Jun - 17 - 2019

Management Science - Stephen G. Powell & Kenneth R. Baker

This book is subtitled The Art of Modeling with Spreadsheets, and that sets the focus of the book - the art of using spreadsheet models for decision support. It has always been the dream of management to make informed decisions using more than just intuition. Over the years there have been 'rules of thumb,' 'standard practices,' 'trial and error,' and dozens of other methods of making decisions. Ultimately, business decisions are complex balances of stakeholder interests, employee capabilities, and financial implications. While spreadsheet models do not provide much help in managing shareholder expectations or employee education, they are, when used correctly, powerful tools for analyzing the financial implications of various options confronting the decision maker. This book helps the reader to develop these tools, but make no mistake - this is a textbook intended for a college-level finance class - just the kind of book I love.

Overall, this is a great book. If you are new to modeling or just looking to improve your chops this book will probably be a great investment. Highly recommended. The book has 16 chapters, so instead of going into detail on each chapter I will just touch on some of the highlights.

Chapter 1 covers basic introductions to modeling concepts in general and does a great job if identifying the behavior of "Expert Modelers" and how that behavior differs from "Novice Modelers." This is an amazingly important concept for new modelers because it helps to set them on the right path.

Additionally, this chapter contains a pair of very interesting sections. The first, entitled "Risks of Spreadsheet Use" develops some concerns for how spreadsheet models can add an air of authority and confidence where less of both is warranted. This can lead a decision maker into a false sense of security and a suspension of critical judgment. The second interesting section is entitled "The Real World and the Model World," and provide additional perspective on the risks of spreadsheet use.

Chapter 2 develops a structured methodology for developing spreadsheets in a problem-solving context. I found this section very familiar due to my software development background. Don't let anyone fool you into thinking that developing a large spreadsheet model is not a software development effort. It carries all of the same problems, risks, and issues of any other software development effort. However, there is no widely studied approach for structuring and controlling the development effort so as to reduce complexity and error introduction. This chapter attempts to introduce such a methodology and does a pretty good job of moving in that direction within the limitations of the spreadsheet paradigm.

Chapters 3 and 4 document the basic and advanced Excel skills needed for the rest of the book to make sense.

Chapter 5 is entitled "Spreadsheet Engineering" and builds on the development methodologies introduced in chapter 2.

Chapters 6, 7, and 8 go into detail on data (as in spreadsheet database) analysis and various forms regression analysis such as linear, geometric, and multivariate. Sensitivity analysis is also covered in this chapter.

Chapter 9 covers short-term forecasting based on the regression models introduced earlier as well as time-series, moving average, and exponential smoothing. This chapter also covers trend analysis and cyclical factors such as seasonality.

Chapters 10 and 11 cover nonlinear optimization and linear programming as approaches to optimizing various types of business problems such as profitability optimization or facility location considerations.

Chapter 12 covers network models as tools for process modeling and optimization.

Chapter 13 covers integer programming and the use of Solver with and without linked constraints.

Chapter 14 is a culmination of the chapter thus far and is about decision analysis. This chapter goes into decision trees, the application of probabilities and benchmarking, and additional sensitivity analysis. This is an awesome chapter.

Chapter 15 (my favorite chapter) covers Monte Carlo simulation and analysis and provides some techniques that support this form of analysis and modeling without becoming so complex that the results are difficult to interpret. If you have the software for the book, it includes a limited version of Crystal Ball1 that can be of help in understanding Monte Carlo modeling .

Chapter 16 covers simulation optimization and concludes the book.


Rating: Highly Recommended
Amazon link: Management Science: The Art of Modeling with Spreadsheets

1. I have the 2nd edition of the book which includes a limited version of Crystal Ball. The 3rd edition switched to Risk Solver.

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