Management Science: The Art of Modeling with Spreadsheets (Anglais) Relié – 22 octobre 2010
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A lot of topics are very amenable to spreadsheets-regression, moving average, exponential smoothing, and optimization. For forecasting methods such as moving average, it is intuitive to use =Average(lag 1, lag 2,....), the same goes for exponential smoothing . A hint on exponential smoothing-you will need to find the parameter alpha (and beta if including trend), you can use Excel's solver to find this by minimizing MSE. The way the authors teach is by doing sensitivity analysis and finding it by trial and error. Excel spits out a regression output for you, clickpath: Data-> Data Analysis-> Regression.
For the rest of the topics, you must use the Risk Solver Platform that comes with the book. There is no CD, there is a code in the front flap that is needed to install a trial version along with a code from the instructor. When my class used Risk Solver Platform, there were a lot of bugs. It is also clunky to use for linear and integer optimization, decision analysis, and Monte Carlo simulations. The explanations and walkthrough written in the book is not very helpful and have lots of gaps. It was frustrating to do the exercises because there are steps needed in solving the problems that were left out of the book. I think it is unfair to give problems that cannot be solved by deduction from the material.
I give this book three stars, and I have not found a better alternative. This book is not suitable for self-study and that is a big part of the deduction in stars. I think you could probably do well in a class if you tough it out and ask a lot of questions from your professor. And if you do have to take a class that uses this book, DO THE PROBLEMS EARLY!! You not only have to solve the problem, you will have to debug because it is very rare that you will get it right the first time. I recall in my class when students would raise questions 24 hours to submission before the midterm and it took me a week to get it all right.
Very little background should be necessary beyond the ability to understand basic equations. Excel does the math for you.
This is one of the few times, where too much information is a bad thing. The first two chapters of the book are probably the best as they explain how decision modeling is an "art form" and something that is more conceptually-based and uses Excel as a way to express itself. Unfortunately, the author quickly forgets this after Chapter 3 and dives headfirst into complex work problems, as if the reader has advanced knowledge of these concepts and a mastery of Excel.
The book seems to be designed for those who have more than a general knowledge of Excel and decision modeling. But as a teaching reference it is a poor choice for students.