Spreadsheet Modeling and Applications : Essentials of Practical Management Science (with CD-ROM and InfoTrac®)

Knowing spreadsheets can save your job? Absolutely. SPREADSHEET MODELING AND APPLICATIONS: ESSENTIALS OF PRACTICAL MANAGEMENT SCIENCE shows you how with just a little spreadsheet magic you can solve real life business problems. It's relevant, it's simple and it comes with DecisionTools(R) Suite. With DecisionTools(R) Suite you'll get the spreadsheet add-ins to make homework a snap.

Table of Contents:
Part I: INTRODUCTION TO SPREADSHEET MODELING. 1. Introduction to Modeling. Introduction. A Waiting Line Example. Modeling Versus Models. The Seven-Step Modeling Process. A Successful Management Science Application. Why Study Management Science? Software Included in This Book. Conclusion. 2. Introductory Spreadsheet Modeling. Introduction. Basic Spreadsheet Modeling: Concepts and Best Practices. Cost Projections. Breakeven Analysis. Ordering with Quantity Discounts and Demand Uncertainty. Decisions Involving the Time Value of Money. Conclusion. Appendix: Tips for Editing and Documenting Spreadsheets. Part II: DECISION MAKING UNDER CERTAINTY. 3. Introduction to Optimization Modeling. Introduction. Introduction to Optimization. A Two-Variable Model. Sensitivity Analysis. Properties of Linear Models. Infeasibility and Unboundedness. A Product Mix Model. A Multiperiod Production Model. A Comparison of Algebraic and Spreadsheet Models. A Decision Support System. Conclusion. Appendix: Information on Solvers. 4. Linear Programming Models. Introduction. Advertising Models. Static Workforce Scheduling Models. Aggregate Planning Models. Blending Models. Production Process Models. Financial Models. Conclusion. 5. Network Models. Introduction. Transportation Models. Assignment Models. Minimum Cost Network Flow Models. Shortest Path Models. Project Scheduling Models. Conclusion. 6. Linear Optimization Models with Integer Variables. Introduction. Overview of Optimization with Integer Variables. Capital Budgeting Models. Fixed-Cost Models. Set Covering Models and Location/Assignment Models. Conclusion. 7. Nonlinear Optimization Models. Introduction. Basic Ideas of Nonlinear Optimization. Pricing Models. Advertising Response and Selection Models. Facility Location Models. Models for Rating Sports Teams. Portfolio Optimization Models. Conclusion. Part III: DECISION MAKING UNDER UNCERTAINTY. 8. Decision Making Under Uncertainty. Introduction. Elements of a Decision Analysis. The PrecisionTree Add-In. Bayes Rule. Multistage Decision Problems. Incorporating Attitudes Toward Risk. Conclusion. 9. Introduction to Simulation Modeling. Introduction. Real Applications of Simulation. Probability Distributions for Input Variables. Simulation with Built-In Excel Tools. Introduction to @RISK. The Effect of Input Distributions on Results. Conclusion. 10. Simulation Models. Introduction. Operations Models. Financial Models. Marketing Models. Simulating Games of Chance. Conclusion. 11. Queuing Models. Introduction. Elements of Queuing Models. The Exponential Distribution. Important Queueing. Relationships. Analytical Queuing Models. Queuing Simulation Models. Conclusion. 12. Regression and Forecasting Models. Introduction. Overview of Regression Models. Simple Regression Models. Multiple Regression Models. Overview of Time Series Models. Moving Averages Models. Exponential Smoothing Models. Conclusion.

