(Pearson Education) A textbook emphasizing the fundamental concepts of modeling, featuring a wide array of realistic examples. The CD-ROM contains add-in software, Microsoft Project 200 trialware, TreePlan, Crystal Ball Pro 2000, and GLP, a graphic visualization program, and Excel templates. DLC: Management--Mathematical models.

From the Inside Flap

PREFACE TO THE STUDENT OF MANAGEMENT:

The building of explicit models for analysis and managerial decision making has traditionally been called management science.

Webster's New World Dictionary defines oxymoron as "a figure of speech in which opposite or contradictory ideas or terms are combined." Common examples include sweet sorrow, thunderous silence, jumbo shrimp, sport sedan, bureaucratic efficiency, proprietary standard-you can probably think of many more. And management science?

The same dictionary says that management is "the act, art, or manner of managing, or handling, controlling, directing, etc. " If management is an art, is management science then an oxymoron-a contradiction in terms?

Not to us!

Science is the process of using observation and testing to establish principles and then using these principles to answer questions. Much of business is based on the same approach. Actuaries use statistical models to set insurance rates. Organizations use discounted cash flow models to make decisions on capital expenditures. Sales executives use models based on demand elasticity to determine prices, and managers use investment models to control their personal investment portfolios.

This book is devoted to modeling concepts that may apply to a variety of different management situations. Indeed, many of the models we will study are generic models. Just as the model for discounting cash flows can be used for situations with different time periods, different interest rates, and different cash flows, so can the models and concepts studied in this textbook be used in widely different situations.

As you work your way through this text, you will find that it is so full of specific example models as to appear to be a modeling cookbook. Our goal in writing this book, however, was not to produce good recipes but to produce good cooks. Thus, you should avoid becoming so immersed in the technical details of the models and their Excel representation that you lose track of the general skills that you must develop to be both a good modeler and a good manager. We believe that you will find this book useful to the extent that you focus upon (1) the real-world setting that motivated the creation of the spreadsheet model in the first place, and (2) actively engage in the model building and analysis. Doing one without the other is a common, and mistaken, approach employed by managers because it leads to inadequate comprehension needed for good decision making and learning.

It is possible to do the assignments in this book, and not have the concepts affect you in your career. To avoid this, you must work to personally own these modeling concepts. To do this requires "hands-on" work with Excel modeling. The responsibility for maintaining this focus rests with you. Learning to be both a good modeler and a good manager is far more challenging than learning the mechanics of Excel modeling. We will help by focusing on both management and model, but you can achieve that same focus only with personal effort. TO THE INSTRUCTOR:

As evident in our message above, Excel-based management science has a lot to offer your students. We believe a good textbook coupled with your teaching and enthusiasm can play a critical role in helping to shape the attitudes of tomorrow's managers towards the proper use of quantitative modeling in business. Certainly, spreadsheets have become the near-exclusive tool used by millions of managers in analyzing business problems. They now contain many powerful tools that can be used to analyze more sophisticated models and make better decisions. Given the pervasive use of spreadsheets in management, our task is to focus students upon developing their modeling skills-how to "paint" onto the blank canvas of the worksheet to develop helpful, practical business models-and not upon algorithms or mathematical puzzles.

This textbook is designed for introductory courses in applying the Microsoft Excel spreadsheet to management decision modeling at the upper undergraduate, executive, or MBA level. It introduces students to the key ideas of modeling and management decision making that will be important to them throughout their careers. Addressing the needs of readers interested in either general management or more specialized decision science careers, the book emphasizes

The importance of strong conceptual foundations for all topics, as opposed to just "cookbook" spreadsheet prescriptions Role of spreadsheet modeling in the larger context of management decision making, as opposed to algorithmic techniques.

With this in mind, the sixth edition was revised to make it state-of-the-art in the Excel tools that it teaches and to help you make it more relevant to the management careers your students face. With this in mind, content has further shifted away from solution procedures and other mathematical details toward additional case material. For example, over a dozen new cases have been added across the chapters. We have added an expanded number of new problems at the end of most chapters (both basic skill problems and more advanced application problems). For more advanced classes we include Enrichment Topics on the book's CD-ROM for such things as treatment of degeneracy, branch-and-bound algorithms, Evolutionary Solver advanced features, and conditional probability and Bayes' theorem.

We have adopted a very "hands-on" approach to modeling many different challenges a business may face in the areas of operations, finance, human resources, marketing, and the public sector, to name a few. Students strongly prefer this approach because (1) they learn marketable skills they will use immediately in their careers, and more importantly, (2) they develop valuable modeling habits and insights of longer-term benefit. Many students have called us to say that this was one of the most valuable courses they took in college because it combines tangible applications and modeling philosophy with learning by doing.

The book has a strong focus on models-what they are, how they are created, how they are used, what kinds of insights they provide-and on the critical importance of managerial judgment in utilizing those insights. At the same time, for readers interested in the more in-depth aspects of the subject, there is an unparalleled treatment of optimization and decision analysis techniques.

