Model Driven DSS

# Model Driven DSS

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## Model Driven DSS

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1. Model Driven DSS Chapter 9

2. What is a Model? • A mathematical representation that relates variables • For solving a decision problem • Convert the decision problem into a model • There can be multiple solutions to a model • Use math techniques solve the model

3. Environmental factors More variables? Help from Management Capabilities Success at work Opportunities A simple model

4. Types of models • Explanatory model • Fitting the data to a model • May be used for forecasting • Contemplative models • To do what-if type analysis • User Interaction centered • Algebraic models • Goal seek and optimization

5. Model driven DSS • Analytical capabilities; Can answer ‘what-if’ scenarios • Can be used for deciding which path to take (Goal seek) • Can be used to determine what inputs will get you the desired output (Solving)

6. Make or Buy Model based DSS? • Buy • Buy and customize • Very rarely develop from scratch

7. Software packages • Statistical modeling • Forecasting software • Spreadsheets • Optimization software • Financial modeling software

8. Some popular statistical software packages

9. Forecasting tools For more forecasting software visit http://morris.wharton.upenn.edu/forecast/software.html

10. Electronic Spreadsheets Known as DSS generators For more products http://www.dssresources.com/spreadsheets/products.html

11. Optimization software MATLAB® 7.2

12. Financial modeling

13. Models for accounting and financials • Break-even analysis • demo at dssresources.com • Cost-benefit analysis • Financial budgeting • Return on investment • Price determination

14. Decision Analysis Models • Muti-attribute utility models • Given a set of alternatives how to choose the best • Consider attributes of alternatives • Try online software at dssresources.com • Analytical Hierarchical Process • Comparing an alternative to another alternative on each attribute • Assign a grade between 1 and 9 to record preferences • Use eigen-values to come up with ranking

15. Diagrams • Decision trees • Uses two types of nodes – Choice and chance nodes • Calculate expected payoffs for each branch in the tree • Influence diagrams • Representation for decision situation • Variables and how they influence one another • Non-cyclical • Types of variables • Decision (controllable) variable (rectangle) • Chance (uncontrollable) variable ( Circle) • Outcome variable (oval) • Does not represent temporal events or actions • Develop an influence diagram for some personal decision

16. Forecasting • Extrapolation – simple average • Moving average • Exponential smoothing (example) • Regress and econometric models

17. Optimization models • What input values will get me the maximal output value? • Constraints may not be violated • Linear programming • Integer programming • Solver example

18. Questions?