About the Author :
S. Christian Albright received his B.S. degree in mathematics from Stanford in 1968 and his Ph.D. in operations research from Stanford in 1972. Since then, he has been teaching in the Operations and Decision Technologies Department in the Kelley School of Business at Indiana University. He has taught courses in management science, computer simulation, and statistics to all levels of business students: undergraduates, MBAs, and doctoral students. His current interest is in spreadsheet modeling, including development of VBA applications in Excel(R). Dr. Albright has published more than 20 articles in leading operations research journals in the area of applied probability. He has also published a number of successful textbooks, including DATA ANALYSIS AND DECISION MAKING, DATA ANALYSIS FOR MANAGERS, and SPREADSHEET MODELING AND APPLICATIONS. Wayne L. Winston is Professor of Operations and Decision Technologies in the Kelley School of Business at Indiana University, where he has taught since 1975. Wayne received his B.S. degree in Mathematics from MIT and his Ph.D. degree in Operations Research from Yale. He has written the successful textbooks OPERATIONS RESEARCH: APPLICATIONS AND ALGORITHMS, MATHEMATICAL PROGRAMMING: APPLICATIONS AND ALGORITHMS, SIMULATION MODELING WITH @RISK, PRATICAL MANAGEMENT SCIENCE, DATA ANALYSIS FOR MANAGERS, SPREADSHEET MODELING AND APPLICATIONS, AND FINANCIAL MODELS USING SIMULATION AND OPTIMIZATION. Wayne has published over 20 articles in leading journals and has won many teaching awards, including the school-wide MBA award four times. His current interest is in showing how spreadsheet models can be used to solve business problems in all disciplines, particularly in finance and marketing.

Review :
Part I: INTRODUCTION TO SPREADSHEET MODELING. 1. Introduction to Modeling. Introduction. A Waiting Line Example. Modeling Versus Models. The Seven-Step Modeling Process. A Successful Management Science Application. Why Study Management Science? Software Included in This Book. Conclusion. 2. Introductory Spreadsheet Modeling. Introduction. Basic Spreadsheet Modeling: Concepts and Best Practices. Cost Projections. Breakeven Analysis. Ordering with Quantity Discounts and Demand Uncertainty. Decisions Involving the Time Value of Money. Conclusion. Appendix: Tips for Editing and Documenting Spreadsheets. Part II: DECISION MAKING UNDER CERTAINTY. 3. Introduction to Optimization Modeling. Introduction. Introduction to Optimization. A Two-Variable Model. Sensitivity Analysis. Properties of Linear Models. Infeasibility and Unboundedness. A Product Mix Model. A Multiperiod Production Model. A Comparison of Algebraic and Spreadsheet Models. A Decision Support System. Conclusion. Appendix: Information on Solvers. 4. Linear Programming Models. Introduction. Advertising Models. Static Workforce Scheduling Models. Aggregate Planning Models. Blending Models. Production Process Models. Financial Models. Conclusion. 5. Network Models. Introduction. Transportation Models. Assignment Models. Minimum Cost Network Flow Models. Shortest Path Models. Project Scheduling Models. Conclusion. 6. Linear Optimization Models with Integer Variables. Introduction. Overview of Optimization with Integer Variables. Capital Budgeting Models. Fixed-Cost Models. Set Covering Models and Location/Assignment Models. Conclusion. 7. Nonlinear Optimization Models. Introduction. Basic Ideas of Nonlinear Optimization. Pricing Models. Advertising Response and Selection Models. Facility Location Models. Models for Rating Sports Teams. Portfolio Optimization Models. Conclusion. Part III: DECISION MAKING UNDER UNCERTAINTY. 8. Decision Making Under Uncertainty. Introduction. Elements of a Decision Analysis. The PrecisionTree Add-In. Bayes? Rule. Multistage Decision Problems. Incorporating Attitudes Toward Risk. Conclusion. 9. Introduction to Simulation Modeling. Introduction. Real Applications of Simulation. Probability Distributions for Input Variables. Simulation with Built-In Excel Tools. Introduction to @RISK. The Effect of Input Distributions on Results. Conclusion. 10. Simulation Models. Introduction. Operations Models. Financial Models. Marketing Models. Simulating Games of Chance. Conclusion. 11. Queuing Models. Introduction. Elements of Queuing Models. The Exponential Distribution. Important Queueing. Relationships. Analytical Queuing Models. Queuing Simulation Models. Conclusion. 12. Regression and Forecasting Models. Introduction. Overview of Regression Models. Simple Regression Models. Multiple Regression Models. Overview of Time Series Models. Moving Averages Models. Exponential Smoothing Models. Conclusion.