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DSS Modeling

DSS Modeling. Current trends Multidimensional analysis (modeling) A modeling method that involves data analysis in several dimensions Influence diagram

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DSS Modeling

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  1. DSS Modeling • Current trends • Multidimensional analysis (modeling) A modeling method that involves data analysis in several dimensions • Influence diagram A diagram that shows the various types of variables in a problem (e.g., decision, independent, result) and how they are related to each other

  2. Static and Dynamic Models • Static models Models that describe a single interval of a situation • Dynamic models Models whose input data are changed over time (e.g., a five-year profit or loss projection)

  3. MSS Modeling with Spreadsheets • Models can be developed and implemented in a variety of programming languages and systems • The spreadsheet is clearly the most popular end-user modeling tool because it incorporates many powerful financial, statistical, mathematical, and other functions

  4. MSS Modeling with Spreadsheets

  5. MSS Modeling with Spreadsheets • Other important spreadsheet features include what-if analysis, goal seeking, trial and error, optimization , data management, and programmability • Most spreadsheet packages provide fairly seamless integration because they read and write common file structures and easily interface with databases and other tools • Static or dynamic models can be built in a spreadsheet

  6. MSS Modeling with Spreadsheets

  7. Mathematical Programming Optimization

  8. Mathematical Programming Optimization

  9. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking • Multiple goals Refers to a decision situation in which alternatives are evaluated with several, sometimes conflicting, goals • Sensitivity analysis A study of the effect of a change in one or more input variables on a proposed solution

  10. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking • Sensitivity analysis tests relationships such as: • The impact of changes in external (uncontrollable) variables and parameters on the outcome variable(s) • The impact of changes in decision variables on the outcome variable(s) • The effect of uncertainty in estimating external variables • The effects of different dependent interactions among variables • The robustness of decisions under changing conditions

  11. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking • Sensitivity analyses are used for: • Revising models to eliminate too-large sensitivities • Adding details about sensitive variables or scenarios • Obtaining better estimates of sensitive external variables • Altering a real-world system to reduce actual sensitivities • Accepting and using the sensitive (and hence vulnerable) real world, leading to the continuous and close monitoring of actual results • The two types of sensitivity analyses are automatic and trial-and-error

  12. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking • Automatic sensitivity analysis • Automatic sensitivity analysis is performed in standard quantitative model implementations such as LP • Trial-and-error sensitivity analysis • The impact of changes in any variable, or in several variables, can be determined through a simple trial-and-error approach

  13. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking • What-If Analysis A process that involves asking a computer what the effect of changing some of the input data or parameters would be

  14. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking

  15. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking • Goal seeking Asking a computer what values certain variables must have in order to attain desired goals

  16. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking

  17. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking • Computing a break-even point by using goal seeking • Involves determining the value of the decision variables that generate zero profit

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