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UID:

Format: 1 Online-Ressource (XXVII, 382 p. 94 illus., 5 illus. in color).

ISBN: 978-3-031-56988-3

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-56987-6

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-56989-0

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-56990-6

Language: English

Subjects: Economics

DOI: 10.1007/978-3-031-56988-3

URL: Volltext  (URL des Erstveröffentlichers)

URL: Volltext  (URL des Erstveröffentlichers)

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XXVII, 382 p. 94 illus., 5 illus. in color. , online resource.

Edition: 1st ed. 2024.

ISBN: 9783031569883

Content: This book integrates for readers three areas of knowledge, pertaining to risk-based project decision making: project risk management (PRM), complexity theory, and decision-making under deep uncertainty (DMDU). Readers will appreciate that in practice, too often relevant complexity and uncertainty factors are either ignored or overlooked resulting in epic project failures. The author discusses a variety of methodologies and a decision-tree-type framework to determine why, when and how particular methodologies should be applied to ensure project success. These include nonlinear Monte Carlo techniques, a dynamic adaptive methodology to adapt to external environment changes, game theory for devising robust decision-making criteria, systems dynamics and cost escalation modelling, as well as risk-based & economic-based alternatives selection methodologies. This book will be an eye-opener for many PRM practitioners, helping to increase their chances of project success by properly handlinginescapable project-complexity and deep-uncertainty implications in specific contexts. Integrates project risk management (PRM), complexity theory, and decision-making under deep uncertainty (DMDU); Provides conceptual overview of PRM, project complexity and DMDU methodologies, their interdependencies and integration; Enables robust, risk-based decision-making for contingency development and alternatives selection, rooted in game theory.

Note: Introduction -- PRM and types of project uncertainties -- Overview of DMDU methodologies -- Project complexity concept -- Decision-making framework -- Selection of project options in situations of deep uncertainty -- Development of project schedule and cost contingencies in complex projects -- Cost escalation and exchange-rate volatility risk assessment methods -- High-level overview of simplistic Monte Carlo and parametric risk assessment methods -- Case study 1: applications of a tradition PRM (scoring method) -- Case study 2: applications of "linear Monte Carlo" methodology -- Case study 3: applications of "non-linear Monte Carlo" methodology -- Case study 4: selection of project options (a few "futures") -- Case study 5: selection of project options (multiple "futures") -- Case study 6: applications of cost escalation and exchange-rate volatility modelling methods -- Conclusion.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783031569876

Additional Edition: Printed edition: ISBN 9783031569890

Additional Edition: Printed edition: ISBN 9783031569906

Language: English

DOI: 10.1007/978-3-031-56988-3

URL: https://doi.org/10.1007/978-3-031-56988-3

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (399 pages)

Edition: First edition.

