Simulation Modeling Using @RISK by Wayne Winston )
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.
PREFACE 1WHAT 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
Wayne L. Winston is Professor Emeritus of Decision Sciences at the Kelley School of Business at Indiana University and is now Professor of Decision and Information Sciences at the Bauer College at the University of Houston. Dr. Winston has received more than 45 teaching awards and is a six-time recipient of the school-wide M.B.A. 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, including 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 2016; 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.
| SKU | Unavailable |
| ISBN 13 | 9780534380595 |
| ISBN 10 | 053438059X |
| Title | Simulation Modeling Using @RISK |
| Author | Wayne Winston |
| Condition | Unavailable |
| Publisher | Cengage Learning, Inc |
| Year published | 2000-11-03 |
| Number of pages | 230 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |