1. CHAPTER 5
Modeling and Analysis

2. Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Modeling and Analysis
Major DSS component
Model base and model management
CAUTION - Difficult Topic Ahead
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. Modeling and Analysis
Structure of some successful models and methodologies
Decision analysis
Decision trees
Optimization
Heuristic programming
Simulation
New developments in modeling tools / techniques
Important issues in model base management

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

5. Modeling for MSS Key element in most DSS
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 (case application 5.1)
Scott Homes AHP select a supplier model (case application 5.2)
IMERYS optimization clay production model (case application 5.3)

7. Major Modeling Issues Problem identification Environmental analysis
Variable identification
Forecasting
Multiple model use
Model categories or selection (Table 5.1)
Model management
Knowledge-based modeling

8. 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

10. Treating Certainty, Uncertainty, and Risk
Certainty Models
Uncertainty
Risk

11. 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)

13. Analytica Influence Diagram of a Marketing Problem: The Marketing Model (Figure 5.2a) (Courtesy of Lumina Decision Systems, Los Altos, CA)

14. Analytica: Price Submodel (Figure 5
Analytica: Price Submodel (Figure 5.2b) (Courtesy of Lumina Decision Systems, Los Altos, CA)

15. Analytica: Sales Submodel (Figure 5
Analytica: Sales Submodel (Figure 5.2c) (Courtesy of Lumina Decision Systems, Los Altos, CA)

16. MSS Modeling in Spreadsheets
Spreadsheet: most popular end-user modeling tool
Powerful functions
Add-in functions and solvers
Important for analysis, planning, modeling
Programmability (macros)
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17. 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 (Figure 5.4)

18. Decision Analysis of Few Alternatives (Decision Tables and Trees)
Single Goal Situations
Decision tables
Decision trees

19. 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

20. 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%

21. View Problem as a Two-Person Game
Payoff Table 5.2
Decision variables (alternatives)
Uncontrollable variables (states of economy)
Result variables (projected yield)

22. 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% %

23. Treating Uncertainty
Optimistic approach
Pessimistic approach

24. Treating Risk Use known probabilities (Table 5.3)
Risk analysis: compute expected values
Can be dangerous

25. 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%

26. 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)

27. 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

28. 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

29. Optimization via Mathematical Programming
Linear programming (LP)
Used extensively in DSS
Mathematical Programming
Family of tools to solve managerial problems in allocating scarce resources among various activities to optimize a measurable goal

30. LP Allocation Problem Characteristics
1. Limited quantity of economic resources
2. Resources are used in the production of products or services
3. Two or more ways (solutions, programs) to use the resources
4. Each activity (product or service) yields a return in terms of the goal
5. Allocation is usually restricted by constraints

31. LP Allocation Model Rational economic assumptions
1. Returns from allocations can be compared in a common unit
2. Independent returns
3. Total return is the sum of different activities’ returns
4. All data are known with certainty
5. The resources are to be used in the most economical manner
Optimal solution: the best, found algorithmically

32. Linear Programming Decision variables Objective function
Objective function coefficients
Constraints
Capacities
Input-output (technology) coefficients
Line

33. Lindo LP Product-Mix Model DSS in Focus 5.4
<< The Lindo Model: >>
MAX X X2
SUBJECT TO
LABOR) X X2 <=
BUDGET) X X2 <=
MARKET1) X1 >= 100
MARKET2) X2 >= 200
END

34. << Generated Solution Report >>
LP OPTIMUM FOUND AT STEP
OBJECTIVE FUNCTION VALUE 1)
VARIABLE VALUE REDUCED COST X X

35. ROW SLACK OR SURPLUS DUAL PRICES
LABOR)
BUDGET)
MARKET1)
MARKET2)
NO. ITERATIONS=

36. RANGES IN WHICH THE BASIS IS UNCHANGED:
OBJ COEFFICIENT RANGES
VARIABLE CURRENT ALLOWABLE ALLOWABLE
COEF INCREASE DECREASE
X INFINITY
X INFINITY
RIGHTHAND SIDE RANGES
ROW CURRENT ALLOWABLE ALLOWABLE
RHS INCREASE DECREASE
LABOR
BUDGET INFINITY
MARKET INFINITY
MARKET

37. Heuristic Programming
Cuts the search
Gets satisfactory solutions more quickly and less expensively
Finds rules to solve complex problems
Finds good enough feasible solutions to complex problems
Heuristics can be
Quantitative
Qualitative (in ES)

38. When to Use Heuristics 1. Inexact or limited input data
2. Complex reality
3. Reliable, exact algorithm not available
4. Computation time excessive
5. To improve the efficiency of optimization
6. To solve complex problems
7. For symbolic processing
8. For making quick decisions

39. Advantages of Heuristics
1. Simple to understand: easier to implement and explain
2. Help train people to be creative
3. Save formulation time
4. Save programming and storage on computers
5. Save computational time
6. Frequently produce multiple acceptable solutions
7. Possible to develop a solution quality measure
8. Can incorporate intelligent search
9. Can solve very complex models

