Cost Estimation
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.
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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.
Content
Foreword xv
About the Authors xix
Preface xxi
Acronyms xxvii
CHAPTER 1 "Looking Back: Reflections on Cost Estimating" 1
CHAPTER 2 Introduction to Cost Estimating 13
CHAPTER 3 Non-DoW Acquisition and the Cost-Estimating Process 39
CHAPTER 4 Data Sources 71
CHAPTER 5 Data Normalization 95
CHAPTER 6 Statistics for Cost Estimators 131
CHAPTER 7 Single-Variable Linear Regression Analysis 149
CHAPTER 8 Multivariable Linear Regression Analysis 187
CHAPTER 9 Intrinsically Linear Regression 213
CHAPTER 10 Learning Curves: Unit Theory 231
CHAPTER 11 Learning Curves: Cumulative Average Theory 263
CHAPTER 12 Learning Curves: Production Breaks/Lost Learning 277
CHAPTER 13 Wrap Rates and Step-Down Functions 293
CHAPTER 14 Cost Factors and the Analogy Technique 305
CHAPTER 15 Software Cost Estimation 321
CHAPTER 16 Cost-Benefit Analysis and Risk and Uncertainty 337
CHAPTER 17 Epilogue 359
Index 1
