Introduction to Simulation and Risk Analysis (2nd Edition)

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9780130329288: Introduction to Simulation and Risk Analysis (2nd Edition)

Soundly structured and highly practical, this informative guide introduces users to the concepts, methodologies, and applications of simulation in business, using easy-to-apply Microsoft Excel spreadsheets as the principal means to illustrate simulation modeling concepts, computational issues, and analysis of results. Uses spreadsheets throughout to convey quantitative methodologies in a language readers can most easily understand, and allows them to address the elementary concepts of both risk analysis and systems simulation approaches in a common framework. Fully covers all basic concepts of simulation (i.e., the nature of simulation models, systems (time/event driven) simulation, techniques for implementing simple simulation models on Excel spreadsheets, statistical concepts and methods important in simulation analysis, and more. Offers an in-depth study of risk analysis using the Excel add-in Crystal Ball as a practical method for Monte Carlo simulation. Presents a detailed analysis of systems simulation including discussions on the fundamentals of simulating inventory and queueing systems and event-driven simulation. Provides SkillBuilder exercises for practicing and developing spreadsheet and software applications skills, as well as Simulation in Practice cases and numerous examples and illustrations of simulation models throughout. For business administrators, industrial engineers, and related professionals who want to learn about simulation and risk analysis.

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The purpose of this book is to provide an introduction to the concepts, methodologies, and applications of simulation in business. One of the difficulties in teaching and learning simulation is the choice of a software platform. There exists a wide variety of excellent simulation software packages and languages, most of which require significant start-up on the part of the student (and in many cases, the instructor as well). A course can easily deteriorate into a language course, and students can easily lose sight of the basic concepts and principles. Moreover, most packages are generally oriented toward engineering applications and are not suitable for applications of risk analysis, a subject that is becoming increasingly used in practical business applications. We avoid these problems by using spreadsheets as the principal means to illustrate simulation modeling concepts, computational issues, and analysis of results to provide a foundation for learning more powerful simulation software.

Spreadsheets provide the ideal environment with which to introduce simulation to business students. First, spreadsheets are nearly as common as calculators and provide a way to convey quantitative methodologies in a language that business students can most easily understand. Second, spreadsheets allow one to address the elementary concepts of both risk analysis and systems simulation approaches in a common framework. For these reasons, spreadsheets are used in this book as the foundation for conveying basic principles about simulation models and allowing students considerable hands-on experience with minimal frustration. With such a foundation, the advanced student can more easily learn to use commercial simulation software. However, beyond the basics, we use two commercial packages, Crystal Ball for risk analysis and ProcessModel—new to the second edition—for systems simulation to illustrate more complex and robust applications.

This book is aimed at upper-level undergraduate and beginning graduate students in business administration and related disciplines. Microsoft Excel is used exclusively throughout the book, although most models can easily be translated into other spreadsheet formats. The book is logically divided into four parts. Part 1 consists of three chapters that provide the basic concepts of simulation. Chapter 1 describes the nature of simulation models, provides examples of pure Monte-Carlo (repeated sampling) approaches, and introduces the concept of systems (time/event driven) simulation. The simulation process and benefits and limitations of simulation are also discussed. Chapter 2 describes how to implement simple simulation models on Excel spreadsheets. Methods for generating probabilistic outcomes and performing simple Monte-Carlo simulations are also introduced. The appendix to Chapter 2 presents optional material about random number generation techniques. Chapter 3 focuses on probability and statistics in simulation. It provides a comprehensive review of statistical concepts and methods important in simulation analysis, probability distributions commonly used in simulation, issues related to modeling probabilistic inputs, random variates and their generation, and statistical issues of analyzing the output from Monte-Carlo simulations. We assume that students will have had at least a basic course in business statistics.

Part 2 consists of two chapters that focus exclusively on risk analysis. The Excel add-in, Crystal Ball, is introduced as a practical method for Monte-Carlo simulation. It is used throughout the book, and a time-limited version of the full software is included with the book. Chapter 4 provides a comprehensive overview of Crystal Ball as well as an original application developed by Cinergy Corporation, a major Midwest gas and electric utility. Chapter 5 presents a variety of applications in operations management, finance, and marketing. These examples show the variety of uses of Monte-Carlo simulation as well as the flexibility of Crystal Ball in addressing risk.

