Modern Risk Quantification in Complex Projects

Non-linear Monte Carlo and System Dynamics Methodologies
 
 
Oxford University Press
  • erschienen am 16. Juli 2020
  • |
  • 275 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-0-19-258264-5 (ISBN)
 
Project practitioners and decision makers complain that both parametric and Monte Carlo methods fail to produce accurate project duration and cost contingencies in majority of cases. Apparently, the referred methods have unacceptably high systematic errors as they miss out critically important components of project risk exposure. In the case of complex projects overlooked are the components associated with structural and delivery complexity. Modern Risk Quantification in Complex Projects: Non-linear Monte Carlo and System Dynamics Methodologies zeroes in on most crucial but systematically overlooked characteristics of complex projects. Any mismatches between two fundamental interacting subsystems - a project structure subsystem and a project delivery subsystem - result in non-linear interactions of project risks. Three kinds of the interactions are distinguished - internal risk amplifications stemming from long-term ('chronic') project system issues, knock-on interactions, and risk compounding. Affinities of interacting risks compose dynamic risk patterns supported by a project system. A methodology to factor the patterns into Monte Carlo modelling referred to as non-linear Monte Carlo schedule and cost risk analysis (N-SCRA) is developed and demonstrated. It is capable to forecast project outcomes with high accuracy even in the case of most complex and difficult projects including notorious projects-outliers: it has a much lower systematic error. The power of project system dynamics is uncovered. It can be adopted as an accurate risk quantification methodology in complex projects. Results produced by the system dynamics and the non-linear Monte Carlo methodologies are well-aligned. All built Monte Carlo and system dynamics models are available on the book's companion website.
  • Englisch
  • Oxford
  • |
  • Großbritannien
  • 5,39 MB
978-0-19-258264-5 (9780192582645)
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Dr Yuri G. Raydugin is Principal Consultant of Risk Services & Solution Inc., a Canadian consulting company. Yuri worked for Royal Dutch Shell, TransCanada Pipelines, SNC-Lavalin, and Saudi Aramco, managing risks of several megaprojects. He has been involved in risk management of projects with combined budget of about $150B. Yuri has an engineering degree in nuclear physics from Urals Polytechnics Institute, Russia, a PhD in physics and mathematics from Russia's Academy of Sciences, and an MBA in business strategy from Henley Management College in England. He is a member of the Association of Professional Engineers and Geoscientists of Alberta (APEGA) and Saudi Council of Engineers (SCE).
  • Cover
  • Modern Risk Quantification in Complex Projects: Non-linear Monte Carlo and System Dynamics Methodologies
  • Copyright
  • Dedication
  • Foreword
  • Preface
  • Contents
  • List of Figures
  • List of Tables
  • List of Abbreviations
  • List of Models
