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Simulation Modeling Using @RISK: Updated for Version 4 1st Edition

Wayne L. Winston

  • Published
  • Previous Editions 1996
  • 230 Pages

Overview

With its understandable explanations of Monte Carlo and step-by-step instructions for Microsoft Excel, Lotus, and @Risk software, this text/software package offers both the instruction and the practice students need to begin solving complex business problems. It is designed for use as the primary learning tool in a short business simulation course (for advanced undergraduate and MBA students), or as a supplement to courses in investments, corporate finance, management science, marketing strategy, operations management, and actuarial science.

Wayne L. Winston, Indiana University, Kelley School of Business (Emeritus)

Wayne L. Winston is Professor Emeritus of Decision Sciences at the Kelley School of Business at Indiana University and is now a Professor of Decision and Information Sciences at the Bauer College at the University of Houston. He has won more than 45 teaching awards and is a six-time recipient of the school-wide MBA award. His current interest focuses on showing how to use spreadsheet models to solve business problems in all disciplines, particularly in finance, sports, and marketing. In addition to publishing more than 20 articles in leading journals, Dr. Winston has written such successful textbooks as OPERATIONS RESEARCH: APPLICATIONS AND ALGORITHMS; MATHEMATICAL PROGRAMMING: APPLICATIONS AND ALGORITHMS; SIMULATION MODELING WITH @RISK; DATA ANALYSIS FOR MANAGERS; SPREADSHEET MODELING AND APPLICATIONS; MATHLETICS, DATA ANALYSIS AND BUSINESS MODELING WITH EXCEL 2013; MARKETING ANALYTICS; and FINANCIAL MODELS USING SIMULATION AND OPTIMIZATION. Dr. Winston received his B.S. degree in mathematics from MIT and his Ph.D. in operations research from Yale.
PREFACE
1. WHAT IS SIMULATION?
Actual Applications of Simulation / What''s Ahead? / Simulation Models Versus Analytic Models
2. RANDOM NUMBERS -- THE BUILDING BLOCKS OF SIMULATION PROBLEMS
3. USING SPREADSHEETS TO PERFORM SIMULATIONS
Example 3.1: The Newsvendor Problem / Finding a Confidence Interval for Expected Profit / How Many Trials Do We Need? / Determination of the Optimal Order Quantity / Using Excel Data Tables to Repeat a Simulation / Performing the Newsvendor Simulation with the Excel Random Number Generator / Problems
4. AN INTRODUCTION TO @RISK
Simulating the Newsvendor Example with @RISK / Explanation of Statistical Results / Conclusions
5. GENERATING NORMAL RANDOM VARIABLES
Simulating Normal Demand with @RISK / Using the Graph Type Icons / Placing Target Values in the Statistics Output / Estimating the Mean and Standard Deviation of a Normal Distribution / Problems
6. APPLICATIONS OF SIMULATION TO CORPORATE FINANCIAL PLANNING
Using the Triangular Distribution to Model Sales / Sensitivity Analysis with Tornado Graphs / Sensitivity Analysis with Scenarios / Alternative Modeling Strategies / Problems
7. SIMULATING A CASH BUDGET
Example 7.1: Cash Budgeting / Problems
8. A SIMULATION APPROACH TO CAPACITY PLANNING
Example 8.1: Wozac Capacity Example / Problems
9. SIMULATION AND BIDDING
Uniform Random Variables / A Bidding Example / Problems
10. DEMING''S FUNNEL EXPERIMENT
Simulating Rule 1 (Don''t Touch That Funnel!) / Simulating Rule 2 / Comparison of Rules 1-4 / Lesson of the Funnel Experiment / Mathematical Explanation of the Funnel Experiment / Problems
11. THE TAGUCHI LOSS FUNCTIONS
Using @RISK to Quantify Quality Loss / Problems
12. THE USE OF SIMULATION ON PROJECT MANAGEMENT
The Widgetco Example / Estimating Probability Distribution of Projected Completion Time / Determining the Probability That an Activity is Critical / The Beta Distribution and Project Management / Problems
13. SIMULATING CRAPS (AND OTHER GAMES)
Example 13.1: Simulating Craps / Confidence Interval for Winning at Craps / Problems
14. USING SIMULATION TO DETERMINE OPTIMAL MAINTENANCE POLICIES
Example 14.1 / Problems
15. USING THE WEIBULL DISTRIBUTION TO MODEL MACHINE LIFE
Example 15.1: Simulating Equipment Replacement Decisions / Problems
16. SIMULATING STOCK PRICES AND OPTIONS
Modeling the Price of a Stock / Estimating the Mean and Standard Deviation of Stock Returns from Historical Data / What Is an Option? / Pricing a Call Option / Example 16.1a: Pricing a European Call Option with @RISK / Analyzing a Portfolio of Investments / Example 16.1b: Simulating Portfolio Return / Problems
17. PRICING PATH-DEPENDENT AND EXOTIC OPTIONS
Example 17.1: Pricing a Path Dependent Option / Problems
18. USING IMMUNIZATION TO MANAGE INTEREST RATE RISK
Duration / Convexity / Immunization Against Interest Rate Risk / Example 18.1: Immunization Using Solver / Better Models for Interest Rate Risk / Problems
19. HEDGING WITH FUTURES
Hedging with Futures: The Basics / Modeling Futures Risk with @RISK / Problems
20. MODELING MARKET SHARE
Example 20.1a: Market Share Simulation / Is Advertising Worthwhile? / Example 20.1b: Advertising Effectiveness / To Coupon or Not to Coupon? / Example 20.1c: Should Coke Give Out Coupons? / Problems
21. GENERATING CORRELATED VARIABLES: DESIGNING A NEW PRODUCT
Example 21.1 / Problems
22. SIMULATING SAMPLING PLANS WITH THE HYPERGEOMETRIC DISTRIBUTION
Example 22.1: Simulating a Sampling Plan / Problems
23. SIMULATING INVENTORY MODELS
Example 23.1: Simulating a Periodic Review Inventory System / Problems
24. SIMULATING A SINGLE-SERVER QUEUING SYSTEM
Example 24.1: Queuing Simulation in @RISK / Estimating the Operating Characteristics of a Queuing System / Problems