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.
Part 1 Models And Modeling
- Chapter 1 Introduction To Modeling
- Chapter 2 Spreadsheet Modeling
Part 2 Optimization Modeling
- Chapter 3 Linear Optimization
- Chapter 4 Linear Optimization: Sensitivity Analysis
- Chapter 5 Linear Optimization: Applications
- Chapter 6 Integer Optimization
- Chapter 7 Nonlinear Optimization
Part 3 Probabilistic Models
- Chapter 8 Decision Analysis
- Chapter 9 Monte Carlo Simulation
- Chapter 10 Discrete Event Simulation
etc.