  • Introduction
  • Part One: Risk Quantification in Simple Projects
  • 1: High-level Overview of Project Risk Management (PRM)
  • 1.1 Introduction
  • 1.2 Two Fundamental Goals of Risk Management
  • 1.3 Inanimate, Animate, and Mixed Systems and Risks
  • 1.4 PRM Context-PRM Method Mismatch
  • 1.4.1 A Role of PRM Planning
  • 1.4.2 Project Phases and Selection of PRM Methodologies
  • 1.4.3 Bias and PRM Zealotry
  • 1.4.4 Conventional and Unconventional PRM Methods
  • 1.4.5 Sampling Theory Basics
  • 1.4.5.1 Two kinds of statistics
  • 1.4.5.2 A general approach towards sampling
  • 1.4.5.3 Sampling in PRM
  • 1.4.5.4 Parametric sampling and inference
  • 1.4.5.5 Monte Carlo sampling and inference
  • 1.4.5.6 Implications of the small sample theory
  • 1.4.6 A List of methods Used In or In Place of Inferential Statistics
  • 1.5 A High-level overview of Conventional PRM Methodologies
  • 1.5.1. Two Important Conventional PRM Methodologies
  • 1.5.2 Advantages and Limitations of Conventional PRM Methodologies
  • 1.6 A High-level Overview of Regression-based Methodologies
  • 1.6.1 Flaws of Averages
  • 1.6.1.1 A first limitation: predictions are limited by the used sample
  • 1.6.1.2 A second limitation: a proclivity to use convenience and judgement sampling
  • 1.6.1.3 A third limitation: ignored historic aspect of data collection
  • 1.6.1.4 A fourth limitation: misalignment with mathematics fundamenta
  • 1.6.2 Low Accuracy of Parametric Inferential Methods and a Room for Their Applicability
  • 1.6.3 Remarkable Achievements of Regression-based Methods in Descriptive Statistics
  • 1.7 High-level Introduction of Emerging Unconventional Risk Quantification Methods: System Dynamics and ANN
  • 1.8 Chapter's Markup and Takeaway
  • 2: Conventional Project Risk Management (PRM) Methodologies
  • 2.1 A Definition of Project Risk
  • 2.1.1 Occurrence and Probability Uncertainty
  • 2.1.2 Uncertainty of Impact
  • 2.1.3 Uncertainty of Favourability
  • 2.1.4 Uncertainty of Manageability
  • 2.1.5 Uncertainty of Identification
  • 2.2 Three Components of a Conventional Risk Management System
  • 2.2.1 PRM Context
  • 2.2.2 PRM Process
  • 2.2.3 PRM Tools
  • 2.2.3.1 Risk breakdown structure
  • 2.2.3.2 Bowtie diagram for risk identification
  • 2.2.3.3 Bowtie diagram for risk addressing
  • 2.2.3.4 Risk assessment tools
  • 2.2.3.5 Project risk registers
  • 2.3 Role of Bias in PRM
  • 2.3.1 Psychological Bias
  • 2.3.2 Organizational bias
  • 2.4 Chapter's markup and takeaway
  • 3: Overview of Conventional Risk Quantification Methods
  • 3.1 A Challenge to Select Adequate Risk Quantification Methods
  • 3.2 Deterministic (Scoring) Risk Quantification Method
  • 3.3 Integrated Risk-based and Economics-based Selection of Project Alternatives
  • 3.4 Probabilistic (Monte Carlo) Risk Quantification Methods
  • 3.4.1 A Historic Overview of Monte Carlo Modelling
  • 3.4.2 Activity and Line-item Ranging
  • 3.4.3 Monte Carlo Schedule Risk Analysis
  • 3.4.4 Monte Carlo Cost Risk Analysis
  • 3.5 Accuracy of Conventional Risk Quantification Methods
  • 3.5.1 When Accuracy is Low
  • 3.5.2 When Accuracy is High
