CHAPTER 5
Modelling and Analysis 1
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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
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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
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Modelling and Analysis Topics
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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
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
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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
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Dupont Simulates Rail Transportation
System and Avoids Costly Capital Expense
Vignette
 Promodel simulation created representing
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entire transport system
Applied what-if analyses
Visual simulation
Identified varying conditions
Identified bottlenecks
Allowed for downsized fleet without downsizing
deliveries
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
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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
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Treating Certainty, Uncertainty, and
Risk
 Certainty Models
 Uncertainty
 Risk
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
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)
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Unit Price
~
Amount used in advertisement
Income
Units Sold
Profit
Expense
Unit Cost
Fixed Cost
FIGURE 5.1 An Influence Diagram for the Profit Model.
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Analytica Influence Diagram of a Marketing
Problem: The Marketing Model
http://www.youtube.com/watch?v=dSzvuMGJTlk
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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)
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
MSS Modelling in Spreadsheets
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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
http://www.youtube.com/watch?v=z7pjvTwoz8I&feature=related
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Decision Analysis
of Few Alternatives
(Decision Tables and Trees)
Single Goal Situations
 Decision tables
 Decision trees
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
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
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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%
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
View Problem as a Two-Person Game
Payoff Table 5.2
 Decision variables (alternatives)
 Uncontrollable variables (states of economy)
 Result variables (projected yield)
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Table 5.2: Investment Problem
Decision Table Model
States of Nature
Solid
Stagnation Inflation
Alternatives Growth
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Bonds
12%
6%
3%
Stocks
15%
3%
-2%
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
Treating Uncertainty
 Optimistic approach
 Pessimistic approach
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Treating Risk
 Use known probabilities (Table 5.3)
 Risk analysis: compute expected values
 Can be dangerous
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Table 5.3: Decision Under Risk and Its Solution
Solid
Stagnation
Growth
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Inflation
Expected
Value
Alternatives
.5
.3
.2
Bonds
12%
6%
3%
Stocks
15%
3%
-2%
8.0%
CDs
6.5%
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
8.4% *
 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
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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
Table 5.5: Discrete vs. Continuous
Probability Distribution
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Daily
Demand
Discrete
Probability
Continuous
5
6
7
8
9
.1
.15
.3
.25
.2
Normally distributed with
a mean of 7 and a
standard deviation of 1.2
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