Cost Estimation
Methods and Tools
2nd Edition
Published on 22. April 2026
Book
Hardback
416 pages
978-1-394-29804-4 (ISBN)
Description
Provides a practical guide to cost estimation tools, methods, and applications across complex projects
Cost estimation plays a pivotal role in informing investment and resource allocation decisions in government, industrial, and military projects. With increasingly complex systems, high-risk environments, and growing demands for accountability, the ability to develop accurate and justifiable cost models is critical. Cost Estimation: Methods and Tools is an essential, up-to-date introduction to the quantitative techniques that underpin effective cost analysis, ensuring readers gain both theoretical understanding and practical application skills.
This thoroughly revised second edition reflects the latest changes in regulations, directives, and reporting practices. It delivers clear, practical explanations of core cost estimation techniques, including regression analysis, inflation indices, learning curves, cost factors, and wrap rates. New coverage of Agile software methods highlights emerging practices and evolving needs across the field. The book equips readers to address the full spectrum of cost challenges-from research and development to production, deployment, operations and support, and finally disposal.
Combining methodological rigor and applied insight, the second edition of Cost Estimation:
Explains fundamental cost estimation techniques through accessible, step-by-step examples
Incorporates a "deeper dive" into data normalization and regression analysis
Features numerous worked examples and end-of-chapter problem sets to reinforce comprehension
Highlights the use of cost models in risk assessment and uncertainty analysis
Introduces historical context and key terminology to build a solid foundation in cost estimation
Includes updated examples that reflect real-world applications across both defense and industry sectors along with a real-world cost case study.
Written by experienced practitioners and educators, Cost Estimation: Methods and Tools, Second Edition is ideal for graduate students in Operations Research, Industrial Engineering, Systems Engineering, and Cost Estimation, particularly in courses such as Cost Estimating and Analysis or Engineering Economics. It is equally valuable as a reference for professional cost estimators, analysts, and decision makers working in government, defense, and industry.
Cost estimation plays a pivotal role in informing investment and resource allocation decisions in government, industrial, and military projects. With increasingly complex systems, high-risk environments, and growing demands for accountability, the ability to develop accurate and justifiable cost models is critical. Cost Estimation: Methods and Tools is an essential, up-to-date introduction to the quantitative techniques that underpin effective cost analysis, ensuring readers gain both theoretical understanding and practical application skills.
This thoroughly revised second edition reflects the latest changes in regulations, directives, and reporting practices. It delivers clear, practical explanations of core cost estimation techniques, including regression analysis, inflation indices, learning curves, cost factors, and wrap rates. New coverage of Agile software methods highlights emerging practices and evolving needs across the field. The book equips readers to address the full spectrum of cost challenges-from research and development to production, deployment, operations and support, and finally disposal.
Combining methodological rigor and applied insight, the second edition of Cost Estimation:
Explains fundamental cost estimation techniques through accessible, step-by-step examples
Incorporates a "deeper dive" into data normalization and regression analysis
Features numerous worked examples and end-of-chapter problem sets to reinforce comprehension
Highlights the use of cost models in risk assessment and uncertainty analysis
Introduces historical context and key terminology to build a solid foundation in cost estimation
Includes updated examples that reflect real-world applications across both defense and industry sectors along with a real-world cost case study.
Written by experienced practitioners and educators, Cost Estimation: Methods and Tools, Second Edition is ideal for graduate students in Operations Research, Industrial Engineering, Systems Engineering, and Cost Estimation, particularly in courses such as Cost Estimating and Analysis or Engineering Economics. It is equally valuable as a reference for professional cost estimators, analysts, and decision makers working in government, defense, and industry.
More details
Series
Edition
2nd edition
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
ISBN-13
978-1-394-29804-4 (9781394298044)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
05/2026
2nd Edition
Wiley
€107.99
Available for download
E-Book
04/2026
2nd Edition
Wiley
€107.99
Available for download
Previous edition
Book
06/2015
1st Edition
Wiley
€114.50
Article exhausted; check for reprint
Persons
GREGORY K. MISLICK is a Senior Lecturer in the Department of Operations Research at the Naval Postgraduate School (NPS), where he has taught since 2000. His expertise includes life cycle cost estimation, regression analysis, learning curves, data analysis, and optimization. He has advised numerous cost and aviation theses, was an Associate Dean at NPS and is the Program Manager of the Master's degree in Cost Estimating and Analysis curriculum.
