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Management Science: The Art of Modeling with Spreadsheets, Third Edition

Book Description

Now in its third edition, Management Science helps business professionals gain the essential skills needed to develop real expertise in business modeling. The biggest change in the text is the conversion of software from Crystal Ball to Risk Solver to reflect changes in the field. More coverage of management science topics has been added. Broader coverage of Excel demonstrates how to create models. Additional open-ended case studies that are less structured have also been included along with new exercises. These changes will help business professionals learn how to apply the information in the field.

Table of Contents

  1. Management Science: The Art of Modeling With Spreadsheets, Third Edition
    1. Copyright
    2. Dedication
    3. Brief Contents
    4. Table of Contents
    5. Preface
      1. WHY THIS BOOK?
      2. NEW IN THE THIRD EDITION
      3. TO THE READER
      4. TO THE TEACHER
      5. SOFTWARE ACCOMPANYING THE THIRD EDITION
      6. ACKNOWLEDGMENTS
    6. About The Authors
    7. Chapter 1: Introduction
      1. MODELS AND MODELING
      2. THE ROLE OF SPREADSHEETS
      3. THE REAL WORLD AND THE MODEL WORLD
      4. LESSONS FROM EXPERT AND NOVICE MODELERS
      5. ORGANIZATION OF THE BOOK
      6. SUMMARY
      7. SUGGESTED READINGS
    8. Chapter 2: Modeling in a Problem-Solving Framework
      1. INTRODUCTION
      2. THE PROBLEM-SOLVING PROCESS
      3. INFLUENCE CHARTS
      4. CRAFT SKILLS FOR MODELING
      5. SUMMARY
      6. SUGGESTED READINGS
      7. EXERCISES
    9. Chapter 3: Basic Excel Skills
      1. INTRODUCTION
      2. EXCEL PREREQUISITES
      3. THE EXCEL WINDOW
      4. CONFIGURING EXCEL
      5. MANIPULATING WINDOWS AND SHEETS
      6. NAVIGATION
      7. SELECTING CELLS
      8. ENTERING TEXT AND DATA
      9. EDITING CELLS
      10. FORMATTING
      11. BASIC FORMULAS
      12. BASIC FUNCTIONS
      13. CHARTING
      14. PRINTING
      15. HELP OPTIONS
      16. SUMMARY
      17. SUGGESTED READINGS
    10. Chapter 4: Advanced Excel Skills
      1. INTRODUCTION
      2. KEYBOARD SHORTCUTS
      3. CONTROLS
      4. CELL COMMENTS
      5. NAMING CELLS AND RANGES
      6. ADVANCED FORMULAS AND FUNCTIONS
      7. RECORDING MACROS AND USING VBA
      8. SUMMARY
      9. SUGGESTED READINGS
    11. Chapter 5: Spreadsheet Engineering
      1. INTRODUCTION
      2. DESIGNING A SPREADSHEET
      3. DESIGNING A WORKBOOK
      4. BUILDING A WORKBOOK
      5. TESTING A WORKBOOK
      6. SUMMARY
      7. SUGGESTED READINGS
      8. EXERCISES
    12. Chapter 6: Analysis Using Spreadsheets
      1. INTRODUCTION
      2. BASE-CASE ANALYSIS
      3. WHAT-IF ANALYSIS
      4. BREAKEVEN ANALYSIS
      5. OPTIMIZATION ANALYSIS
      6. SIMULATION AND RISK ANALYSIS
      7. EXERCISES
    13. Chapter 7: Data Analysis for Modeling
      1. INTRODUCTION
      2. FINDING FACTS FROM DATABASES
      3. ANALYZING SAMPLE DATA
      4. ESTIMATING PARAMETERS: POINT ESTIMATES
      5. ESTIMATING PARAMETERS: INTERVAL ESTIMATES
      6. SUMMARY
      7. SUGGESTED READINGS
      8. EXERCISES
    14. Chapter 8: Regression Analysis
      1. INTRODUCTION
      2. A DECISION-MAKING EXAMPLE
      3. EXPLORING DATA: SCATTER PLOTS AND CORRELATION
      4. SIMPLE LINEAR REGRESSION
      5. GOODNESS-OF-FIT
      6. SIMPLE REGRESSION IN THE BPI EXAMPLE
      7. SIMPLE NONLINEAR REGRESSION
      8. MULTIPLE LINEAR REGRESSION
      9. MULTIPLE REGRESSION IN THE BPI EXAMPLE
      10. REGRESSION ASSUMPTIONS
      11. USING THE EXCEL TOOLS TRENDLINE AND LINEST
      12. SUMMARY
      13. SUGGESTED READINGS
      14. EXERCISES
    15. Chapter 9: Short-Term Forecasting
      1. INTRODUCTION
      2. FORECASTING WITH TIME-SERIES MODELS
      3. THE EXPONENTIAL SMOOTHING MODEL
      4. EXPONENTIAL SMOOTHING WITH A TREND