  • 3.5.3 Typical Distributions of Project Completion Dates and Costs
  • 3.5.4 Statistical Bias and Systematic and Random Errors
  • 3.5.5 Root Causes of Statistical Bias and Random Errors
  • 3.6 Chapter's Markup and Takeaway
  • 4: Overview of Unconventional Risk Quantification Methods
  • 4.1 Regression-based Risk Quantification Methods
  • 4.1.1 Historic Overview of Regression-based Methods
  • 4.1.2 Descriptive and Prescriptive Regression-based Methods
  • 4.1.3 Direct Parametric modelling
  • 4.1.4 Reverse Parametric Modelling
  • 4.1.5 Introduction of Uncertainty to Deterministic Results
  • 4.1.6 Systematic and Random Errors of Parametric Modelling
  • 4.1.7 A Role and Place of Parametric Modelling
  • 4.2 A Hybrid Model
  • 4.3 System Dynamics Risk Quantification Methods
  • 4.3.1 Applications of System Dynamics to Project Management
  • 4.3.2 Suitability of System Dynamics to Project Risk Quantification
  • 4.4 ANN Risk Quantification Methods
  • 4.4.1 The ANN Concept
  • 4.4.2 The ANN Modelling Process
  • 4.4.3 ANN Modelling Difficulties
  • 4.5 Accuracy of Unconventional Risk Quantification Methods
  • 4.6 Chapter's Markup and Takeaway
  • 5: Project Zemblanity Business Case (I): Linear Monte Carlo Schedule and Cost Risk Analysis (L-SCRA)
  • 5.1 A Business Case
  • 5.2 L-SCRA Method Statement
  • 5.3 A SCRA Workshop
  • 5.4 L-SCRA Modelling Results
  • 5.5 Discussion of L-SCRA Results
  • 5.6 Further Improvements of the L-SCRA Modelling
  • 5.6.1 Before-addressing and After-addressing Modelling
  • 5.6.2 Burn Rates
  • 5.6.3 Jointed Confidence Level (JCL)
  • 5.6.3.1 JCL: a general concept
  • 5.6.3.2 JCL: applicability
  • 5.6.3.3 JCL correction factors for the L-SCRA model
  • 5.6.4 Sensitivity Charts
  • 5.6.5 Fighting Systematic Error
  • 5.7 Chapter's Markup and Takeaway
  • 6: A Brief Introduction to System Dynamics (SD)
  • 6.1 A System-dynamics Crash Course: An Overview of a BT Model
  • 6.2 Results for Models BT1 and BT2
  • 6.3 Results for Models BT3 and BT4
  • 6.4 Results for Model BT5
  • 6.5 Chapter's Markup and Takeaway
  • 7: Project System Dynamics (SD): A Linear Case
  • 7.1 Project System Dynamics Modelling Approach
  • 7.2 Project System Dynamics Model SD1: A Risk-free Case
  • 7.3 Project SD Model SD2: Two Standalone Rework Cycles
  • 7.4 Project SD Model SD3: Mirroring the L-SCRA Model
  • 7.5 Chapter's Markup and Takeaway
  • Part Two: Principles of Risk Quantification in Complex Projects
  • 8: Introduction to Project Complexity
  • 8.1 Complexity of Projects
  • 8.1.1 Complexity of Projects: A Literature Review
  • 8.1.2 Complexity of Projects: Practically Valuable ideas
  • 8.1.3 Complexity of Projects: A New Definition
  • 8.2 A New Project Complexity Framework
  • 8.2.1 A Fundamental Project Complexity Concept: A PSS-PDS Mismatch
  • 8.2.2 Primary Complexity Aspects
  • 8.3 Complexity in Projects
  • 8.4 Chapter's Markup and Takeaway
  • 9: Interactions in a Project System
  • 9.1 Traditional Definitions of Project and Project Risk
  • 9.2 A Project System as Two Interacting Subsystems
  • 9.2.1 Where a Project Owner Belongs To?
  • 9.2.2 Is It Enough to Consider Two Subsystems?