In addition to revising the pioneering chapter on general modeling with Excel introduced in the previous edition, this edition has added two entirely new chapters. To complement our coverage of Monte Carlo simulation, a new chapter introduces discrete event simulation with Excel and with Extend. A new chapter on implementation that focuses on organizational and management issues surrounding institutionalization of a model has been added, which includes an extensive real-world case for class discussion of this critically important topic. We have also significantly revised two chapters-Project Management has been expanded to include both approaches to project modeling, activities on arcs and activities on nodes via use of the software package, MS Project for Windows and the Monte Carlo simulation chapter has been expanded to include examples on optimization of Excel simulation models via OptQuest.

Continuing the fine tradition of previous editions, the text offers unequaled coverage of optimization and introduces the concept of a "theme case" at the end of each chapter (Ebel Mining) in which a multi-part case's model is made increasingly more sophisticated in building block fashion as more concepts are developed in chapters.

The merging of topics begun with the previous edition has been continued in this edition in recognition of the increasing teaching pressure to streamline topic coverage. This edition combines the previously separate chapters on graphical and sensitivity analysis into a single integrated chapter that introduces SolverTable. Developed at Stanford's GSB five years ago, SolverTable is an add-in that extends Excel's Data Table to perform parametric analysis, including tabulations of Sensitivity Report values, of optimization models.

Finally, this edition increases its coverage of chapter examples, particularly by adding simpler, introductory models to facilitate learning, and maintains its Macintosh-friendly tone of documenting differences for those students modeling in Excel for Macintosh. Chapters are filled with marginal "Tips" to help students avoid pitfalls in Excel while avoiding a break in the conceptual developments in the chapters. In addition, detailed appendices on Solver and the special features of Excel for modeling not normally covered in mechanics-of-spreadsheets courses have been expanded to enable the student to improve their spreadsheet skills and gain a greater appreciation for the modeling capability of Excel.

Spreadsheet applications and examples in Microsoft Excel, including the use of popular spreadsheet add-ins (Solver, Crystal Ball, @Risk, and TreePlan), are integrated throughout as the modeling paradigm. This edition introduces Evolutionary Solver, based upon a genetic search algorithm, to illustrate applications that previously frustrated student attempts to analyze highly nonlinear models that make use of Excel's nonsmooth functions, such as =IF( ).

Considerable attention has been paid to the procedural (almost tutorial) steps to build and analyze decision-making models in Excel. The emphasis again is "hands-on" use of Excel and its add-ins. Updated to include the latest Excel version, Excel 2000, the book provides more than 500 screen "shots" of Excel models. (Most examples are applicable to earlier versions of Excel.) Importantly, the book includes more than ten software application packages students will use long after the course is completed:

A graphic visualization program, GLP, for interactive optimization of linear program

ming models-software included with the book. Premium Edition Solver for Education including infeasibility and nonlinear diagnostic reports to aid students in debugging their optimization models-software included with book. SolverTable add-in software for parametric analysis, including Sensitivity Report values, of optimization models-software included with book. Evolutionary Solver (part of Premium Edition Solver for Education) for performing genetic search on models having highly nonlinear or nonsmooth relationships-software included with book. Professional version (140 day time-limit) of the Monte Carlo simulation add-in, Crystal Ball-software included with the textbook. This version includes the Monte Carlo simulation optimizer OptQuest. Decision analysis add-in software, TreePlan-software included with the book. Excel templates for queuing model calculations-software included with book. The discrete event simulation package Extend LT-software included with textbook. The Manufacturing and Business Process Reengineering simulation library extensions to the simulation package Extend LT-software included with textbook. Microsoft Project 2000 (120 day time-limit)-software included with the book.

The book is divided into four parts: the first deals with general modeling issues, the second with deterministic models, the third with probalistic (stochastic) models; and the fourth with implementation issues for applying models in organizations. This provides a logical organizational framework for the material while allowing for greater emphasis on and enhanced coverage of currently "hot" areas such as genetic optimization, AHP, Monte Carlo simulation, discrete event simulation, multi-objective decision making. And the general use of spreadsheets in modeling. There is more material than can be covered in a typical first course. We believe our organizations of topics allows each instructor the flexibility to tailor their course to different audiences and needs.

The graphic visualization program, GLP, for interactive optimization of linear programming models for the material in Chapters 4 and 6. Premium Edition Solver for Education including infeasibility and nonlinear diagnostic reports for the material in Chapters 3, 4, 5, 6, and 7. SolverTable add-in software for parametric analysis of optimization models for the material in Chapters 4, 5, 6, and 7. Evolutionary Solver (part of Premium Edition Solver for Education) for performing genetic optimization for the material in Chapter 7. Professional (140 day) version of the Monte Carlo simulation add-in, Crystal Ball for the material in Chapter 9. This includes the Monte Carlo simulation optimizer OptQuest (also for Chapter 9). Decision analysis add-in software, TreePlan for the material in Chapter 8. The discrete event simulation package Extend LT for the material in Chapter 10. 120 day Evaluation version of Microsoft Project 2000. Excel templates for queuing models. Excel spreadsheet files for all in-text examples and any relevant data for end-of-chapter problems and cases.