonstrative, Base-Case Scenario Constraint Date©2012-2013 Permission is granted to PMA Technologies 24
  25. 25. Simulation Trial, Planned Date ILO SNE Date11 Not Risked Planned Date©2012-2013 Permission is granted to PMA Technologies 25
  26. 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©2012-2013 Permission is granted to PMA Technologies 26
  27. 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©2012-2013 Permission is granted to PMA Technologies 27
  28. 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©2012-2013 Permission is granted to PMA Technologies 28
  29. 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©2012-2013 Permission is granted to PMA Technologies 29
  30. 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©2012-2013 Permission is granted to PMA Technologies 30
  31. 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©2012-2013 Permission is granted to PMA Technologies 31
  32. 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©2012-2013 Permission is granted to PMA Technologies 32
  33. 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©2012-2013 Permission is granted to PMA Technologies 33
  34. 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©2012-2013 Permission is granted to PMA Technologies 34
  35. 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©2012-2013 Permission is granted to PMA Technologies 35
  36. 36. Simulation Trial, Floating & Pacing Risks Pacing Floating©2012-2013 Permission is granted to PMA Technologies 36
  37. 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. 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. 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©2012-2013 Permission is granted to PMA Technologies 39
  40. 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©2012-2013 Permission is granted to PMA Technologies 40
  41. 41. Criticality & Priority Indices Demonstrative©2012-2013 Permission is granted to PMA Technologies 41
  42. 42. SYNOPSIS NETRISK NetPoint module that allows users to work with deterministic and probabilistic GPM & CPM schedules seamlessly©2012-2013 Permission is granted to PMA Technologies 42
  43. 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©2012-2013 Permission is granted to PMA Technologies 43
  44. 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©2012-2013 Permission is granted to PMA Technologies 44
  45. 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. 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. 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%©2012-2013 Permission is granted to PMA Technologies 47
  48. 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!©2012-2013 Permission is granted to PMA Technologies 48
  49. 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. 50. REFERENCES1) 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 practice3) NASA. (1962). PERT/COST Systems Design. DOD and NASA Guide4) 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 guide9) 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 ©2012-2013 Permission is granted to PMA Technologies 50
  51. 51. Ask Questions Get Answers ©2012-2013 Permission is granted to PMA Technologies 51
  52. 52. A budget reserveis to contractorsas red meat is tolions, and they willdevour it! Attributed to Bent Flyvbjerg by Kahneman in Thinking Fast and Slow Photo source: http://www.vegansoapbox.com/we-are-not-lions/ ©2012-2013 Permission is granted to PMA Technologies 52
  53. 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

×