Description
Quantitative Risk Management and Decision Making in Construction introduces valuable techniques for weighing and evaluating alternatives in decision making with a focus on risk analysis for identifying, quantifying, and mitigating risks associated with construction projects.
Singh addresses such topics as probabilistic cost estimating, contingency analysis, cause-effect diagrams, FAST diagrams, and decision trees, and explains the tools available to quantify risks such as payoff matrices, Bayes’ theorem, matrix analysis, and analytical hierarchy process. Finally, Singh shows how the information gained from analysis can be applied to mitigate risks using a risk-analysis card game, by monitoring performance, and by managing inventory.
Intended for graduate and upper-level undergraduate students, each topic is accompanied by numerous examples, drawings, and exercises to illustrate and reinforce these concepts. In addition, the common techniques can be executed by business and construction managers for practical construction risk assessment.
Chapter
Chapter 1: Risk Management Planning
1.2.2 Risk Categorization
1.3 Risk Assessment and Analysis
1.3.1 Qualitative Risk Analysis
1.3.2 Quantitative Risk Analysis
1.3.2.3 Probabilistic Techniques
Chapter 2: Probabilistic Cost Analysis
2.2 Triangular (Three-Point) Estimates
2.3 Cost Items (Work Packages)
2.4 Work Package Cost Fluctuations
2.6 Triangular Distribution
2.9 Probability Evaluation by Triangular Distribution
2.10 Probabilities of Project Cost
2.11 Cumulative Probability of Project Costs
2.12 Finding the Bid Price
2.13 Additional Perspectives
2.13.1 The Owner's Perspective
2.13.2 Guaranteed Maximum Price (GMP)
2.13.3 Contractor's Risk Transfer and Subcontractor's Risk
Chapter 3: Contingency Analysis and Allocation
3.1 Introduction: Contingency Cost Management
3.2 Contingency Cost Process Overview
3.2.1 Contingency Cost Allocation Methodology
3.3.1 Identifying Critical Work Packages
3.3.2 Handling Noncritical Work Packages
3.3.3 Criticality Rule Examples
3.4 Monte Carlo Simulation
3.4.1 Creating the Monte Carlo Slab
3.4.2 Generating Random Numbers
3.5 Cumulative Probability Profile
3.6 Contingency Allocation
Chapter 4: Cause-and-Effect Diagrams
4.3 Applications of Cause-and-Effect Diagrams
4.4 Types of Cause-and-Effect Diagrams
4.4.1 Drawing the Dispersion Analysis Type of Cause-and-Effect Diagram
4.4.2 Drawing the Cause Enumeration Type of Cause-and-Effect Diagram
4.4.3 Drawing the Production Process Classification Type of Cause-and-Effect Diagram
4.5.1 Example 1: Dispersion Analysis Type
4.5.2 Example 2: Production Process Classification Type
4.5.3 Example 3: Cause Enumeration Type
Chapter 5: Function Analysis System Technique-Structuring Uncertainty
5.2 Background of the Functional Analysis System Technique (FAST)
5.3.2 Classifying Functions
5.3.2.2 Secondary Functions
5.3.2.3 Higher-Order and Lower-Order Functions
5.3.2.4 Assumed Functions
5.3.2.5 All-Time Functions
5.3.2.6 Dependent and Independent Functions
5.3.2.8 Analyzing Functions
5.4 Function Analysis System Technique and Value Methodology
5.4.1 The How-Why Concept
5.4.4 FAST Procedure Tree
Chapter 6: Decision Trees
6.2.1 Understanding the Potential and Limits of Decision Trees
6.4.1 Example 1: Application in Construction Engineering
6.4.2 Example 2: Application in Fundraising
6.4.3 Example 3: Application in Insurance
7.3 Example Problem 1: Increasing Monthly Profits
7.4 Answer to Example Problem 1
7.4.1 Determining Decision Alternatives
7.4.2 Determining States of Nature
7.4.3 Determining Maximin
7.4.4 Determining Maximax
7.4.5 Determining Minimax Regret
