Decision Support and

Business Intelligence

Systems

(9th Ed., Prentice Hall)

Chapter 4:

Modeling and Analysis

Learning Objectives

4-2

Understand the basic concepts of

management support system (MSS) modeling

Describe how MSS models interact with data

and the users

Understand the well-known model classes and

decision making with a few alternatives

Describe how spreadsheets can be used for

MSS modeling and solution

Explain the basic concepts of optimization,

simulation and heuristics; when to use which

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Learning Objectives

4-3

Describe how to structure a linear

programming model

Understand how search methods are used to

solve MSS models

Explain the differences among algorithms,

blind search, and heuristics

Describe how to handle multiple goals

Explain what is meant by sensitivity analysis,

what-if analysis, and goal seeking

Describe the key issues of model management

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Opening Vignette:

“Model-Based Auctions Serve More

Lunches in Chile”

Background: problem situation

Proposed solution

Results

Answer and discuss the case questions

4-4

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Modeling and Analysis Topics

4-5

Modeling for MSS (a critical component)

Static and dynamic models

Treating certainty, uncertainty, and risk

Influence diagrams (in the posted PDF file)

MSS modeling in spreadsheets

Decision analysis of a few alternatives (with

decision tables and decision trees)

Optimization via mathematical programming

Heuristic programming

Simulation

Model base management

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

MSS Modeling

A key element in most MSS

Leads to reduced cost and increased

revenue

DuPont Simulates Rail Transportation System

and Avoids Costly Capital Expenses

Procter & Gamble uses several DSS models

collectively to support strategic decisions

4-6

Locating distribution centers, assignment of DCs to

warehouses/customers, forecasting demand,

scheduling production per product type, etc.

Fiat, Pillowtex (…operational efficiency)…

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Major Modeling Issues

Problem identification and environmental

analysis (information collection)

Variable identification

Forecasting/predicting

4-7

More information leads to better prediction

Multiple models: A MSS can include several

models, each of which represents a

different part of the decision-making

problem

Influence diagrams, cognitive maps

Categories of models >>>

Model management

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Categories of Models

Category

4-8

Objective

Techniques

Optimization of

problems with few

alternatives

Find the best solution from Decision tables,

a small number of

decision trees

alternatives

Optimization via

algorithm

Find the best solution from

a large number of

alternatives using a stepby-step process

Linear and other

mathematical

programming

models

Optimization via

an analytic formula

Find the best solution in

one step using a formula

Some inventory

models

Simulation

Find a good enough

solution by experimenting

with a dynamic model of

the system

Several types of

simulation

Heuristics

Find a good enough

solution using “commonsense” rules

Heuristic

programming and

expert systems

Predictive

and

Predict future

Forecasting, Markov

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

other models

occurrences, what-if

chains, financial, …

Static and Dynamic Models

Static Analysis

Dynamic Analysis

4-9

Single snapshot of the situation

Single interval

Steady state

Dynamic models

Evaluate scenarios that change over time

Time dependent

Represents trends and patterns over time

More realistic: Extends static models

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making:

Treating Certainty, Uncertainty and

Risk

Certainty Models

Uncertainty

Several outcomes for each decision

Probability of each outcome is unknown

Knowledge would lead to less uncertainty

Risk analysis (probabilistic decision making)

4-10

Assume complete knowledge

All potential outcomes are known

May yield optimal solution

Probability of each of several outcomes

occurring

Level of uncertainty => Risk (expected value)

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Certainty, Uncertainty and Risk

4-11

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Influence Diagrams

(Posted on the Course Website)

Graphical representations of a model

“Model of a model”

A tool for visual communication

Some influence diagram 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 are connected with arrows indicates the

direction of influence (relationship)

4-12

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Influence Diagrams:

Relationships

CERTAINTY

Amount in

CDs

Interest

Collected

UNCERTAINTY

Price

Sales

The shape of

the arrow

indicates the

type of

relationship

RANDOM (risk) variable: Place a tilde (~) above the variable’s name

~

Demand

Sales

4-13

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Influence Diagrams: Example

An influence diagram for the profit model

Unit Price

~

Amount used in

Advertisement

Income

Units Sold

Profit

Profit = Income – Expense

Unit Cost

Income = UnitsSold * UnitPrice

UnitsSold = 0.5 * Advertisement Expense

Expenses = UnitsCost * UnitSold + FixedCost

Expenses

Fixed Cost

4-14

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Influence Diagrams: Software

Analytica, Lumina Decision Systems

DecisionPro, Vanguard Software Co.

