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.

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PDF Pages	PDF Title
1	Cover
3	Other Titles of Interest
5	Copyright
8	Contents
16	Preface
20	1. Risk Management Planning 1.1 Introduction
21	1.2 Risk Identification
22	1.2.1 Risk Recognition 1.2.2 Risk Categorization
24	1.3 Risk Assessment and Analysis 1.3.1 Qualitative Risk Analysis
27	1.3.2 Quantitative Risk Analysis 1.3.2.1 Value Assignment
28	1.3.2.2 Simulation 1.3.2.3 Probabilistic Techniques
29	1.3.2.4 Discrete Events 1.4 Risk Control
30	1.4.1 Risk Reduction
31	1.4.2 Risk Distribution
32	1.4.3 Risk Acceptance
33	1.5 Risk Allocation
34	1.6 Risk Monitoring 1.7 Risk Planning
35	1.8 Summary
36	1.9 Exercises
37	References
38	2. Probabilistic Cost Analysis 2.1 Introduction
39	2.2 Triangular (Three-Point) Estimates
40	2.3 Cost Items (Work Packages 2.4 Work Package Cost Fluctuations 2.5 Price Probability
41	2.6 Triangular Distribution
42	2.7 Requesting Prices
43	2.8 Background 2.9 Probability Evaluation by Triangular Distribution
46	2.10 Probabilities of Project Cost
47	2.11 Cumulative Probability of Project Costs
48	2.12 Finding the Bid Price
51	2.13 Additional Perspectives 2.13.1 The Owner’s Perspective 2.13.2 Guaranteed Maximum Price (GMP
52	2.13.3 Contractor’s Risk Transfer and Subcontractor’s Risk 2.14 Summary
53	2.15 Exercises 2.16 Acknowledgments
54	References
56	3. Contingency Analysis and Allocation 3.1 Introduction: Contingency Cost Management
57	3.2 Contingency Cost Process Overview
58	3.2.1 Contingency Cost Allocation Methodology 3.3 Critical Cost 3.3.1 Identifying Critical Work Packages
60	3.3.2 Handling Noncritical Work Packages 3.3.3 Criticality Rule Examples
61	3.4 Monte Carlo Simulation 3.4.1 Creating the Monte Carlo Slab
62	3.4.2 Generating Random Numbers
63	3.5 Cumulative Probability Profile
64	3.6 Contingency Allocation
65	3.7 Drawdown Curve
68	3.8 Summary 3.9 Exercises
70	References
72	4. Cause-and-Effect Diagrams 4.1 Introduction 4.2 Kaoru Ishikawa
73	4.3 Applications of Cause-and-Effect Diagrams
75	4.4 Types of Cause-and-Effect Diagrams 4.4.1 Drawing the Dispersion Analysis Type of Cause-and-Effect Diagram
78	4.4.2 Drawing the Cause Enumeration Type of Cause-and-Effect Diagram
79	4.4.3 Drawing the Production Process Classification Type of Cause-and-Effect Diagram 4.5 Example Problems
80	4.5.1 Example 1: Dispersion Analysis Type
83	4.5.2 Example 2: Production Process Classification Type
84	4.5.3 Example 3: Cause Enumeration type
89	4.6 Exercises References
90	5. Function Analysis System Technique—Structuring Uncertainty 5.1 Introduction
91	5.2 Background of the Functional Analysis System Technique (FAST
92	5.3 Function Analysis
93	5.3.1 Defining Function
97	5.3.2 Classifying Functions 5.3.2.1 Basic Functions 5.3.2.2 Secondary Functions
98	5.3.2.3 Higher-Order and Lower-Order Functions 5.3.2.4 Assumed Functions
99	5.3.2.5 All-Time Functions 5.3.2.6 Dependent and Independent Functions 5.3.2.7 Critical Path 5.3.2.8 Analyzing Functions
100	5.4 Function Analysis System Technique and Value Methodology 5.4.1 The How–Why Concept