DANIEL A. NUSSBAUM is a Professor in the Department of Operations Research at NPS and Chair of the Energy Academic Group. With four decades of experience in financial estimating and analysis for the U.S. Federal government, his research spans cost/benefit analysis, life cycle cost modeling, and financial modeling for strategic decision making.
KAREN R. MISLICK is a Senior Lecturer in the Department of Operations Research at NPS where she teaches cost estimating, scheduling, earned value management, Agile development, and data visualization and storytelling. She previously spent two decades at the Government Accountability Office, leading cost, schedule, and earned value analyses and over 250 audits across major federal programs. She is Level III certified in both cost estimating and financial management.
DANIEL A. NUSSBAUM is a Professor in the Department of Operations Research at NPS and Chair of the Energy Academic Group. With four decades of experience in financial estimating and analysis for the U.S. Federal government, his research spans cost/benefit analysis, life cycle cost modeling, and financial modeling for strategic decision making.
KAREN R. MISLICK is a Senior Lecturer in the Department of Operations Research at NPS where she teaches cost estimating, scheduling, earned value management, Agile development, and data visualization and storytelling. She previously spent two decades at the Government Accountability Office, leading cost, schedule, and earned value analyses and over 250 audits across major federal programs. She is Level III certified in both cost estimating and financial management.
Content
Foreword xiii
About the Authors xvii
Preface xix
Acronyms xxv
Chapter 1 "Looking Back: Reflections on Cost Estimating" 1
Reference 12
Chapter 2 Introduction to Cost Estimating 13
2.1 Introduction 13
2.2 What Is Cost Estimating? 13
2.3 What Are the Characteristics of a Good Cost Estimate? 15
2.4 Importance of Cost Estimating in DoW and in Congress. Why Do We Do Cost Estimating? 17
2.4.1 Importance of Cost Estimating to Congress 18
2.5 An Overview of the DoW Acquisition Process 20
2.6 Acquisition Categories (ACATs) 26
2.7 Cost-Estimating Terminology 29
2.8 Summary 36
References 36
Applications and Questions 37
Chapter 3 Non-DoW Acquisition and the Cost-Estimating Process 39
3.1 Introduction 39
3.2 Who Practices Cost Estimation? 40
3.3 The Government Accountability Office (GAO) and the 12-STEP Process 41
3.4 Cost Estimating in Other Non-DoW Agencies and Organizations 44
3.4.1 The Intelligence Community (IC) 44
3.4.2 National Aeronautics and Space Administration (NASA) 45
3.4.3 The Department of Energy (DOE) 45
3.4.4 Federally Funded Research and Development Centers (FFRDCs) 46
3.4.5 The MITRE Corporation 47
3.4.6 RAND Corporation 47
3.4.7 University Affiliated Research Centers (UARCs) 47
3.4.8 Commercial Firms 48
3.4.9 Cost Estimating Book of Knowledge (CEBoK) 48
3.5 The Cost-Estimating Process 50
3.5.1 Definition and Planning: Knowing the Purpose of the Estimate 51
3.5.2 Definition and Planning: Defining the System 53
3.5.3 Definition and Planning: Establishing the Ground Rules and Assumptions 55
3.5.4 Definition and Planning: Selecting the Estimating Approach 56
3.5.5 Definition and Planning: Putting the Team Together 59
3.6 Data Collection 59
3.7 Formulation of the Estimate 60
3.8 Review and Documentation 60
3.9 Work Breakdown Structure (WBS) 61