7.4.6 Expected Monetary Value
7.4.7 Expected Opportunity Loss
7.4.8 Decision Tree Approach
7.5 Example Problem 2: Limiting Decision Costs
7.6 Answer to Example Problem 2
7.6.1 Setting up a Payoff Table
7.6.2 Determining Minimax
7.6.3 Determining Minimin
7.6.4 Determining Minimax Regret
7.7 Example Problem 3: Qualitative Decision Making
7.8 Answer to Example Problem 3
7.9 Importance of the Payoff Matrix
Chapter 8: Bayes' Theorem
8.2 What is Bayes' Theorem?
8.3 Criticism and Advantages
8.4 Derivation of Bayes' Theorem from First Principles
8.4.1 Events and Sample Space
8.4.2 Conditional Probability and Dependence
8.4.3 Bayes' Theorem Derived from Conditional Probability
8.5 Logic of Prior/Posterior Probabilities and Tree Diagrams
8.6 Examples and Sample Exercises: Applications of Bayes' Theorem
8.6.1 Example 1: Contractor Performance Information
8.6.2 Example 2: Defective Parts at a Firearms Manufacturer
Chapter 9: Matrix Analysis
9.3 Alternatives/Activities
9.6 Case Study 1: Construction Site Preparation
9.7 Case Study 2: Selection of an Engineering College
9.8 Case Study 3: Selecting Solar Panels
9.9 Case Study 4: Bid Analysis
Chapter 10: MCDM and the Analytic Hierarchy Process
10.2 The Analytic Hierarchy Process (AHP)
10.4 AHP Approaches-The Deductive Approach and the Systems Approach
10.6 Structuring a Problem
10.8.2 Range Between 0 and 1
10.8.3 Weighting the Criteria
10.9.1 Pairwise Comparison
10.9.3 Largest Eigenvalue
10.9.5 Random Index and Consistency Ratio
10.9.6 Sensitivity Analysis
10.10 AHP Advantage and Critiques
Chapter 11: Project Planning-The OOPS Game
11.3 How to Play the OOPS Game
11.4.1 Reducing the Scope of Analysis
11.4.2 Approach of the Analysis
11.5.2 All-BUILD Strategy
11.5.3 All PLAN Strategies
11.6.1 Lowest-Mean-Cost Strategy of 6T0 through 6T6 Strategies
11.6.2 BUILD When Probability of Success is Greater than 50% (P > 0.5)
11.6.3 BUILD When the Probability of Success is Greater thanor Equal to 50% (P ≥ 0.5)
11.6.4 Varying the Threshold for Attempting to BUILD
11.6.5 Choosing PLAN in the Beginning
Chapter 12: Tracking Performance through Control Charts
12.2 The History of Control Charts
12.3 Causes of Process Variation
12.4 Run and Control Charts, Upper Control Limit and Lower Control Limit, and Six Sigma
12.5 Control Chart Concepts
12.6 Variable Control Charts
12.7 Attribute Control Charts
12.8 Control Chart Analysis
12.9 Summary, Analysis, and Conclusion
Chapter 13: Economic Order Quantity for Inventory Control
13.2 Costs Associated with Carrying Inventory
13.2.3 Total Inventory Cost
13.3 Economic Order Quantity
13.3.2 Assumptions of the EOQ Model
13.5.1 Case where L × S < EOQ
13.5.2 Case where L × S > EOQ
13.6 Sensitivity of EOQ Model
13.8 Weaknesses of the EOQ Model
13.9.1 Setting the Safety Stock Level
13.10 EOQ with Back-Ordering
13.10.1 Back-Ordering Example
13.11 Ordering with Quantity Discounts
13.11.1 Quantity Discounts Example
Appendix 1: Case Study for a Risk Plan: Kapi`olani Boulevard
A1.3.1 Roundabout at Kapi`olani Boulevard and Ward Avenue
A1.4.1 Risk Identification for the Roundabout at Kapi`olani Boulevard and Ward Avenue
A1.5.1 Risk Assessment for the Roundabout at Kapi`olani Boulevard and Ward Avenue
A1.6.1 Risk Analysis for the Roundabout at Kapi`olani Boulevard and Ward Avenue
A1.7 Risk Mitigation and Planning
A1.9 Risk Monitoring and Updating
A1.9.1 Risk Monitoring and Updating for the Roundabout at Kapi`olani Boulevard and Ward Avenue
Appendix 2: Example Contingency Fund Evaluation Problem
Quantitative Risk Management and Decision Making in Construction
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