Advanced Risk Analysis in Engineering Enterprise Systems

Since the emerging discipline of engineering enterprise systems extends traditional systems engineering to develop webs of systems and systems-of-systems, the engineering management and management science communities need new approaches for analyzing and managing risk in engineering enterprise systems. Advanced Risk Analysis in Engineering Enterprise Systems presents innovative methods to address these needs. With a focus on engineering management, the book explains how to represent, model, and measure risk in large-scale, complex systems that are engineered to function in enterprise-wide environments. Along with an analytical framework and computational model, the authors introduce new protocols: the risk co-relationship (RCR) index and the functional dependency network analysis (FDNA) approach. These protocols capture dependency risks and risk co-relationships that may exist in an enterprise. Moving on to extreme and rare event risks, the text discusses how uncertainties in system behavior are intensified in highly networked, globally connected environments. It also describes how the risk of extreme latencies in delivering time-critical data, applications, or services can have catastrophic consequences and explains how to avoid these events. With more and more communication, transportation, and financial systems connected across domains and interfaced with an infinite number of users, information repositories, applications, and services, there has never been a greater need for analyzing risk in engineering enterprise systems. This book gives you advanced methods for tackling risk problems at the enterprise level.

Engineering Risk Management Introduction Objectives and Practices New Challenges Perspectives on Theories of Systems and RiskIntroductionGeneral Systems TheoryRisk and Decision TheoryEngineering Risk Management Foundations of Risk and Decision Theory Introduction Elements of Probability Theory The Value Function Risk and Utility Functions Multiattribute Utility—The Power Additive Utility FunctionApplications to Engineering Risk Management A Concluding Thought A Risk Analysis Framework in Engineering Enterprise SystemsIntroduction Perspectives on Engineering Enterprise SystemsA Framework for Measuring Enterprise Capability RiskA Risk Analysis Algebra Information Needs for Portfolio Risk Analysis The "Cutting Edge" An Index to Measure Risk Co-Relationships Introduction RCR Postulates, Definitions, and TheoryComputing the RCR IndexApplying the RCR Index: A Resource Allocation Example Summary Functional Dependency Network Analysis Introduction FDNA Fundamentals Weakest Link FormulationsFDNA (a, ß) Weakest Link Rule Network Operability and Tolerance Analyses Special TopicsSummary A Decision-Theoretic Algorithm for Ranking Risk Criticality Introduction A Prioritization Algorithm A Model for Measuring Risk in Engineering Enterprise SystemsA Unifying Risk Analytic Framework and Process Summary Random Processes and Queuing Theory Introduction Deterministic ProcessRandom ProcessMarkov ProcessQueuing Theory Basic Queuing ModelsApplications to Engineering Systems Summary Extreme Event Theory Introduction to Extreme and Rare Events Extreme and Rare Events and Engineering Systems Traditional Data AnalysisExtreme Value AnalysisExtreme Event Probability Distributions Limit DistributionsDetermining Domain of Attraction Using Inverse FunctionDetermining Domain of Attraction Using Graphical MethodComplex Systems and Extreme and Rare EventsSummary Prioritization Systems in Highly Networked Environments IntroductionPriority Systems Types of Priority SystemsSummary Risks of Extreme Events in Complex Queuing Systems Introduction Risk of Extreme LatencyConditions for Unbounded LatencyConditions for Bounded LatencyDerived Performance MeasuresOptimization of PS Summary Appendix: Bernoulli Utility and the St. Petersburg Paradox References Index Questions and Exercises appear at the end of each chapter.

C. Ariel Pinto is an Associate Professor in the Department of Engineering Management and Systems Engineering at Old Dominion University, where he co-founded the Emergent Risk Initiative. He earned a Ph.D. in systems engineering from the University of Virginia. Dr. Pinto’s research interests encompass the areas of risk management in engineered systems, including project risk management, risk valuation, risk communication, analysis of extreme-and-rare events, and decision making under uncertainty. Paul R. Garvey is Chief Scientist and a Director for the Center for Acquisition and Systems Analysis, a division of The MITRE Corporation. He earned an A.B. and M.Sc. in pure and applied mathematics from Boston College and Northeastern University, respectively, and a Ph.D. in engineering management from Old Dominion University, where he was awarded the doctoral dissertation medal from the faculty of the College of Engineering. He is the author of the CRC Press books Analytical Methods for Risk Management and Probability Methods for Cost Uncertainty Analysis. Dr. Garvey’s research interests include the theory and application of risk-decision analytic methods to operations research problems in the system sciences domains.