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Full Description
Building upon the technical and organizational groundwork presented in the first edition, Risk Assessment and Decision Making in Business and Industry: A Practical Guide, Second Edition addresses the many aspects of risk/uncertainty (R/U) process implementation.
This comprehensive volume covers four broad aspects of R/U: general concepts, implementation processes, technical aspects, and examples of application. Each section provides practical guidance, combining technical information with advice on how to implement R/U techniques and processes in real-world corporate environments. Following an examination of general principles involved in quantitatively assessing risks and their impact on value, the book describes the two main probabilistic measures of project value - Expected Value of Success (EVS) and the Expected Value for the Portfolio (EVP). The text clearly demonstrates how these metrics are used in individual-project and portfolio management.
By presenting concepts in layman's terms and fully integrating advice related to technical and human characteristics of R/U-related corporate life, this book serves as a complete primer for professionals in any business environment.
What's New in the Second Edition:
Provides guidance for implementation of R/U processes in modern corporations
Offers a crucial breakthrough by defining the terms "risk" and "uncertainty" in ways that can be applied in all aspects of science and business
Explores real-world impediments to process change and implementation
Addresses R/U from a corporate decision-maker's perspective, detailing how to employ R/U to set budgets, manage portfolios, value investments, and execute other critical tasks
Contents
About Risk and uncertainty. Risk assessment: General characteristics and benefits. Risk assessment - A process. The perception from within - why this can be so darn hard to implement. Consistency - the key to a risk process. Communication. Building a consensus model. Building a contributing factor diagram. Education - critical at three levels. Risk assessment technologies. Monte Carlo analysis. Decisions and distributions. Chance of failure. Time-Series analysis and dependence. Risk-Weighted values and sensitivity analysis. What companies really want to do. Risk assessment examples.
