Model Driven DSS Chapter 9 What is a

Model Driven DSS Chapter 9

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

More variables? Environmental factors Help from Management Capabilities Opportunities Success at work

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

Model driven DSS • Analytical capabilities; Can answer ‘whatif’ 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)

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

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

Some popular statistical software packages

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

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

Optimization software MATLAB® 7. 2

Financial modeling

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

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

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

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

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

Questions?