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Full Description
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.
Contents
PREFACE Actual Applications of Simulation / What''''''''s Ahead? / Simulation Models Versus Analytic ModelsExample 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 / Problems4. AN INTRODUCTION TO @RISKSimulating the Newsvendor Example with @RISK / Explanation of Statistical Results / Conclusions5. GENERATING NORMAL RANDOM VARIABLESSimulating 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 / ProblemsUsing the Triangular Distribution to Model Sales / Sensitivity Analysis with Tornado Graphs / Sensitivity Analysis with Scenarios / Alternative Modeling Strategies / Problems7. SIMULATING A CASH BUDGETExample 7.1: Cash Budgeting / ProblemsExample 8.1: Wozac Capacity Example / Problems9. SIMULATION AND BIDDINGUniform Random Variables / A Bidding Example / Problems10. DEMING''''''''S FUNNEL EXPERIMENTSimulating 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 / Problems11. THE TAGUCHI LOSS FUNCTIONSUsing @RISK to Quantify Quality Loss / ProblemsThe Widgetco Example / Estimating Probability Distribution of Projected Completion Time / Determining the Probability That an Activity is Critical / The Beta Distribution and Project Management / Problems13. SIMULATING CRAPS (AND OTHER GAMES)Example 13.1: Simulating Craps / Confidence Interval for Winning at Craps / ProblemsExample 14.1 / ProblemsExample 15.1: Simulating Equipment Replacement Decisions / Problems16. SIMULATING STOCK PRICES AND OPTIONSModeling 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 / Problems17. PRICING PATH-DEPENDENT AND EXOTIC OPTIONSExample 17.1: Pricing a Path Dependent Option / ProblemsDuration / Convexity / Immunization Against Interest Rate Risk / Example 18.1: Immunization Using Solver / Better Models for Interest Rate Risk / Problems19. HEDGING WITH FUTURESHedging with Futures: The Basics / Modeling Futures Risk with @RISK / Problems20. MODELING MARKET SHAREExample 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? / Problems21. GENERATING CORRELATED VARIABLES: DESIGNING A NEW PRODUCTExample 21.1 / Problems22. SIMULATING SAMPLING PLANS WITH THE HYPERGEOMETRIC DISTRIBUTIONExample 22.1: Simulating a Sampling Plan / Problems23. SIMULATING INVENTORY MODELSExample 23.1: Simulating a Periodic Review Inventory System / Problems24. SIMULATING A SINGLE-SERVER QUEUING SYSTEMExample 24.1: Queuing Simulation in @RISK / Estimating the Operating Characteristics of a Queuing System / Problems
