1. CHAPTER 5
Modelling and Analysis 1

2. Modelling and Analysis
Major DSS component
Model base and model management
CAUTION
Familiarity with major ideas
Basic concepts and definitions
Tool--influence diagram
Model directly in spreadsheets
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

3. Modelling and Analysis
Structure of some successful models and methodologies
Decision analysis
Decision trees
Optimization
Heuristic programming
Simulation
New developments in modelling tools / techniques
Important issues in model base management

4. Modelling and Analysis Topics
Modelling for MSS
Static and dynamic models
Treating certainty, uncertainty, and risk
Influence diagrams
MSS modelling in spreadsheets
Decision analysis of a few alternatives (decision tables and trees)
Optimization via mathematical programming
Heuristic programming
Simulation
Multidimensional modelling -OLAP
Visual interactive modelling and visual interactive simulation
Quantitative software packages - OLAP
Model base management

5. Modelling for MSS Key element in most DSS Many classes of models
Specialized techniques for each model
Allows for rapid examination of alternative solutions
Multiple models often included in a DSS
Trend toward transparency
Necessity in a model-based DSS
Can lead to massive cost reduction / revenue increases

6. Good Examples of MSS Models
DuPont rail system simulation model (opening vignette)
Procter & Gamble optimization supply chain restructuring models (see presentation pgscredesign.ppt)
Scott Homes AHP select a supplier model
IMERYS optimization clay production model

7. Dupont Simulates Rail Transportation System and Avoids Costly Capital Expense Vignette
Promodel simulation created representing entire transport system
Applied what-if analyses
Visual simulation
Identified varying conditions
Identified bottlenecks
Allowed for downsized fleet without downsizing deliveries

8. Major Modelling Issues
Problem identification
Environmental analysis
Variable identification
Forecasting
Multiple model use
Model categories or selection (Table 5.1)
Model management
Knowledge-based modelling
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

9. Static and Dynamic Models
Static Analysis
Single snapshot
Dynamic Analysis
Dynamic models
Evaluate scenarios that change over time
Time dependent
Trends and patterns over time
Extend static models

11. Treating Certainty, Uncertainty, and Risk
Certainty Models
Uncertainty
Risk
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

12. Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Influence Diagrams
Graphical representations of a model
Model of a model
Visual communication
Some packages create and solve the mathematical model
Framework for expressing MSS model relationships
Rectangle = a decision variable
Circle = uncontrollable or intermediate variable
Oval = result (outcome) variable: intermediate or final
Variables connected with arrows
Example (Figure 5.1)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

13. Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

14. Analytica Influence Diagram of a Marketing Problem: The Marketing Model

15. MSS Modelling in Spreadsheets
Spreadsheet: most popular end-user modelling tool
Powerful functions
Add-in functions and solvers
Important for analysis, planning, modelling
Programmability (macros)
(More)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

16. MSS Modelling in Spreadsheets
What-if analysis
Goal seeking
Simple database management
Seamless integration
Microsoft Excel
Lotus 1-2-3
Excel spreadsheet static model example of a simple loan calculation of monthly payments (Figure 5.3)
Excel spreadsheet dynamic model example of a simple loan calculation of monthly payments and effects of prepayment

17. Decision Analysis of Few Alternatives (Decision Tables and Trees)
Single Goal Situations
Decision tables
Decision trees
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

18. Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Decision Tables
Investment example
One goal: maximize the yield after one year
Yield depends on the status of the economy
(the state of nature)
Solid growth
Stagnation
Inflation
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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Possible Situations
1. If solid growth in the economy, bonds yield 12%; stocks 15%; time deposits 6.5% 2. If stagnation, bonds yield 6%; stocks 3%; time deposits 6.5% 3. If inflation, bonds yield 3%; stocks lose 2%; time deposits yield 6.5%
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

20. View Problem as a Two-Person Game
Payoff Table 5.2
Decision variables (alternatives)
Uncontrollable variables (states of economy)
Result variables (projected yield)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

21. Table 5.2: Investment Problem Decision Table Model
States of Nature
Solid Stagnation Inflation
Alternatives Growth
Bonds 12% 6% 3%
Stocks 15% 3% -2%
CDs 6.5% 6.5% %
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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Treating Uncertainty
Optimistic approach
Pessimistic approach
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

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Treating Risk
Use known probabilities (Table 5.3)
Risk analysis: compute expected values
Can be dangerous
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

24. Table 5.3: Decision Under Risk and Its Solution
Solid Stagnation Inflation Expected
Growth Value
Alternatives
Bonds 12% 6% 3% 8.4% *
Stocks 15% 3% -2% 8.0%
CDs 6.5% 6.5% % 6.5%
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

25. Other methods of treating risk
Decision Trees
Other methods of treating risk
Simulation
Certainty factors
Fuzzy logic
Multiple goals
Yield, safety, and liquidity (Table 5.4)

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Table 5.4: Multiple Goals
Alternatives Yield Safety Liquidity
Bonds 8.4% High High
Stocks 8.0% Low High
CDs 6.5% Very High High
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ

27. Table 5.5: Discrete vs. Continuous Probability Distribution
Daily Discrete Continuous
Demand Probability
Normally distributed with
a mean of 7 and a
standard deviation of 1.2
9 .2
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