  • 9.2.3 A New Definition of Project
  • 9.3 From Standalone Risks to Dynamic Risk Patterns: A New Definition of Risk
  • 9.3.1 Standalone Non-interacting Risks in Complex Projects
  • 9.3.2 Dynamic Patterns of Interacting Risks in Complex Projects
  • 9.4 Risk Identification in Complex Projects: Modified Bowtie Diagrams
  • 9.4.1 A Bowtie Diagram for Internal Risk Amplification
  • 9.4.2 A Bowtie Diagram for Knock-on Interactions
  • 9.4.3 Bowtie Diagram for Risk Compounding
  • 9.4.4 Using Amended Bowtie Diagrams for Risk Interaction's Quantification
  • 9.4.5 Using the Amended Bowtie Diagrams for Risk Interaction's Addressing
  • 9.5 Chapter's Markup and Takeaway
  • 10: A Project Structure Subsystem (PSS)
  • 10.1 Differentiation (Project Parts)
  • 10.1.1 Project Deliverables
  • 10.1.2 Required Labour
  • 10.1.3 Locations
  • 10.1.4 Technologies
  • 10.1.5 Interface Points
  • 10.1.6 Stakeholders
  • 10.2 Interdependencies (Parts' Interactions)
  • 10.3 Chapter's Markup and Takeaway
  • 11: A Project Delivery Subsystem (PDS)
  • 11.1 Adopted Procedures
  • 11.1.1 Opportunity Shaping
  • 11.1.2 Project Team Design
  • 11.1.3 Engineering
  • 11.1.4 Contracting and Procurement
  • 11.1.5 Construction
  • 11.1.6 Stakeholder Management
  • 11.1.7 Change and Risk Management Procedures
  • 11.2 Procedure's Implementation
  • 11.3 Chapter's Markup and Takeaway
  • 12: Project System Maturity Evaluation
  • 12.1 Evaluation of the PSS
  • 12.2 Evaluation of the PDS
  • 12.3 The Project System Maturity Evaluation Framework
  • 12.4 Chapter's Markup and Takeaway
  • 13: Non-linear Multipliers for Risk Interactions
  • 13.1 A Monte Carlo Modelling without Correlations
  • 13.2 A Traditional Monte Carlo Modelling with Correlations
  • 13.3 Linear vs. Non-linear Regression Models
  • 13.4 Risk Interactions in Non-linear Monte Carlo Models
  • 13.4.1 Internal Non-linear Amplifications of Standalone Risks
  • 13.4.2 Knock-on Impacts from Up-the-line Risks
  • 13.4.3 Risk Compounding
  • 13.4.4 A Dual Nature of Non-linear Multipliers
  • 13.4.5 Non-linear General Uncertainties
  • 13.4.6 Non-linear Multipliers in Case of Upside Risks (Opportunities)
  • 13.4.7 Units of Measure of Non-linearity Parameters
  • 13.5 Chapter's Markup and Takeaway
  • 14: Non-linear Monte Carlo Modelling Requirements
  • 14.1 Cross-risk Interaction Mapping: Dynamic Risk Patterns
  • 14.2 Risk Interactions: An Overall Calibration and Individual Evaluations
  • 14.2.1 An Overall Risk Interaction Calibration
  • 14.2.2 Evaluation of Individual Risk Interactions
  • 14.3 Risk Interaction Addressing
  • 14.4 Definition of a Generic L-SCRA Case
  • 14.5 Chapter's Markup and Takeaway
  • Part Three: Risk Quantification in Complex Projects
  • 15: Project Zemblanity Business Case (II): Non-linear Monte Carlo Schedule and Cost Risk Analysis (N-SCRA)
  • 15.1 N-SCRA Method Statement
  • 15.2 A First N-SCRA-A Workshop
  • 15.3 N-SCRA-A Modelling results
  • 15.4 Discussion of N-SCRA-A Results
  • 15.4.1 A Second N-SCRA-B Workshop
  • 15.5 N-SCRA-B Modelling Results
  • 15.6 Discussion of N-SCRA-B Results
  • 15.7 Comparison of Results for the Models L-SCRA, N-SCRA-A, and N-SCRA-B
  • 15.7.1 Comparison of General Results
  • 15.7.2 Comparison of Schedule-driven Costs
  • 15.7.3 Comparison of JCL Correction Factors
  • 15.7.4 Main Factors of Project Risk Exposure: A Hierarchy of Importance
  • 15.8 Consortium Participants' Conclusions
  • 15.9 Chapter's Markup and Takeaway
  • 16: Project System Dynamics: A Non-linear Case