3.9.1 Program Work Breakdown Structure 61
3.9.2 Military-Standard (MIL-STD) 881F 64
3.10 Cost Element Structure (CES) 65
3.11 Summary 67
References 67
Applications and Questions 68
Chapter 4 Data Sources 71
4.1 Introduction 71
4.2 Background and Considerations to Data Collection 71
4.2.1 Cost Data 74
4.2.2 Technical Data 74
4.2.3 Programmatic Data 74
4.2.4 Risk Data 75
4.3 Cost Reports and Earned Value Management (EVM) 76
4.3.1 Cost and Software Data Reporting (CSDR) and FlexFiles 76
4.3.2 Contract Performance Report (CPR) 82
4.3.3 EVM in Action 85
4.3.4 Selected Acquisition Reports, DAES, and Other Oversight Tools 88
4.4 Cost Databases 89
4.4.1 Cost Assessment Data Enterprise (CADE) 90
4.4.2 Defense Acquisition Visibility Environment (DAVE) 91
4.4.3 Enterprise Visibility and Management of Operating and Support Costs (EVAMOSC) 91
4.5 Summary 92
References 93
Applications and Questions 93
Chapter 5 Data Normalization 95
5.1 Introduction 95
5.2 The Role of Data Normalization in Cost Estimating 95
5.3 Normalizing for Content 97
5.4 Normalizing for Quantity 99
5.5 Normalizing for Inflation 102
5.6 DoW Appropriations and Background 106
5.7 Constant Year Dollars (CY$) 108
5.8 Base Year Dollars (BY$) 110
5.9 DoW Inflation Indices 112
5.10 Then-Year Dollars (TY$) 117
5.11 Using the Joint Inflation Calculator (JIC) 121
5.12 Expenditure (Outlay) Profile 123
5.13 Escalation 127
5.14 Summary 127
References 128
Applications and Questions 128
Chapter 6 Statistics for Cost Estimators 131
6.1 Introduction 131
6.2 Background to Statistics 131
6.3 Margin of Error 132
6.4 Taking a Sample 136
6.5 Measures of Central Tendency 137
6.6 Dispersion Statistics 139
6.7 Coefficient of Variation 145
6.8 Summary 146
References 147
General Reference 147
Applications and Questions 147
Chapter 7 Single-Variable Linear Regression Analysis 149
7.1 Introduction 149
7.2 Home Buying Example 149
7.3 Regression Background and Terminology 154
7.4 Evaluating a Regression 159
7.5 Standard Error (SE) 159
7.6 Coefficient of Variation (CV) 161
7.7 Analysis of Variance (ANOVA) 162
7.8 Coefficient of Determination (R 2) 164
7.9 F-Statistic and t-Statistics 165
7.10 Regression Hierarchy 168
7.10.1 Hierarchy of Regression 168
7.11 Staying within the Range of Your Data 170
7.12 Treatment of Outliers 171
7.12.1 Handling Outliers with Respect to X (The Independent Variable Data) 172
7.12.2 Handling Outliers with Respect to Y (The Dependent Variable Data) 173
7.13 Residual Analysis 175
7.14 "Deeper Dive: Beyond the Basics" 178
7.15 "Solar Array Panel" Case Study (Continued, Part 2) 181
7.16 Summary 185
Reference 185
Applications and Questions 185
Chapter 8 Multivariable Linear Regression Analysis 187
8.1 Introduction 187
8.2 Background of Multivariable Linear Regression 187
8.3 Home Prices Example 189
8.4 Multicollinearity (MC) 194
8.5 Detecting Multicollinearity (MC), Method #1: Widely Varying Regression Slope Coefficients 195
8.6 Detecting Multicollinearity, Method #2: Correlation Matrix 196
8.7 Multicollinearity Example, MC #1: Home Prices 197
8.8 Determining Statistical Relationships between Independent Variables 199
8.9 Multicollinearity Example, MC #2: Weapon Systems 200
8.10 Conclusions of Multicollinearity 203
8.11 Multivariable Regression Guidelines 204
8.12 Deeper Dive: Beyond the Basics 206
8.13 "Solar Array Panel" Case Study (Continued, Part 3) 206
8.14 Summary 209
Applications and Questions 210
