Probability Methods for Cost Uncertainty Analysis A Systems Engineering Perspective, Second Edition

Probability Methods for Cost Uncertainty Analysis: A Systems Engineering Perspective, Second Edition gives you a thorough grounding in the analytical methods needed for modeling and measuring uncertainty in the cost of engineering systems. This includes the treatment of correlation between the cost of system elements, how to present the analysis to decision-makers, and the use of bivariate probability distributions to capture joint interactions between a system’s cost and schedule. Analytical techniques from probability theory are stressed, along with the Monte Carlo simulation method. Numerous examples and case discussions illustrate the practical application of theoretical concepts. While the original chapters from the first edition remain unchanged, this second edition contains new material focusing on the application of theory to problems encountered in practice. Highlights include the use of GERM to build development and production cost estimating relationships as well as the eSBM, which was developed from a need in the community to offer simplified analytical alternatives to advanced probability-based approaches. The book also lists the major technical works of the late Dr. Stephen A. Book, a mathematician and world-renowned cost analyst whose contributions advanced the theory and practice of cost risk analysis.

Theory and FoundationsUncertainty and the Role of Probability in Cost AnalysisIntroduction and Historical Perspective The Problem Space Presenting Cost as a Probability Distribution Benefits of Cost Uncertainty Analysis Concepts of Probability Theory Introduction Sample Spaces and Events Interpretations and Axioms of Probability Conditional ProbabilityBayes’ Rule Distributions and the Theory of Expectation Random Variables and Probability DistributionsExpectation of a Random Variable Probability Inequalities Useful in Cost Analysis Cost Analysis Perspective Special Distributions for Cost Uncertainty Analysis Trapezoidal Distribution Beta Distribution Normal Distribution Lognormal Distribution Specifying Continuous Probability Distributions Functions of Random Variables and Their Application to Cost Uncertainty Analysis Introduction Linear Combinations of Random Variables Central Limit Theorem and a Cost Perspective Transformations of Random Variables Mellin Transform and Its Application to Cost Functions System Cost Uncertainty Analysis Work Breakdown Structures Analytical Framework Monte Carlo Simulation Modeling Cost and Schedule Uncertainties: An Application of Joint Probability Theory Introduction Joint Probability Models for Cost-Schedule Summary Practical Considerations and ApplicationsElements of Cost Uncertainty Analysis: A Review Introduction Cost as Probability Distribution Monte Carlo Simulation and Method of Moments Distribution Summary Correlation: A Critical Consideration Introduction Correlation Matters Valuing Correlation Summary Building Statistical Cost Estimating ModelsIntroduction Classical Statistical Regression General Error Regression Method Summary Mathematics of Cost Improvement Curves Introduction Learning Curve Theories Production Cost Models Built by Single-Step Regression Summary Enhanced Scenario-Based MethodIntroduction Nonstatistical eSBMStatistical eSBMHistorical Data for eSBMSummary Cost Uncertainty Analysis Practice Points Treating Cost as a Random Variable Risk versus Uncertainty Subjective Probability Assessments Subjectivity in Systems Engineering and Analysis ProblemsCorrelation Capturing Cost-Schedule Uncertainties Distribution Function of a System’s Total Cost Benefits of Cost Uncertainty Analysis Establishing a Cost and Schedule Risk Baseline Determining Cost Reserve Conducting Risk Reduction Trade-Off Analyses Documenting the Cost Uncertainty Analysis Management Perspectives Collected Works of Dr. Stephen A. Book Textbooks Journal PublicationsConference Presentations and Proceedings Appendices Index Exercises and References appear at the end of each chapter.

Paul R. Garvey is Chief Scientist for the Center for Acquisition and Management Sciences at The MITRE Corporation. He has extensive experience in the theory and application of risk-decision analytic methods to operations research problems in the system sciences domains. He is the author of the CRC Press books Advanced Risk Analysis in Engineering Enterprise Systems (with C. Ariel Pinto) and Analytical Methods for Risk Management. He earned an A.B. and M.Sc. in pure and applied mathematics from Boston College and Northeastern University and a Ph.D. in engineering systems risk analysis from Old Dominion University. Stephen A. Book (1941–2012) was a Distinguished Engineer in the Systems Engineering Division at the Aerospace Corporation, chief technical officer at Management Consulting and Research (MCR), and emeritus professor of mathematics at California State University, Dominguez Hills. A world-renowned cost analyst, Dr. Book produced innumerable contributions to the field and made his work accessible to professionals from a variety of academic and technical backgrounds. He earned an A.B., M.A., and Ph.D. in mathematics from Georgetown University, Cornell University, and the University of Oregon, respectively. Raymond P. Covert is the founder of Covarus LLC, a boutique consulting firm specializing in engineering and economic modeling. He has over 30 years of experience in aerospace and defense engineering and is the author of a series of books on cost and schedule estimating. He has published more than 60 technical and tutorial presentations. He earned an Sc.B. in aeronautical and astronautical engineering from the Massachusetts Institute of Technology and an M.S. in electrical engineering from Loyola Marymount University.