      5. EXPONENTIAL SMOOTHING WITH TREND AND CYCLICAL FACTORS
      6. SUMMARY
      7. SUGGESTED READINGS
      8. EXERCISES
    16. Chapter 10: Nonlinear Optimization
      1. INTRODUCTION
      2. AN OPTIMIZATION EXAMPLE
      3. BUILDING MODELS FOR SOLVER
      4. MODEL CLASSIFICATION AND THE NONLINEAR SOLVER
      5. NONLINEAR PROGRAMMING EXAMPLES
      6. SENSITIVITY ANALYSIS FOR NONLINEAR PROGRAMS
      7. THE PORTFOLIO OPTIMIZATION MODEL
      8. SUMMARY
      9. SUGGESTED READINGS
      10. EXERCISES
    17. Chapter 11: Linear Optimization
      1. INTRODUCTION
      2. ALLOCATION MODELS
      3. COVERING MODELS
      4. BLENDING MODELS
      5. SENSITIVITY ANALYSIS FOR LINEAR PROGRAMS
      6. PATTERNS IN LINEAR PROGRAMMING SOLUTIONS
      7. DATA ENVELOPMENT ANALYSIS
      8. SUMMARY
      9. SUGGESTED READINGS
      10. EXERCISES
      11. APPENDIX 11.1 THE SOLVER SENSITIVITY REPORT
    18. Chapter 12: Optimization of Network Models
      1. INTRODUCTION
      2. THE TRANSPORTATION MODEL
      3. ASSIGNMENT MODEL
      4. THE TRANSSHIPMENT MODEL
      5. A STANDARD FORM FOR NETWORK MODELS
      6. NETWORK MODELS WITH YIELDS
      7. NETWORK MODELS FOR PROCESS TECHNOLOGIES
      8. SUMMARY
      9. EXERCISES
    19. Chapter 13: Integer Optimization
      1. INTRODUCTION
      2. INTEGER VARIABLES AND THE INTEGER SOLVER
      3. BINARY VARIABLES AND BINARY CHOICE MODELS
      4. BINARY VARIABLES AND LOGICAL RELATIONSHIPS
      5. THE FACILITY LOCATION MODEL
      6. SUMMARY
      7. SUGGESTED READINGS
      8. EXERCISES
    20. Chapter 14: Optimization of Non-Smooth Models
      1. INTRODUCTION
      2. FEATURES OF THE EVOLUTIONARY SOLVER
      3. AN ILLUSTRATIVE EXAMPLE: NONLINEAR REGRESSION
      4. THE ADVERTISING BUDGET PROBLEM (REVISITED)
      5. THE CAPITAL BUDGETING PROBLEM (REVISITED)
      6. THE FIXED COST PROBLEM (REVISITED)
      7. THE MACHINE-SEQUENCING PROBLEM
      8. THE TRAVELING SALESPERSON PROBLEM
      9. GROUP ASSIGNMENT
      10. SUMMARY
      11. EXERCISES
    21. Chapter 15: Decision Analysis
      1. INTRODUCTION
      2. PAYOFF TABLES AND DECISION CRITERIA
      3. USING TREES TO MODEL DECISIONS
      4. USING DECISION TREE SOFTWARE
      5. MAXIMIZING EXPECTED UTILITY WITH DECISION TREE
      6. SUMMARY
      7. SUGGESTED READINGS
      8. EXERCISES
    22. Chapter 16: Monte Carlo Simulation
      1. INTRODUCTION
      2. A SIMPLE ILLUSTRATION
      3. THE SIMULATION PROCESS
      4. CORPORATE VALUATION USING SIMULATION
      5. OPTION PRICING USING SIMULATION
      6. SELECTING UNCERTAIN PARAMETERS
      7. SELECTING PROBABILITY DISTRIBUTIONS
      8. ENSURING PRECISION IN OUTPUTS
      9. INTERPRETING SIMULATION OUTCOMES
      10. WHEN TO SIMULATE AND WHEN NOT TO SIMULATE
      11. SUMMARY
      12. SUGGESTED READINGS
      13. EXERCISES
    23. Chapter 17: Optimization In Simulation
      1. INTRODUCTION
      2. OPTIMIZATION WITH ONE OR TWO DECISION VARIABLES
      3. STOCHASTIC OPTIMIZATION
      4. CHANCE CONSTRAINTS
      5. TWO-STAGE PROBLEMS WITH RECOURSE
      6. SUMMARY
      7. SUGGESTED READINGS
      8. EXERCISES
    24. Modeling Cases
      1. RETIREMENT PLANNING
      2. DRAFT TV COMMERCIALS*
      3. ICEBERGS FOR KUWAIT*
      4. THE RACQUETBALL RACKET*
      5. THE XYZ COMPANY*
      6. MEDICAL SUPPLIES FOR BANJUL*
      7. REID’S RAISIN COMPANY
      8. THE BIG RIG TRUCK RENTAL COMPANY
      9. FLEXIBLE INSURANCE COVERAGE
      10. SNOEY SOFTWARE COMPANY
      11. COX CABLE AND WIRE COMPANY
      12. THE BMW COMPANY
      13. THE ERP DECISION*
      14. NATIONAL LEASING, INC.*
      15. MEGA PHARMA AND MICRO PHARMA*
    25. Appendix: Basic Probability Concepts
      1. INTRODUCTION
      2. PROBABILITY DISTRIBUTIONS
      3. EXAMPLES OF DISCRETE DISTRIBUTIONS
      4. EXAMPLES OF CONTINUOUS DISTRIBUTIONS
      5. EXPECTED VALUES
      6. CUMULATIVE DISTRIBUTION FUNCTIONS
      7. TAIL PROBABILITIES
      8. VARIABILITY
      9. SAMPLING THEORY
    26. INDEX