Transcript Modelling and Analysis
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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 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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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
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
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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
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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 entire transport system
Applied what-if analyses
Visual simulation
Identified varying conditions
Identified bottlenecks
Allowed for downsized fleet without downsizing deliveries
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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
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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 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Influence Diagrams
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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
~ Amount used in advertisement Unit Price Units Sold Unit Cost Fixed Cost Income Expense Profit 13
FIGURE 5.1 An Influence Diagram for the Profit Model.
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 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) 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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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 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 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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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
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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
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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
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Table 5.2: Investment Problem Decision Table Model
Solid Alternatives Growth States of Nature Stagnation Inflation Bonds 12% 6% 3% Stocks CDs 15% 6.5% 3% 6.5% -2% 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
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Table 5.3: Decision Under Risk and Its Solution
Solid Growth Stagnation Inflation Expected Value Alternatives .5
.3
.2
Bonds Stocks CDs 12% 15% 6.5% 6% 3% 6.5% 3% -2% 6.5% 8.4% * 8.0% 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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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 Bonds 8.4% Stocks CDs 8.0% 6.5% Safety High Liquidity High Low High 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
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5 6 7 8 9
Table 5.5: Discrete vs. Continuous Probability Distribution
Daily Demand Discrete Probability Continuous .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