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CHAPTER 5

Modeling and Analysis

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s Major DSS component

s Model base and model management

s 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 editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ

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s Structure of some successful models and

methodologies Decision analysis

Decision trees

Optimization

Heuristic programming Simulation

s New developments in modeling tools / techniques

s Important issues in model base management



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Major Modeling Issues

s Problem identification

s Environmental analysis

s Variable identification

s Forecastings Multiple model use

s Model categories or selection (Table 5.1)

s

Model managements Knowledge-based modeling


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Static and Dynamic Models

s Static Analysis

Single snapshot

s Dynamic Analysis

Dynamic models

Evaluate scenarios that change overtime

Time dependent

Trends and patterns over timeExtend static models


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Treating Certainty,

Uncertainty, and Risk

s Certainty Models

s

s Uncertainty

s

s Risk


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Influence Diagrams

s

Graphical representations of a models Model of a model

s Visual communication

s Some packages create and solve the mathematical model

s 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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8Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ

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Analytica Influence Diagram of a MarketingProblem: The Marketing Model (Figure 5.2a)

(Courtesy of Lumina Decision Systems, Los Altos, CA)


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MSS Modeling in Spreadsheets

s Spreadsheet: most popular end-user modeling tools Powerful functions

s Add-in functions and solvers

s Important for analysis, planning, modeling

s Programmability (macros) (More)


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s What-if analysis

s Goal seeking

s Simple database management

s Seamless integration

s Microsoft Excel

s Lotus 1-2-3

s

s Excel spreadsheet static model example of a simpleloan calculation of monthly payments (Figure5.3)

s

s Excel spreadsheet dynamic model example of asimple loan calculation of monthly payments andeffects of prepayment (Figure 5.4)


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Decision Analysisof Few Alternatives

(Decision Tables and Trees)

Single Goal Situations

s

s Decision tables

s

s Decision trees


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Decision Tables

s Investment example

s One goal: maximize the yield after one year

s Yield depends on the status of the economy

(thestate of nature)

Solid growth

Stagnation

Inflation


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1. If solid growth in the economy, bonds yield12%; 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%

Possible Situations


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View Problem as a Two-Person Game

Payoff Table 5.2s

s Decision variables (alternatives)

s

s Uncontrollable variables (states of economy)

s

s Result variables (projected yield)


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Table 5.2: Investment ProblemDecision Table Model

States of Nature

Solid Stagnation Inflation

Alternatives Growth

Bonds 12% 6% 3%

Stocks 15% 3% -2%

CDs 6.5% 6.5% 6.5%


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Treating Uncertainty

s Optimistic approach

s

s Pessimistic approach


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Treating Risk

s Use known probabilities (Table 5.3)

s

s

Risk analysis: compute expected valuess

s Can be dangerous


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Table 5.3: Decision Under Risk and ItsSolution

Solid Stagnation Inflation Expected

Growth Value

Alternatives .5 .3 .2

Bonds 12% 6% 3% 8.4% *

Stocks 15% 3% -2% 8.0%

CDs 6.5% 6.5% 6.5% 6.5%


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s Decision Trees

s Other methods of treating risk Simulation

Certainty factors

Fuzzy logic

s Multiple goals

s 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


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Optimization via MathematicalProgramming

s Linear programming (LP)

Used extensively in DSS

s Mathematical Programming Family of tools to solve managerial problems

in allocating scarce resources among

various activities to optimize a measurablegoal


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LP AllocationProblem Characteristics

1. Limited quantity of economic resources

2. Resources are used in the production ofproducts or services

3. Two or more ways (solutions, programs)to use the resources

4. Each activity (product or service) yieldsa return in terms of the goal

5. Allocation is usually restricted byconstraints


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LP Allocation Model

s 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 economicalmanner

s Optimal solution: the best, found algorithmically

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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Linear Programming

s Decision variables

s Objective function

s

Objective function coefficientss Constraints

s Capacities

s Input-output (technology) coefficients

Line



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Heuristic Programming

s Cuts the search

s Getssatisfactory solutions more quickly and lessexpensively

s Finds rules to solve complex problems

s Finds good enough feasible solutions to complex problems

s Heuristics can be

Quantitative

Qualitative (in ES)



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When to Use Heuristics

1. Inexact or limited input data

2. Complex reality

3. Reliable, exact algorithm not available

4. Computation time excessive5. To improve the efficiency of optimization

6. To solve complex problems

7. For symbolic processing

8. For making quick decisions



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Simulation

s Technique for conducting experiments with acomputer on a model of a management system

s

s Frequently used DSS tool

s



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Major Characteristics of Simulation

s Imitates reality and capture its richness

s

s Technique for conducting experiments

s

s Descriptive, not normative tool

s

s Often to solve very complex, risky problems



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Simulation Methodology

Model real system and conduct repetitiveexperiments

1. Define problem

2. Construct simulation model 3. Test and validate model

4. Design experiments

5. Conduct experiments

6. Evaluate results 7. Implement solution



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Multidimensional Modeling

s Performed in online analytical processing (OLAP)

s From a spreadsheet and analysis perspective

s 2-D to 3-D to multiple-D

s Multidimensional modeling tools: 16-D +s Multidimensional modeling - OLAP (Figure 5.6)

s Tool can compare, rotate, and slice and dicecorporate data across different management

viewpoints



Vi l I t ti M d li (VIS) d

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Visual Interactive Modeling (VIS) andVisual Interactive Simulation (VIS)

s Visual interactive modeling (VIM) (DSS In Action 5.8)Also called

Visual interactive problem solving

Visual interactive modeling

Visual interactive simulation

s Use computer graphics to present the impact of differentmanagement decisions.

s Can integrate with GIS

s Users perform sensitivity analysis

s Static or a dynamic (animation) systems (Figure 5.7)



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Visual Interactive Simulation (VIS)

s Decision makers interact with the simulatedmodel and watch the results over time

s Visual interactive models and DSS

VIM (Case Application W5.1 on books Website)

Queueing



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Quantitative Software Packages-OLAP

s Preprogrammed models can expedite DSSprogramming time

s Some models are building blocks of other models

Statistical packages

Management science packages

Revenue (yield) management

Other specific DSS applications including spreadsheet add-ins



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Model Base Management

s MBMS: capabilities similar to that of DBMS

s But, there are no comprehensive model base managementpackages

s Each organization uses models somewhat differently

s There are many model classess Within each class there are different solution approaches

s Some MBMS capabilities require expertise and reasoning



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