  • 16.1 A System Dynamics Model SD4: Mirroring the Model N-SCRA-A
  • 16.2 A System Dynamics Model SD5: Mirroring the Model N-SCRA-B
  • 16.3 A System Dynamics Model SD4 with Compounding
  • 16.4 Project System Dynamics: Lessons Learned
  • 16.4.1 Limitations of Project System Dynamics Modelling
  • 16.4.2 A Quest for Calibration
  • 16.4.3 A Possibility of Monte Carlo Model Input's Modelling
  • 16.5 Chapter's Markup and Takeaway
  • 17: Spin-off Discussions on Project Complexity
  • 17.1 Projects as Complex Adaptive Systems and Over-planning
  • 17.2 Single and Multiple Project Failure Modes
  • 17.3 A Single Failure Mode: An Analogy from Physics
  • 17.4 Quality in Complex Project Systems: Practical Aspects
  • 17.5 Nature and Origin of General Uncertainties and Internal Risk Amplifications
  • 17.5.1 The Nature and Origin of General Uncertainties
  • 17.5.2 General Uncertainties and the Hybrid Model
  • 17.5.3 The Nature and Origin of Internal Risk Amplifications
  • 17.6 Expected Project Outcome: Reverse Engineering and Stretched Targets
  • 17.6.1 Expected Project Outcome: Reverse Engineering
  • 17.6.2 Expected Project Outcome: Stretched Targets
  • 17.7 Chapter's Markup and Takeaway
  • 18: Conclusion
  • Appendix A: The Linear Monte Carlo Schedule and Cost Risk Analysis Model (L-SCRA)
  • A.1 Outcome of Deterministic Risk Analysis
  • A.2 Inputs to the Linear Schedule Risk Analysis (L-SCRA) Model
  • A.3 The Workable Model L-SCRA
  • Appendix B: Analytical Solutions for the Bathtub (BT) Model
  • B.1 A General Differential Equation for the BT Model
  • B.2 An Analytical Solution for the Model BT1
  • B.3 An Analytical Solution for the Model BT2
  • B.4 An Analytical Solution for the Model BT3
  • B.5 An Analytical Solution for the Model BT4
  • B.6 An Analytical Solution for the Model BT5
  • B.7 The workable models BT1-BT5
  • Appenidx C: Inputs to the System Dynamics Models SD1-SD3
  • C.1 Activation of the Construction Rework Cycle
  • C.2 Times to Detect Errors and Omissions
  • C.3 Selected Timing of Risk Impacts in the Model SD3
  • C.4 Recalculation of Risk Impacts: The General Approach
  • C.5 Recalculation of Risk Impacts for the Models SD2 and SD3
  • Appendix D: The Non-linear Monte Carlo Schedule and Cost Risk Analysis Models N-SCRA-A, N-SCRA-B, and N-SCRA-C
  • D.1 Factoring Risk Interactions to the SCRA Risk Register
  • D.2 N-SCRA Model's Calibration
  • D.3 Assessment of Non-linearity Parameters: the Model N-SCRA-A
  • D.4 Assessment of Non-linearity Parameters: the Model N-SCRA-B
  • D.5 Modified Inputs to the Monte Carlo Models N-SCRA-A and N-SCRA-B
  • D.6 Inputs to the Monte Carlo Models N-SCRA-C
  • D.7 The Workable Models N-SCRA-A, N-SCRA-B, and N-SCRA-C
  • Appendix E: EInputs to the System Dynamics Models SD4 and SD5
  • E.1 Activation of the Construction Rework Cycle in the Models SD4 and SD5
  • E.2 Times to Detect Errors and Omissions in the Models SD and SD5
  • E.3 Selected Timing of Risk Impacts
  • E.4 Risk Multipliers in the Models SD4 and SD5
  • E.5 Recalculation of Risk Impacts for the Models SD4 and SD5: General Approach
  • E.6 Recalculation of Risk Impacts for the Model SD4
  • E.7 Recalculation of Risk Impacts for the Model SD5
  • E.8 Recalculation of Risk Impacts for the Model SD4 with Compounding
  • E.9 A Share of Engineering Errors and Omissions Resolved in Construction
  • E.10 The Workable Models SD4 and SD4
  • Glossary of Terms
  • References
  • Index

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