ISBN: 9783031569883

Note: Intro -- Preface -- References -- Contents -- Abbreviations -- Chapter 1: Introduction: Why and How This Book Has Been Written? -- References -- Chapter 2: Cornerstone Ideas, Concepts and Paradigms -- 2.1 Traditional Project Risk Management Paradigm: Standalone Risks and Predict-Then-Act Decision-Making Pattern -- 2.1.1 Predict-Then-Act Decision-Making Pattern -- 2.1.2 Traditional Definition of Risk -- 2.1.3 Additional Inconsistencies of the Traditional PRM and Departure from It -- 2.2 Nonlinear Project Risk Management Paradigm: Interacting Risks -- 2.2.1 Inconsistency of the Traditional PRM Paradigm in Complex Projects -- 2.2.2 A Project as a System -- 2.2.3 Risk Interactions in Complex Project Systems -- 2.2.4 Risk Definition for Complex Projects -- 2.2.5 Emergence of Non-linear Monte Carlo Methodology -- 2.2.6 Dynamics of Complex Projects as Complex Systems -- 2.3 When Predict-Then-Act Decision-Making Does Not Work? -- 2.3.1 Limits and Shortcomings of Predict-Then-Act Decision-Making Pattern -- 2.3.2 Essay on Economy and Structural Imbalances: Micro-Economy Vs. Macro-Economy Mindsets -- 2.3.3 What's in It for a Project Specialist?: Emergence of Monitor-and-Adapt Decision-Making Pattern -- 2.3.4 Assay on Systems Thinking -- 2.3.5 Some Previous Practical Examples -- 2.4 Shaping Monitor-and-Adapt Decision-Making -- 2.4.1 Essay on Mathematical Theory of Games -- 2.4.2 Decision Making Under Deep Uncertainty (DMDU) -- 2.4.3 Definition of Risk for the Monitor-and-Adapt Decision-Making Pattern -- 2.4.4 Model-Centric Concept of Future -- 2.4.5 Future-Based Project Uncertainty Taxonomy and Its Relevance to PRM -- 2.4.6 Overview of Deep Uncertainty Handling Methods -- 2.4.7 A Way Forward -- 2.5 Merging Traditional, Complexity and Deep Uncertainty Paradigms with Project Work Process -- 2.5.1 Limitations of the Traditional Project Work Process. , 2.5.2 Dynamic Adaptive Project Work Process -- 2.6 Intent of this Book -- References -- Part I: Traditional PRM and Vicinities -- Chapter 3: Scoring Method -- 3.1 Basic Concepts Behind the Traditional PRM -- 3.1.1 Project Value Concept -- 3.1.2 Project Objectives -- 3.1.2.1 Quantifiable Project Objectives -- 3.1.2.2 Qualitative Project Objectives -- 3.1.2.3 Hard Vs. Soft Objectives Vs. Modelling -- 3.1.2.4 Reasonable Number of Project Objectives -- 3.1.3 Traditional Risk Definition -- 3.1.4 Project Uncertainty Parameters -- 3.1.4.1 Uncertainty of Impact -- 3.1.4.2 Uncertainty of Occurrence -- 3.1.4.3 Four Primary Risk Objects of the Traditional PRM -- 3.1.4.4 Uncertainty of Favourability -- 3.1.4.5 Uncertainty of Manageability -- 3.1.4.6 Uncertainty of Identification -- 3.1.4.7 Additional Terminology: (Un)Known-(Un)Knowns -- 3.1.4.8 PRM System to Handle Uncertainty Parameters -- 3.1.5 Role of Bias -- 3.1.5.1 Definitions and Types of Bias -- 3.1.5.2 Psychological Bias -- 3.1.5.3 Organizational Bias -- 3.1.5.4 Role of Bias in Selecting Wrong PRM Tools -- 3.1.5.5 Bias and Stretched Targets -- 3.1.6 PRM Fallacies -- 3.2 Traditional PRM Framework: Context and Requirements -- 3.2.1 Understanding PRM Context -- 3.2.2 Shaping PRM Requirements -- 3.2.2.1 Project Categories -- 3.2.2.2 Line-of-Sight Concept and Three PRM Integration Dimensions -- 3.2.2.3 Project Phase -- 3.2.2.4 PRM Deliverables -- 3.3 Traditional PRM Process -- 3.4 Traditional PRM Tools -- 3.4.1 Risk Breakdown Structure (RBS) -- 3.4.2 A Bowtie Diagram -- 3.4.2.1 Using the Traditional Bowtie Diagram for Risk Identification -- 3.4.2.2 Using Bowtie Diagram for Risk Addressing -- 3.4.2.3 Using Bowtie Diagram for Probability Assessments -- 3.4.3 Risk Assessment Matrix (RAM) -- 3.4.3.1 RAM for Scoring Risk Assessments -- 3.4.3.2 RAM for Risk Addressing -- 3.4.3.3 RAM for Risk Reporting. , 3.4.4 Risk Register -- 3.4.5 PRM Plan -- 3.4.6 PRM Questionnaire -- 3.5 Business Case: Traditional PRM Application -- References -- Chapter 4: Linear Monte Carlo Methodology -- 4.1 Traditional Monte Carlo Framework: Context and Requirements -- 4.1.1 Two Traditional PRM Risk Assessment Methodologies: Scoring Vs. Monte Carlo -- 4.1.2 Versions of Sampling Methodology -- 4.1.3 History of Monte Carlo Methodology -- 4.1.4 Comparison of Monte Carlo Methodology Versions -- 4.1.5 Organizational Context of SCRA Implementation -- 4.1.6 Confidence Levels and Decision-Making Criteria -- 4.1.6.1 Pre-determined Confidence Levels -- 4.1.6.2 Stretches Targets -- 4.1.6.3 Project and Corporate Reserves -- 4.1.6.4 Joint Confidence Levels (JCL) -- 4.1.6.5 Unknown-Unknown Allowances -- 4.1.6.6 Contingency Drawdown -- 4.2 Traditional Monte Carlo Process -- 4.2.1 Plan -- 4.2.2 Prepare -- 4.2.2.1 Freezing Project Scope -- 4.2.2.2 Development of a Proxy Schedule -- 4.2.2.3 Developing of a Cost Estimate -- 4.2.2.4 Converting Existing Scoring Risk Register to Probabilistic (Monte Carlo) Risk Register -- 4.2.2.5 Templates for Weather Events and Environmental Protection Restrictions -- 4.2.3 Risk Mapping of Schedule Risks -- 4.2.4 Assess Before Addressing (As-Is) (Monte Carlo) -- 4.2.5 Factor in Addressing -- 4.2.6 Assess After Addressing (To-Be) (Monte Carlo) -- 4.2.7 Factor in General Uncertainties -- 4.2.8 Correlations -- 4.2.9 Validate Inputs -- 4.2.10 Build and Run Monte Carlo Model -- 4.2.11 Review Results -- 4.2.12 What-If Scenarios? -- 4.2.13 Develop and Run What-If Scenarios -- 4.2.14 Report -- 4.3 Traditional Monte Carlo Tools -- 4.3.1 L-SCRA Input Data Collection Templates -- 4.3.1.1 General Duration Uncertainties (Template T1) -- 4.3.1.2 General Cost Uncertainties (Template T2) -- 4.3.1.3 Schedule and Cost Uncertain Events (Template T3): Probabilistic Risk Register. , 4.3.1.4 Weather Modelling (Template T4) -- 4.3.1.5 Environment-Protection Closure Windows (Template T5) -- 4.3.1.6 Resources (Template T6) -- 4.3.2 Monte Carlo Software Packages for L-SCRA Modelling -- 4.4 Business Case: Traditional Monte Carlo (L-SCRA) Modelling -- References -- Chapter 5: Phenomenological Methods -- 5.1 General Overview of Phenomenological Methods -- 5.2 Schedule Benchmarking -- 5.2.1 Organizational Framework of Benchmarking -- 5.2.2 Benchmarking Process -- 5.2.2.1 Plan -- 5.2.2.2 Establishing Discipline Duration Dictionaries (DDD) -- 5.2.2.3 Establishing Rate Tables -- 5.2.2.4 Merging DDD and RT with a Project WBS -- 5.2.2.5 Running Benchmarking Metrics -- 5.2.2.6 Benchmarking Result's Validation -- 5.2.2.7 Reporting -- 5.2.3 Benchmarking Tools -- 5.3 Parametric Cost Estimating -- 5.3.1 Organizational Framework of Parametric Cost Estimating -- 5.3.2 Parametric Cost Estimating Process -- 5.3.2.1 Plan -- 5.3.2.2 Model Definition -- 5.3.2.3 Data Collection -- 5.3.2.4 Data Normalization and Model Development -- 5.3.2.5 Model Testing -- 5.3.2.6 Cost Forecasting -- 5.3.2.7 Reporting -- 5.3.3 Parametric Cost Estimating Tools -- 5.3.4 Main Deliverables -- References -- Chapter 6: Risk-Based and Economic-Based Alternative's Selection (RBEBAS) -- 6.1 RBEBAS Framework: Context and Requirements -- 6.1.1 A Project as an Idea in FEL0 and FEL1 -- 6.1.2 RBEBAS Context in FEL2A -- 6.1.3 Risk Definition for Alternative's Selection -- 6.1.4 RBEBAS Organizational Requirements -- 6.1.5 Alternative's Selection beyond FEL2A -- 6.1.6 Role of Bias in RBEBAS -- 6.2 RBEBAS Process -- 6.2.1 Plan -- 6.2.2 Generate Alternatives -- 6.2.3 Formulate Alternatives -- 6.2.4 Identify, Relevant?, Reject -- 6.2.5 Assess As-Is, Plan Response, Assess To-Be -- 6.2.6 Risk Level Acceptable? and Reject -- 6.2.7 Develop NPV CF -- 6.2.8 Select Alternative with a Best NPV CF. , 6.2.9 Reporting -- 6.3 RBEBAS Tools -- 6.3.1 Risk Breakdown Structure (RBS) for RBEBAS -- 6.3.2 Bowtie Diagram for RBEBAS -- 6.3.3 Risk Assessment Matrix (RAM) for RBEBAS -- 6.3.4 Risk Register for RBEBAS -- 6.4 Business Case: Using RBEBAS -- References -- Chapter 7: Cost-Escalation and Exchange-Rate Volatility Modelling -- 7.1 Cost-Escalation and Exchange-Rate Volatility Framework: Context and Requirements -- 7.1.1 Two Additional Cost Risk Contingencies -- 7.1.2 Three Cost-Escalation Modelling Methods -- 7.1.2.1 General-Inflation (GI) Modelling -- 7.1.2.2 Project-Cost-Structure (PCS) Modelling -- 7.1.2.3 Project-Cash-Flow (PCF) Modelling -- 7.1.3 Foundations of the PCF Method for Cost Escalation Modelling -- 7.1.4 Foundations of the PCF Method for Exchange-Rate Volatility Modelling -- 7.1.5 Upgrade of the PCF Method to Its Probabilistic (Monte Carlo) Version -- 7.1.6 Cash Flow Optimization to Reduce Cost Escalation and Exchange-Rate Volatility Contingencies -- 7.1.7 PCF Method Implementation Requirements -- 7.2 Cost-Escalation and Exchange-Rate Volatility Modelling Process -- 7.2.1 Plan -- 7.2.2 Probabilistic? -- 7.2.3 Cash Flow Statement -- 7.2.4 Marco-Economic Indices Selection -- 7.2.5 Contract Markups -- 7.2.6 Uncertainty Ranges -- 7.2.7 PCF Model Development -- 7.2.8 PCF Model Run -- 7.2.9 PCF Result's Review -- 7.2.10 What-If Scenarios? -- 7.2.11 What-If Scenario's Development -- 7.2.12 What-If Scenario's Runs -- 7.2.13 RBEBAS Implications -- 7.2.14 Reporting -- 7.3 Cost-Escalation and Exchange-Rate Volatility Modelling Tools -- 7.4 Business Case: Cost Escalation Modelling -- References -- Part II: Project System Complexity, Nonlinear PRM and Systems Dynamics -- Chapter 8: Projects as Systems -- 8.1 A Generic Structure of Projects as Systems: Three Interacting Subsystems -- 8.1.1 Four Project System Complexity Types. , 8.1.2 Three Types of Part's Interactions.