Chapter 9 Intrinsically Linear Regression 213
9.1 Introduction 213
9.2 Background of Intrinsically Linear Regression 213
9.3 The Multiplicative Model 217
9.4 Data Transformation 218
9.5 Interpreting the Regression Results 222
9.6 Deeper Dive: Beyond the Basics 223
9.7 Summary 227
References 228
Applications and Questions 228
Chapter 10 Learning Curves: Unit Theory 231
10.1 Introduction 231
10.2 Learning Curve, Scenario # 1 231
10.3 Cumulative Average Theory Overview 233
10.4 Unit Theory Overview 234
10.5 Unit Theory 238
10.6 Estimating Lot Costs 241
10.7 Fitting a Curve Using Lot Data 245
10.7.1 Lot Midpoint 246
10.7.2 Average Unit Cost (AUC) 248
10.8 Alternative LMP and Lot Cost Calculations 257
10.9 Deeper Dive: Beyond the Basics 258
10.10 Summary 260
References 260
Applications and Questions 261
Chapter 11 Learning Curves: Cumulative Average Theory 263
11.1 Introduction 263
11.2 Background of Cumulative Average Theory (CAT) 263
11.3 Cumulative Average Theory 265
11.4 Estimating Lot Costs 269
11.5 Cumulative Average Theory, Final Example 270
11.6 Unit Theory vs. Cumulative Average Theory 273
11.6.1 Learning Curve Selection 274
11.7 Summary 275
Applications and Questions 276
Chapter 12 Learning Curves: Production Breaks/Lost Learning 277
12.1 Introduction 277
12.2 The Lost Learning Process 278
12.3 Production Break Scenario 278
12.4 The Anderlohr Method 279
12.5 Production Break Example 281
12.6 The Retrograde Method, Example 12.1 (Part 2) 283
12.7 Summary 290
References 291
Applications and Questions 291
Chapter 13 Wrap Rates and Step-Down Functions 293
13.1 Introduction 293
13.2 Wrap Rate Overview 293
13.3 Wrap Rate Components 295
13.3.1 Direct Labor Wage Rate 295
13.3.2 Overhead Rate 296
13.3.3 Other Costs 297
13.4 Wrap Rate, Final Example (Example 13.2) 298
13.5 Summary of Wrap Rates 299
13.6 Introduction to Step-Down Functions 299
13.7 Step-Down Function Theory 300
13.8 Step-Down Function Example 13.1 300
13.9 Summary of Step-Down Functions 303
Reference 303
Applications and Questions 303
Chapter 14 Cost Factors and the Analogy Technique 305
14.1 Introduction 305
14.2 Cost Factors Scenario 305
14.3 Cost Factors 306
14.4 Which Factor to Use? 309
14.5 Cost Factors Handbooks 310
14.6 Unified Facilities Criteria (UFC) 310
14.7 Summary of Cost Factors 311
14.8 Introduction to the Analogy Technique 312
14.9 Background of Analogy 312
14.10 Methodology 313
14.11 Example 14.2, Part 1: The Historical WBS 314
14.12 Example 14.2, Part 2: The New WBS 316
14.13 Summary of the Analogy Technique 319
Reference 320
Applications and Questions 320
Chapter 15 Software Cost Estimation 321
15.1 Introduction 321
15.2 Background on Software Cost Estimation 321
15.3 What Is Software? 322
15.4 The WBS Elements in a Typical Software Cost-Estimating Task 323
15.5 Software Costing Characteristics and Concerns 324
15.6 Measuring Software Size: Source Lines of Code (SLOC) and Function Points (FPs) 325
15.6.1 Source Lines of Code (SLOC) 325
15.6.2 Function Point (FP) Analysis 327
15.7 The Software Cost-Estimating Process 328
15.8 Problems with Software Cost Estimating: Cost Growth 329
15.9 Commercial Software Availability 330
15.9.1 COTS in the Software Environment 331
15.10 Waterfall vs. Agile: A New Paradigm 332
15.11 Post-Development Software Maintenance Costs 334
15.12 Summary 334
References 334
Applications and Questions 334
Chapter 16 Cost-Benefit Analysis and Risk and Uncertainty 337
16.1 Introduction 337
