![]() ![]() > Managerial Decision Modeling with Spreadsheets, by: Barry Render, Ralph M. > Management Science and Decision Technology, by: Jeff Camm and James Evans Evans, University of Cincinnati, and David L. > Introduction to Simulation and Risk Analysis, 2/e, by: James R. > Applied Risk Analysis: Moving Beyond Uncertainty, by: Johnathan Mun, Decisioneering > Spreadsheet Modeling and Decision Analysis, 4/e, by: Cliff Ragsdale, Georgia Southern University ![]() > Statistics, Data Analysis, and Decision Modeling, 2/e, by: James R. Powell, Dartmouth College, and Kenneth R. > The Art of Modeling with Spreadsheets: Management Science, Spreadsheet Engineering, and Modeling Craft, by: Stephen G. > Quantitative Methods for Business, 8/e, by: David R. > Quantitative Business Modeling, 1/e, by: Jack R. > Essentials of Business Statistics, 1/e, by: James R. Risk Solver runs at lightning speed and certainly rivals Crystal Ball and Excel BallĪctual prices may vary from those listed. Risk Solver is an amazing new add-in created by the makers of the famous Excel Solver add-in. Crystal Ball and are the two most popular and are very high quality (which you would expect from the price). The programs listed below work directly with Excel as add-ins. The popularity of Monte Carlo methods have led to a number of superb commercial tools. If you frequently use Excel for modeling, whether for engineering design or financial analysis, I highly suggest one of the Excel add-ins listed below. Although Excel will not always be the best place to run a scientific simulation, the basics are easily explained with just a few simple examples. This article will guide you through the process of performing a Monte Carlo simulation using Microsoft Excel. However, MC continues to gain popularity, and is often used as a benchmark for evaluating other statistical methods. There are certainly other fields that employ MC methods, and there are also times when MC is not practical (for extremely large problems, computer speed is still an issue). In manufacturing, MC methods are used to help allocate tolerances in order to reduce cost. In the science and engineering communities, MC simulation is often used for uncertainty analysis, optimization, and reliability-based design. ![]() Monte Carlo simulation is often used in business for risk and decision analysis, to help make decisions given uncertainties in market trends, fluctuations, and other uncertain factors. ![]()
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