Practical Spreadsheet Risk Modeling for Management

by Dale Lehman, Huybert Groenendaal, Greg Nolder

Risk analytics is developing rapidly, and analysts in the field need material that is theoretically sound as well as practical and straightforward. A one-stop resource for quantitative risk analysis, Practical Spreadsheet Risk Modeling for Management dispenses with the use of complex mathematics, concentrating on how powerful techniques and methods can be used correctly within a spreadsheet-based environment. Highlights This book is written for anyone interested in conducting applied risk analysis in business, engineering, environmental planning, public policy, medicine, or virtually any field amenable to spreadsheet modeling. The authors provide practical case studies along with detailed instruction and illustration of the features of ModelRisk, the most advanced risk modeling spreadsheet software currently available. If you intend to use spreadsheets for decision-supporting analysis, rather than merely as placeholders for numbers, then this is the resource for you.

276 pages, Kindle Edition

First published January 1, 2011

Reviews

[Chris Pavadore · Rating 4 out of 5]

This book is an excellent tool to be used as a guide to set up and run models more complex than any statistical course you may have encountered. The book is based around excel and more particularly the excel add-in ModelRisk (Vose Software), but it is useful even beyond its intent. I used the text for a spreadsheet modeling class in a MBA program with one of the authors of the book, Dale Lehman. The book is useful in modeling, yes, but it is also quite informative on different ways to set up modeling issues you may encounter in school or on the job. Half of the battle of any issue that requires modeling is in figuring out what you want the outcome to be (i.e., what are you ultimately trying to figure out?). The first few chapters deal with different ways to ask questions and set up spreadsheets in excel to be able to begin to answer the questions. Furthermore, it shows in detail ways to map out problems so you can see the outcome you are looking for and, often, put to rest questions that come to mind that are inherently less important than you may think. For me, seeing the problem in what is called an "influence chart" helps a lot in accomplishing the goals.

I will state that the book is geared to work with ModelRisk and, although I have played around with Crystal Ball and other modeling software, the information is a bit difficult to extrapolate and use in other programs. For the bright mind, however, I could see the information being easily transferred to a different modeling program with some tinkering. At any rate, it is a useful tool to set up problems and really take an in-depth look at the issue. If you are using ModelRisk, you should own this book as it will be even that much more useful than the average modeling personnel.