16.2 Cost-Benefit Analysis (CBA) and Net Present Value (NPV) Overview 337
16.3 Time Value of Money 340
16.4 Example 16.1. Net Present Value 344
16.5 Risk and Uncertainty Overview 348
16.6 Considerations for Handling Risk and Uncertainty 350
16.7 How Do the Uncertainties Affect Our Estimate? 352
16.8 Cumulative Cost and Monte Carlo Simulation 354
16.9 Suggested Resources on Risk and Uncertainty Analysis 357
16.10 Summary 357
References 358
Applications and Questions 358
Chapter 17 Epilogue 359
Looking Back 359
Key Takeaways 360
Lessons from History 360
A Growing Profession 361
Looking to the Future: AI and the Changing Landscape 362
How AIIs Already Helping 362
Why Human Expertise Still Matters 362
The Rise of Purpose-Built Tools 362
The Road Ahead 363
Closing Thoughts 363
Answers to Questions 365
Index 377
About the Authors xvii
Preface xix
Acronyms xxv
Chapter 1 "Looking Back: Reflections on Cost Estimating" 1
Reference 12
Chapter 2 Introduction to Cost Estimating 13
2.1 Introduction 13
2.2 What Is Cost Estimating? 13
2.3 What Are the Characteristics of a Good Cost Estimate? 15
2.4 Importance of Cost Estimating in DoW and in Congress. Why Do We Do Cost Estimating? 17
2.4.1 Importance of Cost Estimating to Congress 18
2.5 An Overview of the DoW Acquisition Process 20
2.6 Acquisition Categories (ACATs) 26
2.7 Cost-Estimating Terminology 29
2.8 Summary 36
References 36
Applications and Questions 37
Chapter 3 Non-DoW Acquisition and the Cost-Estimating Process 39
3.1 Introduction 39
3.2 Who Practices Cost Estimation? 40
3.3 The Government Accountability Office (GAO) and the 12-STEP Process 41
3.4 Cost Estimating in Other Non-DoW Agencies and Organizations 44
3.4.1 The Intelligence Community (IC) 44
3.4.2 National Aeronautics and Space Administration (NASA) 45
3.4.3 The Department of Energy (DOE) 45
3.4.4 Federally Funded Research and Development Centers (FFRDCs) 46
3.4.5 The MITRE Corporation 47
3.4.6 RAND Corporation 47
3.4.7 University Affiliated Research Centers (UARCs) 47
3.4.8 Commercial Firms 48
3.4.9 Cost Estimating Book of Knowledge (CEBoK) 48
3.5 The Cost-Estimating Process 50
3.5.1 Definition and Planning: Knowing the Purpose of the Estimate 51
3.5.2 Definition and Planning: Defining the System 53
3.5.3 Definition and Planning: Establishing the Ground Rules and Assumptions 55
3.5.4 Definition and Planning: Selecting the Estimating Approach 56
3.5.5 Definition and Planning: Putting the Team Together 59
3.6 Data Collection 59
3.7 Formulation of the Estimate 60
3.8 Review and Documentation 60
3.9 Work Breakdown Structure (WBS) 61
3.9.1 Program Work Breakdown Structure 61
3.9.2 Military-Standard (MIL-STD) 881F 64
3.10 Cost Element Structure (CES) 65
3.11 Summary 67
References 67
Applications and Questions 68
Chapter 4 Data Sources 71
4.1 Introduction 71
4.2 Background and Considerations to Data Collection 71
4.2.1 Cost Data 74
4.2.2 Technical Data 74
4.2.3 Programmatic Data 74
4.2.4 Risk Data 75
4.3 Cost Reports and Earned Value Management (EVM) 76
4.3.1 Cost and Software Data Reporting (CSDR) and FlexFiles 76
4.3.2 Contract Performance Report (CPR) 82
4.3.3 EVM in Action 85
4.3.4 Selected Acquisition Reports, DAES, and Other Oversight Tools 88
4.4 Cost Databases 89
4.4.1 Cost Assessment Data Enterprise (CADE) 90
4.4.2 Defense Acquisition Visibility Environment (DAVE) 91
4.4.3 Enterprise Visibility and Management of Operating and Support Costs (EVAMOSC) 91