Integrates influence diagrams and Excel, also supports

Monte Carlo simulations

PrecisionTree, Palisade Co.

4-15

Includes influence diagrams, decision trees and

simulation

Definitive Scenario, Definitive Software

Supports hierarchical (tree structured) diagrams

DATA Decision Analysis, TreeAge Software

Supports hierarchical (multi-level) diagrams

Creates influence diagrams and decision trees directly

in an Excel spreadsheet

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Analytica Influence Diagram of a

Marketing

Problem: The Marketing Model

4-16

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Analytica: The Price Submodel

4-17

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Analytica: The Sales Submodel

4-18

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

MSS Modeling with Spreadsheets

Spreadsheet: most popular end-user modeling

tool

Flexible and easy to use

Powerful functions

4-19

Add-in functions and solvers

Programmability (via macros)

What-if analysis

Goal seeking

Simple database management

Seamless integration of model and data

Incorporates both static and dynamic models

Examples: Microsoft Excel, Lotus 1-2-3

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Excel spreadsheet - static model example:

Simple loan calculation of monthly

payments

F = P(1 + i ) n

i (1 + i ) n

A = P

n

(

1

+

i

)

−

1

4-20

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Excel spreadsheet Dynamic model

example:

Simple loan

calculation of

monthly payments

and effects of

prepayment

4-21

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Decision Analysis: A Few

Alternatives

Single Goal Situations

Decision tables

Decision trees

4-22

Multiple criteria decision analysis

Features include decision

variables (alternatives),

uncontrollable variables, result

variables

Graphical representation of

relationships

Multiple criteria approach

Demonstrates complex

relationships

Cumbersome, if many

alternatives exists

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Decision Tables

Investment example

One goal: maximize the yield after one year

Yield depends on the status of the economy

(the state of nature)

4-23

Solid growth

Stagnation

Inflation

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Investment Example:

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%

4-24

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Investment Example:

Decision Table

4-25

Payoff Decision variables (alternatives)

Uncontrollable variables (states of economy)

Result variables (projected yield)

Tabular representation:

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Business Intelligence

Systems

(9th Ed., Prentice Hall)

Chapter 4:

Modeling and Analysis

Learning Objectives

4-2

Understand the basic concepts of

management support system (MSS) modeling

Describe how MSS models interact with data

and the users

Understand the well-known model classes and

decision making with a few alternatives

Describe how spreadsheets can be used for

MSS modeling and solution

Explain the basic concepts of optimization,

simulation and heuristics; when to use which

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Learning Objectives

4-3

Describe how to structure a linear

programming model

Understand how search methods are used to

solve MSS models

Explain the differences among algorithms,

blind search, and heuristics

Describe how to handle multiple goals

Explain what is meant by sensitivity analysis,

what-if analysis, and goal seeking

Describe the key issues of model management

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Opening Vignette:

“Model-Based Auctions Serve More

Lunches in Chile”

Background: problem situation

Proposed solution

Results

Answer and discuss the case questions

4-4

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Modeling and Analysis Topics

4-5

Modeling for MSS (a critical component)

Static and dynamic models

Treating certainty, uncertainty, and risk

Influence diagrams (in the posted PDF file)

MSS modeling in spreadsheets

Decision analysis of a few alternatives (with

decision tables and decision trees)

Optimization via mathematical programming

Heuristic programming

Simulation

Model base management

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

MSS Modeling

A key element in most MSS

Leads to reduced cost and increased

revenue

DuPont Simulates Rail Transportation System

and Avoids Costly Capital Expenses

Procter & Gamble uses several DSS models

collectively to support strategic decisions

4-6

Locating distribution centers, assignment of DCs to

warehouses/customers, forecasting demand,

scheduling production per product type, etc.