Part 3 consists of four chapters that deal with systems simulation. In Chapter 6, we describe the fundamentals of simulating inventory and queueing systems. This chapter includes a review of essential analytical models; simulation model development from a process view, activity-scanning view, and event-driven view; spreadsheet implementation; and continuous simulation modeling. Chapter 7 discusses output analysis and experimentation in systems simulation, including issues of transient behavior, statistical methods for comparing different systems, and experimental design in simulation. Chapter 8 provides an introduction to ProcessModel, an easy-to-use, yet powerful software package for systems simulation. Chapter 9 presents additional applications using ProcessModel in operations scheduling, information systems, and medicine.

Part 4 consists of the concluding chapter of the book. Chapter 10 discusses simulation in forecasting and optimization, using companion products to Crystal Ball—CB Predictor and OptQuest—to illustrate the approaches. The chapter also includes elementary introductions to time series forecasting and optimization modeling.

Several features have been designed into this book to improve pedagogy. First, cell formulas and detailed explanations are presented for most spreadsheet models. Second, each chapter has at least one or more "Simulation in Practice" feature that describes real applications of simulation in various businesses. Finally, each chapter has numerous questions and problems that provide a means of review of important concepts and allow students to work with and extend models in the chapter or apply the concepts to new situations.

New in the Second Edition

We have taken considerable time in revising this edition to provide more in-depth coverage of key topics and to make the book appeal to a broader audience. Specific changes include:

  • Expanded presentation of basic Excel skills needed to understand and implement the examples in the text (Chapter 2)
  • "SkillBuilder Exercises" throughout the chapters, designed to provide hands-on exercises for the student to practice and develop spreadsheet and software application skills
  • Full (time restricted) student version of Crystal Ball, a powerful Excel add-in for Monte-Carlo simulation
  • Full (time restricted) student version of ProcessModel simulation software for systems simulation
  • New "Simulation in Practice" cases and revised problems and exercises throughout the book
  • Many new examples and illustrations of simulation models, including casino games to convey elementary simulation modeling and analysis principles and more sophisticated financial models and applications
  • Expanded coverage of basic statistical methods important in simulation (Chapter 3)
  • New and expanded discussion of risk analysis concepts (Chapter 4)
  • New chapter on simulation with ProcessModel (Chapter 8)
  • New chapter on simulation in forecasting and optimization (Chapter 10)

Supplemental Material

This edition includes an Instructor's Solutions Manual.

Acknowledgments

We express our appreciation to the reviewers of the first edition manuscript: Vaidyanathan Jayaraman, University of Southern Mississippi; Ralph Badinelli, Virginia Tech; Arnold Bush, U.S. Naval Academy, Postgraduate School; and Linda Friedman, Baruch College, CUNY In addition, we would like to thank the following individuals for many insightful comments and suggestions that have guided us in this revision:

William C. Giauque, Brigham Young University
William V Harper, Otterbein College
Armann Ingolfsson, University of Alberta
Kellie Keeling, Virginia Tech
Scott Malcolm, University of Delaware
Susan Palocsay, James Madison University

and to Professor Xixi Hong of Xiamen University, China, whose careful reading of the first edition revealed several errors and inconsistencies that we have corrected.

A special note of thanks goes to Eric Wainwright, Mike Nagel, and Terry Hardy of Decisioneering, Inc., for their cooperation in providing the student version of Crystal Ball, comments on the manuscript, and permission to use Decisioneering's models and material from user manuals; and also to Mathew Greenfield and Tony Aust of ProcessModel, Inc., for their support in providing the student version of ProcessModel and assistance in developing the manuscript and examples. Last, but certainly not least, we wish to thank our editor, Tom Tucker, the editorial staff at Prentice Hall, and BookMasters, Inc., for their outstanding support and assistance.

James R. Evans, University of Cincinnati
David L. Olson, University of Nebraska

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