102	5.4.2 When Logic
103	5.4.3 And/Or Logic 5.4.4 FAST Procedure Tree
110	5.5 Conclusion 5.6 Exercises
112	References
114	6. Decision Trees 6.1 Objective 6.2 Introduction 6.2.1 Understanding the Potential and Limits of Decision Trees
115	6.2.1.1 Marketing Problem
117	6.3 Diagram Breakdown
119	6.4 Example Problems 6.4.1 Example 1: Application in Construction Engineering
123	6.4.2 Example 2: Application in Fundraising
130	6.4.3 Example 3: Application in Insurance
136	6.5 Review 6.6 Exercises
138	7. Payoff Matrix 7.1 Introduction 7.2 Method
140	7.3 Example Problem 1: Increasing Monthly Profits
141	7.4 Answer to Example Problem 1 7.4.1 Determining Decision Alternatives 7.4.2 Determining States of Nature
142	7.4.3 Determining Maximin 7.4.4 Determining Maximax
143	7.4.5 Determining Minimax Regret
144	7.4.6 Expected Monetary Value 7.4.7 Expected Opportunity Loss
145	7.4.8 Decision Tree Approach
146	7.5 Example Problem 2: Limiting Decision Costs 7.6 Answer to Example Problem 2 7.6.1 Setting up a Payoff Table
147	7.6.2 Determining Minimax 7.6.3 Determining Minimin
148	7.6.4 Determining Minimax Regret
149	7.6.5 EMV
150	7.7 Example Problem 3: Qualitative Decision Making 7.8 Answer to Example Problem 3 7.8.1 Maximin
151	7.8.2 Maximax 7.8.3 Minimax Regret
152	7.8.4 EMV
153	7.9 Importance of the Payoff Matrix
154	7.10 Exercises
156	8. Bayes’ Theorem 8.1 Introduction 8.2 What is Bayes’ Theorem
157	8.3 Criticism and Advantages 8.4 Derivation of Bayes’ Theorem from First Principles 8.4.1 Events and Sample Space
159	8.4.2 Conditional Probability and Dependence
160	8.4.3 Bayes’ Theorem Derived from Conditional Probability
162	8.5 Logic of Prior/Posterior Probabilities and Tree Diagrams
164	8.6 Examples and Sample Exercises: Applications of Bayes’ Theorem
165	8.6.1 Example 1: Contractor Performance Information
168	8.6.2 Example 2: Defective Parts at a Firearms Manufacturer
172	8.7 Exercises
179	8.8 Conclusion
180	References
182	9. Matrix Analysis 9.1 Introduction 9.2 Criteria
183	9.3 Alternatives/Activities
184	9.4 Weights
185	9.5 Scores
186	9.6 Case Study 1: Construction Site Preparation
188	9.7 Case Study 2: Selection of an Engineering College
190	9.8 Case Study 3: Selecting Solar Panels
191	9.9 Case Study 4: Bid Analysis
193	9.10 Discussion 9.11 Exercises
194	References
196	10. MCDM and the Analytic Hierarchy Process 10.1 Introduction 10.2 The Analytic Hierarchy Process (AHP
197	10.3 Users of AHP
198	10.4 AHP Approaches—The Deductive Approach and the Systems Approach 10.5 Weighting Criteria
199	10.6 Structuring a Problem 10.7 Cross Tabulation
201	10.8 Criteria Ranges 10.8.1 Ranges by Rank
202	10.8.2 Range Between 0 and 1
203	10.8.3 Weighting the Criteria
204	10.9 Comparison Matrix 10.9.1 Pairwise Comparison
206	10.9.2 Geometric Mean
207	10.9.3 Largest Eigenvalue 10.9.4 Consistency Index
208	10.9.5 Random Index and Consistency Ratio
209	10.9.6 Sensitivity Analysis 10.10 AHP Advantage and Critiques
210	10.11 Conclusion 10.12 Exercises
211	References
212	11. Project Planning—The OOPS Game 11.1 Introduction 11.2 Origins 11.3 How to Play the OOPS Game
214	11.3.1 OOPS Game Example