Fiat, Pillowtex (…operational efficiency)…

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Major Modeling Issues

Problem identification and environmental

analysis (information collection)

Variable identification

Forecasting/predicting

4-7

More information leads to better prediction

Multiple models: A MSS can include several

models, each of which represents a

different part of the decision-making

problem

Influence diagrams, cognitive maps

Categories of models >>>

Model management

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Categories of Models

Category

4-8

Objective

Techniques

Optimization of

problems with few

alternatives

Find the best solution from Decision tables,

a small number of

decision trees

alternatives

Optimization via

algorithm

Find the best solution from

a large number of

alternatives using a stepby-step process

Linear and other

mathematical

programming

models

Optimization via

an analytic formula

Find the best solution in

one step using a formula

Some inventory

models

Simulation

Find a good enough

solution by experimenting

with a dynamic model of

the system

Several types of

simulation

Heuristics

Find a good enough

solution using “commonsense” rules

Heuristic

programming and

expert systems

Predictive

and

Predict future

Forecasting, Markov

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

other models

occurrences, what-if

chains, financial, …

Static and Dynamic Models

Static Analysis

Dynamic Analysis

4-9

Single snapshot of the situation

Single interval

Steady state

Dynamic models

Evaluate scenarios that change over time

Time dependent

Represents trends and patterns over time

More realistic: Extends static models

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making:

Treating Certainty, Uncertainty and

Risk

Certainty Models

Uncertainty

Several outcomes for each decision

Probability of each outcome is unknown

Knowledge would lead to less uncertainty

Risk analysis (probabilistic decision making)

4-10

Assume complete knowledge

All potential outcomes are known

May yield optimal solution

Probability of each of several outcomes

occurring

Level of uncertainty => Risk (expected value)

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Certainty, Uncertainty and Risk

4-11

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Influence Diagrams

(Posted on the Course Website)

Graphical representations of a model

“Model of a model”

A tool for visual communication

Some influence diagram 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 are connected with arrows indicates the

direction of influence (relationship)

4-12

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Influence Diagrams:

Relationships

CERTAINTY

Amount in

CDs

Interest

Collected

UNCERTAINTY

Price

Sales

The shape of

the arrow

indicates the

type of

relationship

RANDOM (risk) variable: Place a tilde (~) above the variable’s name

~

Demand

Sales

4-13

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Influence Diagrams: Example

An influence diagram for the profit model

Unit Price

~

Amount used in

Advertisement

Income

Units Sold

Profit

Profit = Income – Expense

Unit Cost

Income = UnitsSold * UnitPrice

UnitsSold = 0.5 * Advertisement Expense

Expenses = UnitsCost * UnitSold + FixedCost

Expenses

Fixed Cost

4-14

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Influence Diagrams: Software

Analytica, Lumina Decision Systems

DecisionPro, Vanguard Software Co.

Integrates influence diagrams and Excel, also supports

Monte Carlo simulations

PrecisionTree, Palisade Co.

4-15

Includes influence diagrams, decision trees and

simulation

Definitive Scenario, Definitive Software

Supports hierarchical (tree structured) diagrams

DATA Decision Analysis, TreeAge Software

Supports hierarchical (multi-level) diagrams

Creates influence diagrams and decision trees directly

in an Excel spreadsheet

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Analytica Influence Diagram of a

Marketing

Problem: The Marketing Model

4-16

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Analytica: The Price Submodel

4-17

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Analytica: The Sales Submodel

4-18

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

MSS Modeling with Spreadsheets

Spreadsheet: most popular end-user modeling

tool

Flexible and easy to use

Powerful functions

4-19

Add-in functions and solvers

Programmability (via macros)

What-if analysis

Goal seeking

Simple database management

Seamless integration of model and data

Incorporates both static and dynamic models

Examples: Microsoft Excel, Lotus 1-2-3

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Excel spreadsheet - static model example:

Simple loan calculation of monthly

payments

F = P(1 + i ) n

i (1 + i ) n

A = P

n

(

1

+

i

)

−

1

4-20

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Excel spreadsheet Dynamic model

example:

Simple loan

calculation of

monthly payments

and effects of

prepayment

4-21

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Decision Analysis: A Few

Alternatives

Single Goal Situations

Decision tables

Decision trees

4-22

Multiple criteria decision analysis

Features include decision

variables (alternatives),

uncontrollable variables, result

variables

Graphical representation of

relationships

Multiple criteria approach

Demonstrates complex

relationships

Cumbersome, if many

alternatives exists

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Decision Tables

Investment example

One goal: maximize the yield after one year

Yield depends on the status of the economy

(the state of nature)

4-23

Solid growth

Stagnation

Inflation

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Investment Example:

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%

4-24

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

Investment Example:

Decision Table

4-25

Payoff Decision variables (alternatives)

Uncontrollable variables (states of economy)

Result variables (projected yield)

Tabular representation:

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

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