40. Limitations of Heuristics
1. Cannot guarantee an optimal solution
2. There may be too many exceptions
3. Sequential decisions might not anticipate future consequences
4. Interdependencies of subsystems can influence the whole system
Heuristics successfully applied to vehicle routing

41. Simulation
Technique for conducting experiments with a computer on a model of a management system
Frequently used DSS tool

42. Major Characteristics of Simulation
Imitates reality and capture its richness
Technique for conducting experiments
Descriptive, not normative tool
Often to solve very complex, risky problems

43. Advantages of Simulation
1. Theory is straightforward
2. Time compression
3. Descriptive, not normative
4. MSS builder interfaces with manager to gain intimate knowledge of the problem
5. Model is built from the manager's perspective
6. Manager needs no generalized understanding. Each component represents a real problem component
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44. 7. Wide variation in problem types
8. Can experiment with different variables
9. Allows for real-life problem complexities
10. Easy to obtain many performance measures directly
11. Frequently the only DSS modeling tool for nonstructured problems
12. Monte Carlo add-in spreadsheet packages

45. Limitations of Simulation
1. Cannot guarantee an optimal solution
2. Slow and costly construction process
3. Cannot transfer solutions and inferences to solve other problems
4. So easy to sell to managers, may miss analytical solutions
5. Software is not so user friendly

46. Simulation Methodology
Model real system and conduct repetitive experiments
1. Define problem
2. Construct simulation model
3. Test and validate model
4. Design experiments
5. Conduct experiments
6. Evaluate results
7. Implement solution

47. Simulation Types Probabilistic Simulation Discrete distributions
Continuous distributions
Probabilistic simulation via Monte Carlo technique
Time dependent versus time independent simulation
Simulation software
Visual simulation
Object-oriented simulation

48. Multidimensional Modeling
Performed in online analytical processing (OLAP)
From a spreadsheet and analysis perspective
2-D to 3-D to multiple-D
Multidimensional modeling tools: 16-D +
Multidimensional modeling - OLAP (Figure 5.6)
Tool can compare, rotate, and slice and dice corporate data across different management viewpoints

49. Entire Data Cube from a Query in PowerPlay (Figure 5
Entire Data Cube from a Query in PowerPlay (Figure 5.6a) (Courtesy Cognos Inc.)

50. Graphical Display of the Screen in Figure 5. 6a (Figure 5
Graphical Display of the Screen in Figure 5.6a (Figure 5.6b) (Courtesy Cognos Inc.)

51. Environmental Line of Products by Drilling Down (Figure 5
Environmental Line of Products by Drilling Down (Figure 5.6c) (Courtesy Cognos Inc.)

52. Drilled Deep into the Data: Current Month, Water Purifiers, Only in North America (Figure 5.6d) (Courtesy Cognos Inc.)

53. Visual Spreadsheets
User can visualize models and formulas with influence diagrams
Not cells--symbolic elements

54. Visual interactive modeling (VIM) (DSS In Action 5.8) Also called
Visual Interactive Modeling (VIS) and Visual Interactive Simulation (VIS)
Visual interactive modeling (VIM) (DSS In Action 5.8)
Also called
Visual interactive problem solving
Visual interactive modeling
Visual interactive simulation
Use computer graphics to present the impact of different management decisions.
Can integrate with GIS
Users perform sensitivity analysis
Static or a dynamic (animation) systems (Figure 5.7)

55. Generated Image of Traffic at an Intersection from the Orca Visual Simulation Environment (Figure 5.7) (Courtesy Orca Computer, Inc.)

56. Visual Interactive Simulation (VIS)
Decision makers interact with the simulated model and watch the results over time
Visual interactive models and DSS
VIM (Case Application W5.1 on book’s Web site)
Queueing

57. Quantitative Software Packages-OLAP
Preprogrammed models can expedite DSS programming time
Some models are building blocks of other models
Statistical packages
Management science packages
Revenue (yield) management
Other specific DSS applications
including spreadsheet add-ins

58. Model Base Management MBMS: capabilities similar to that of DBMS
But, there are no comprehensive model base management packages
Each organization uses models somewhat differently
There are many model classes
Within each class there are different solution approaches
Some MBMS capabilities require expertise and reasoning

59. Desirable Capabilities of MBMS
Control
Flexibility
Feedback
Interface
Redundancy reduction
Increased consistency

60. MBMS Design Must Allow the DSS User to:
1. Access and retrieve existing models.
2. Exercise and manipulate existing models
3. Store existing models
4. Maintain existing models
5. Construct new models with reasonable effort

61. Modeling languages
Relational MBMS
Object-oriented model base and its management
Models for database and MIS design and their management

62. SUMMARY Models play a major role in DSS
Models can be static or dynamic
Analysis is under assumed certainty, risk, or uncertainty
Influence diagrams
Spreadsheets
Decision tables and decision trees
Spreadsheet models and results in influence diagrams
Optimization: mathematical programming
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63. Linear programming: economic-based
Heuristic programming
Simulation - more complex situations
Expert Choice
Multidimensional models - OLAP
(More)

64. Quantitative software packages-OLAP (statistical, etc.)
Visual interactive modeling (VIM)
Visual interactive simulation (VIS)
MBMS are like DBMS
AI techniques in MBMS