216	11.4 OOPS Game Analysis 11.4.1 Reducing the Scope of Analysis
219	11.4.2 Approach of the Analysis
221	11.5 Results of Analysis 11.5.1 All Scores
222	11.5.2 All-BUILD Strategy 11.5.3 All PLAN Strategies
224	11.6 Analysis of Data
228	11.6.1 Lowest-Mean-Cost Strategy of 6T0 through 6T6 Strategies
229	11.6.2 BUILD When Probability of Success is Greater than 50% (P > 0.5
232	11.6.3 BUILD When the Probability of Success is Greater than or Equal to 50% (P = 0.5
233	11.6.4 Varying the Threshold for Attempting to BUILD
235	11.6.5 Choosing PLAN in the Beginning
236	11.7 Conclusion
237	11.8 Exercises
238	12. Tracking Performance through Control Charts 12.1 Introduction 12.2 The History of Control Charts
239	12.3 Causes of Process Variation
240	12.4 Run and Control Charts, Upper Control Limit and Lower Control Limit, and Six Sigma 12.5 Control Chart Concepts
242	12.6 Variable Control Charts
247	12.7 Attribute Control Charts
248	12.7.1 The p Chart
254	12.7.2 The np Chart
256	12.7.3 The c Chart
257	12.7.4 The u Chart
258	12.8 Control Chart Analysis
261	12.9 Summary, Analysis, and Conclusion
262	12.10 Exercises
265	References
268	13. Economic Order Quantity for Inventory Control 13.1 Introduction 13.2 Costs Associated with Carrying Inventory 13.2.1 Ordering Costs
269	13.2.2 Carrying Costs
271	13.2.3 Total Inventory Cost 13.3 Economic Order Quantity
272	13.3.1 Derivation of EOQ
273	13.3.2 Assumptions of the EOQ Model
274	13.4 EOQ Example 13.5 Reorder Point
275	13.5.1 Case where L × S < EOQ
276	13.5.2 Case where L × S > EOQ 13.6 Sensitivity of EOQ Model
277	13.7 Average Inventory 13.8 Weaknesses of the EOQ Model
278	13.9 Safety Stock
279	13.9.1 Setting the Safety Stock Level
280	13.10 EOQ with Back-Ordering
283	13.10.1 Back-Ordering Example 13.11 Ordering with Quantity Discounts
284	13.11.1 Quantity Discounts Example
285	13.12 Exercises
286	References
288	Appendix 1 Case Study for a Risk Plan: Kapi‘olani Boulevard A1.1 Introduction A1.2 Background
289	A1.3 Risk Management A1.3.1 Roundabout at Kapi‘olani Boulevard and Ward Avenue
291	A1.4 Risk Identification A1.4.1 Risk Identification for the Roundabout at Kapi‘olani Boulevard and Ward Avenue
292	A1.5 Risk Assessment
294	A1.5.1 Risk Assessment for the Roundabout at Kapi‘olani Boulevard and Ward Avenue
295	A1.6 Risk Analysis
296	A1.6.1 Risk Analysis for the Roundabout at Kapi‘olani Boulevard and Ward Avenue
297	A1.7 Risk Mitigation and Planning
298	A1.8 Risk Allocation
300	A1.9 Risk Monitoring and Updating A1.9.1 Risk Monitoring and Updating for the Roundabout at Kapi‘olani Boulevard and Ward Avenue A1.10 General Discussion
301	A1.11 Case-Study Problem
303	References
304	Appendix 2 Example Contingency Fund Evaluation Problem A2.1 Steps to Solution
312	Index
320	About the Author

Published By	Publication Date	Number of Pages
ASCE	2017	321


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Standard Title	Quantitative Risk Management and Decision Making in Construction
Published Code	ASCE
Publication Date	2017
Pages Count	321

ASCE QuantitativeRiskManagementDecision 2017

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