4.5 Summary 92
References 93
Applications and Questions 93
Chapter 5 Data Normalization 95
5.1 Introduction 95
5.2 The Role of Data Normalization in Cost Estimating 95
5.3 Normalizing for Content 97
5.4 Normalizing for Quantity 99
5.5 Normalizing for Inflation 102
5.6 DoW Appropriations and Background 106
5.7 Constant Year Dollars (CY$) 108
5.8 Base Year Dollars (BY$) 110
5.9 DoW Inflation Indices 112
5.10 Then-Year Dollars (TY$) 117
5.11 Using the Joint Inflation Calculator (JIC) 121
5.12 Expenditure (Outlay) Profile 123
5.13 Escalation 127
5.14 Summary 127
References 128
Applications and Questions 128
Chapter 6 Statistics for Cost Estimators 131
6.1 Introduction 131
6.2 Background to Statistics 131
6.3 Margin of Error 132
6.4 Taking a Sample 136
6.5 Measures of Central Tendency 137
6.6 Dispersion Statistics 139
6.7 Coefficient of Variation 145
6.8 Summary 146
References 147
General Reference 147
Applications and Questions 147
Chapter 7 Single-Variable Linear Regression Analysis 149
7.1 Introduction 149
7.2 Home Buying Example 149
7.3 Regression Background and Terminology 154
7.4 Evaluating a Regression 159
7.5 Standard Error (SE) 159
7.6 Coefficient of Variation (CV) 161
7.7 Analysis of Variance (ANOVA) 162
7.8 Coefficient of Determination (R 2) 164
7.9 F-Statistic and t-Statistics 165
7.10 Regression Hierarchy 168
7.10.1 Hierarchy of Regression 168
7.11 Staying within the Range of Your Data 170
7.12 Treatment of Outliers 171
7.12.1 Handling Outliers with Respect to X (The Independent Variable Data) 172
7.12.2 Handling Outliers with Respect to Y (The Dependent Variable Data) 173
7.13 Residual Analysis 175
7.14 "Deeper Dive: Beyond the Basics" 178
7.15 "Solar Array Panel" Case Study (Continued, Part 2) 181
7.16 Summary 185
Reference 185
Applications and Questions 185
Chapter 8 Multivariable Linear Regression Analysis 187
8.1 Introduction 187
8.2 Background of Multivariable Linear Regression 187
8.3 Home Prices Example 189
8.4 Multicollinearity (MC) 194
8.5 Detecting Multicollinearity (MC), Method #1: Widely Varying Regression Slope Coefficients 195
8.6 Detecting Multicollinearity, Method #2: Correlation Matrix 196
8.7 Multicollinearity Example, MC #1: Home Prices 197
8.8 Determining Statistical Relationships between Independent Variables 199
8.9 Multicollinearity Example, MC #2: Weapon Systems 200
8.10 Conclusions of Multicollinearity 203
8.11 Multivariable Regression Guidelines 204
8.12 Deeper Dive: Beyond the Basics 206
8.13 "Solar Array Panel" Case Study (Continued, Part 3) 206
8.14 Summary 209
Applications and Questions 210
Chapter 9 Intrinsically Linear Regression 213
9.1 Introduction 213
9.2 Background of Intrinsically Linear Regression 213
9.3 The Multiplicative Model 217
9.4 Data Transformation 218
9.5 Interpreting the Regression Results 222
9.6 Deeper Dive: Beyond the Basics 223
9.7 Summary 227
References 228
Applications and Questions 228
Chapter 10 Learning Curves: Unit Theory 231
10.1 Introduction 231
10.2 Learning Curve, Scenario # 1 231
10.3 Cumulative Average Theory Overview 233
10.4 Unit Theory Overview 234
10.5 Unit Theory 238
10.6 Estimating Lot Costs 241
10.7 Fitting a Curve Using Lot Data 245
10.7.1 Lot Midpoint 246
10.7.2 Average Unit Cost (AUC) 248
10.8 Alternative LMP and Lot Cost Calculations 257
10.9 Deeper Dive: Beyond the Basics 258
10.10 Summary 260
References 260
Applications and Questions 261
Chapter 11 Learning Curves: Cumulative Average Theory 263
