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Management Science: The Art of Modeling with Spreadsheets (Anglais) Relié – 22 octobre 2010

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Now in its third edition, Management Science helps business professionals gain the essential skills needed to develop real expertise in business modeling. The biggest change in the text is the conversion of software from Crystal Ball to Risk Solver to reflect changes in the field. More coverage of management science topics has been added. Broader coverage of Excel demonstrates how to create models. Additional open–ended case studies that are less structured have also been included along with new exercises. These changes will help business professionals learn how to apply the information in the field.

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Amazon.com: HASH(0xa2137a5c) étoiles sur 5 17 commentaires
21 internautes sur 22 ont trouvé ce commentaire utile 
HASH(0xa2117ab0) étoiles sur 5 Not suitable for self study 30 juillet 2011
Par Mariano Apuya Jr - Publié sur Amazon.com
Format: Relié
The title of the class I took with this book was called `Decision Models for Managers'. The book covers basic Excel from organizing spreadsheets to sorting, filtering data, and pivot tables. The introduction to modeling covers how to solve problems. The authors have a method where you structure problems by steps which include diagramming. I had a hard time with the method that they describe, it isn't really elegant. And the exercises for these are not very intuitive to solve. While management science requires high level math like linear algebra and third semester calculus, the math in this book is does not go beyond pre-calculus. The math skills that are needed are statistics, and not the advanced kind. If you understand when the p-value is significant, then there you go. This book is almost all about spreadsheets, but sadly, I did not become an Excel power user by studying the book.

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.
3 internautes sur 3 ont trouvé ce commentaire utile 
HASH(0xa22daec4) étoiles sur 5 Helpful for Excel, but less general use knowledge 26 mai 2013
Par Anonymous Customer - Publié sur Amazon.com
Format: Relié Achat vérifié
As a positive, this did provide a good level beyond "back of the envelope" calculations for Excel, which is commonly available, so that was somewhat useful. However, for general conceptual modeling (ie, visualizing it), it spoke more of modeling as being a fluid art (which I agree with) but didn't offer much in the way beyond a few quips of what experienced modelers do. I think this makes it predominantly task-oriented, which can be taken either as positive or negative depending on your needs. In any case, I think this should be seen as a supplementing skill rather than a standalone source.

Very little background should be necessary beyond the ability to understand basic equations. Excel does the math for you.
3 internautes sur 3 ont trouvé ce commentaire utile 
HASH(0xa21ee474) étoiles sur 5 Very clear! 6 mai 2011
Par Fernando Borges - Publié sur Amazon.com
Format: Relié Achat vérifié
Definitely this very well written book takes you by hands so you can go perform your first steps towards the modeling and optimization art. Of course you need to burn out your brain in order to create your own model and its solutions, but the book give you the basis for the advanced levels in problem solving. If you are intending next step, try explore more about VBA so you can program different engine solvers like ant colony, simulated annealing, bats colony and so on.
4 internautes sur 5 ont trouvé ce commentaire utile 
HASH(0xa22e3bb8) étoiles sur 5 Outstanding Book About Optimization and Risk Analysis 21 septembre 2012
Par Mark Harmon - Publié sur Amazon.com
Format: Relié Achat vérifié
As the author of Step-By-Step Optimization With Excel Solver and a college statistics instructor, I can definitely say that If you do any kind of deep optimization modeling with Excel, here's your book. Stephen Powell and Kenneth Baker have put together, by far, the most in-depth book on how to use Excel to scientifically analyze uncertainty in complicated business decisions. They not only present the theory but also provide clear, step-by-step instructions loaded with with screenshots of Excel in use. The book does a GREAT job at presenting very complicated material in a way that a reasonably proficient Excel user can comprehend without getting a headache. Some of the topics they cover are Monte Carlo simulation, Robust Optimization, Nonlinear Optimization using Excel's built-in Solver, Networking Models, Linear and Integer Programming in Excel, and combining Optimization with Simulation. No matter how much of a whiz-bang, quant jock in Excel you are, this book will take you to a new level of expertise in using the Excel Solver to model uncertainty in order to make better business decisions. I love stuff like this and thought it was fantastic. Those who want to be expert in optimization and risk analysis will really enjoy this book!
1 internautes sur 1 ont trouvé ce commentaire utile 
HASH(0xa23c0e64) étoiles sur 5 Textbook is too verbose and work problems are overly complicated 22 février 2013
Par Geekcheck - Publié sur Amazon.com
Format: Relié Achat vérifié
This textbook was required for a Information Systems class in Decision Modeling and both the students and the professor found it difficult to read. The problem lies in the author's habit of over-explaining simple concepts and deluging the reader with "fluff" information. This is especially true, when it comes to the work problems which usually lead off with information that doesn't relate to the important material needed to perform the calculations.

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.
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