Additional Edition: Print version: Raydugin, Yuri G. Risk-Based Project Decisions in Situations of High Complexity and Deep Uncertainty Cham : Springer International Publishing AG,c2024 ISBN 9783031569876

Language: English

UID:

Format: 1 online resource (399 pages)

Edition: 1st ed. 2024.

ISBN: 9783031569883

Content: This book integrates for readers three areas of knowledge, pertaining to risk-based project decision making: project risk management (PRM), complexity theory, and decision-making under deep uncertainty (DMDU). Readers will appreciate that in practice, too often relevant complexity and uncertainty factors are either ignored or overlooked resulting in epic project failures. The author discusses a variety of methodologies and a decision-tree-type framework to determine why, when and how particular methodologies should be applied to ensure project success. These include nonlinear Monte Carlo techniques, a dynamic adaptive methodology to adapt to external environment changes, game theory for devising robust decision-making criteria, systems dynamics and cost escalation modelling, as well as risk-based & economic-based alternatives selection methodologies. This book will be an eye-opener for many PRM practitioners, helping to increase their chances of project success by properly handlinginescapable project-complexity and deep-uncertainty implications in specific contexts. Integrates project risk management (PRM), complexity theory, and decision-making under deep uncertainty (DMDU); Provides conceptual overview of PRM, project complexity and DMDU methodologies, their interdependencies and integration; Enables robust, risk-based decision-making for contingency development and alternatives selection, rooted in game theory.

Note: Introduction -- PRM and types of project uncertainties -- Overview of DMDU methodologies -- Project complexity concept -- Decision-making framework -- Selection of project options in situations of deep uncertainty -- Development of project schedule and cost contingencies in complex projects -- Cost escalation and exchange-rate volatility risk assessment methods -- High-level overview of simplistic Monte Carlo and parametric risk assessment methods -- Case study 1: applications of a tradition PRM (scoring method) -- Case study 2: applications of “linear Monte Carlo” methodology -- Case study 3: applications of “non-linear Monte Carlo” methodology -- Case study 4: selection of project options (a few “futures”) -- Case study 5: selection of project options (multiple “futures”) -- Case study 6: applications of cost escalation and exchange-rate volatility modelling methods -- Conclusion.

Additional Edition: Print version: Raydugin, Yuri G. Risk-Based Project Decisions in Situations of High Complexity and Deep Uncertainty Cham : Springer International Publishing AG,c2024 ISBN 9783031569876

Language: English

DOI: 10.1007/978-3-031-56988-3