11.1 Introduction 263
11.2 Background of Cumulative Average Theory (CAT) 263
11.3 Cumulative Average Theory 265
11.4 Estimating Lot Costs 269
11.5 Cumulative Average Theory, Final Example 270
11.6 Unit Theory vs. Cumulative Average Theory 273
11.6.1 Learning Curve Selection 274
11.7 Summary 275
Applications and Questions 276
Chapter 12 Learning Curves: Production Breaks/Lost Learning 277
12.1 Introduction 277
12.2 The Lost Learning Process 278
12.3 Production Break Scenario 278
12.4 The Anderlohr Method 279
12.5 Production Break Example 281
12.6 The Retrograde Method, Example 12.1 (Part 2) 283
12.7 Summary 290
References 291
Applications and Questions 291
Chapter 13 Wrap Rates and Step-Down Functions 293
13.1 Introduction 293
13.2 Wrap Rate Overview 293
13.3 Wrap Rate Components 295
13.3.1 Direct Labor Wage Rate 295
13.3.2 Overhead Rate 296
13.3.3 Other Costs 297
13.4 Wrap Rate, Final Example (Example 13.2) 298
13.5 Summary of Wrap Rates 299
13.6 Introduction to Step-Down Functions 299
13.7 Step-Down Function Theory 300
13.8 Step-Down Function Example 13.1 300
13.9 Summary of Step-Down Functions 303
Reference 303
Applications and Questions 303
Chapter 14 Cost Factors and the Analogy Technique 305
14.1 Introduction 305
14.2 Cost Factors Scenario 305
14.3 Cost Factors 306
14.4 Which Factor to Use? 309
14.5 Cost Factors Handbooks 310
14.6 Unified Facilities Criteria (UFC) 310
14.7 Summary of Cost Factors 311
14.8 Introduction to the Analogy Technique 312
14.9 Background of Analogy 312
14.10 Methodology 313
14.11 Example 14.2, Part 1: The Historical WBS 314
14.12 Example 14.2, Part 2: The New WBS 316
14.13 Summary of the Analogy Technique 319
Reference 320
Applications and Questions 320
Chapter 15 Software Cost Estimation 321
15.1 Introduction 321
15.2 Background on Software Cost Estimation 321
15.3 What Is Software? 322
15.4 The WBS Elements in a Typical Software Cost-Estimating Task 323
15.5 Software Costing Characteristics and Concerns 324
15.6 Measuring Software Size: Source Lines of Code (SLOC) and Function Points (FPs) 325
15.6.1 Source Lines of Code (SLOC) 325
15.6.2 Function Point (FP) Analysis 327
15.7 The Software Cost-Estimating Process 328
15.8 Problems with Software Cost Estimating: Cost Growth 329
15.9 Commercial Software Availability 330
15.9.1 COTS in the Software Environment 331
15.10 Waterfall vs. Agile: A New Paradigm 332
15.11 Post-Development Software Maintenance Costs 334
15.12 Summary 334
References 334
Applications and Questions 334
Chapter 16 Cost-Benefit Analysis and Risk and Uncertainty 337
16.1 Introduction 337
16.2 Cost-Benefit Analysis (CBA) and Net Present Value (NPV) Overview 337
16.3 Time Value of Money 340
16.4 Example 16.1. Net Present Value 344
16.5 Risk and Uncertainty Overview 348
16.6 Considerations for Handling Risk and Uncertainty 350
16.7 How Do the Uncertainties Affect Our Estimate? 352
16.8 Cumulative Cost and Monte Carlo Simulation 354
16.9 Suggested Resources on Risk and Uncertainty Analysis 357
16.10 Summary 357
References 358
Applications and Questions 358
Chapter 17 Epilogue 359
Looking Back 359
Key Takeaways 360
Lessons from History 360
A Growing Profession 361
Looking to the Future: AI and the Changing Landscape 362
How AIIs Already Helping 362
Why Human Expertise Still Matters 362
The Rise of Purpose-Built Tools 362
The Road Ahead 363
Closing Thoughts 363
Answers to Questions 365
Index 377