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. Success at workCapabilities Environmental factors Opportunities Help from Management More variables?

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 MATLABMATLAB® 7.2

12. Financial modeling

13. Models for accounting and financials Break-even analysis –demo at dssresources.comdssresources.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.comdssresources.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)example Regress and econometric models

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

19. Questions?