Decision Support Systems

kneewastefulAI and Robotics

Oct 29, 2013 (3 years and 11 months ago)

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O’Brien
,
Management Information Systems,

7
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Chapter 10

pg.
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10

Decision Support Systems



I. CHAPTER OVERVIEW


This chapter shows how management information systems, decision support systems, executive information
systems, expert systems, and artificial intelligence technologies can be applied to decision
-
making s
ituations faced
by business managers and professionals in today’s dynamic business environment.


Se
ction I:


Decision Support in
Business

Section II:


Artificial Intelligence Technologies in Business






II. LEARNING OBJECTIVES


Learning Objectives

1.

Ide
ntify the changes taking place in the form and use of decision support in business.

2.

Identify the role and reporting alternatives of management information systems.

3.

Describe how online analytical processing can meet key information needs of managers.

4.

Explai
n the decision support system concept and how it differs from traditional management information
systems.

5.

Explain how the following information systems can support the information needs of executives, managers,
and business professionals:

a.

Executive informa
tion systems

b.

Enterprise information portals

c.

Knowledge management systems

6.

Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be
used in business.

7.

Give examples of several ways expert systems can be us
ed in business decision
-
making situations.


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III. TEACHING SUGGESTIONS


Instructors can use
Figure 10
.2

to discuss the different levels of management and the structure of decision
situations they face. It can also be used to discuss the different types o
f information that is required. The instructor
should discuss the three information
-
reporting alternatives as outlined in the text (periodic, exception, and demand).
While using this figure be sure to stress that unstructured, semistructured, and structu
red decisions are information
products that are produced by the three levels of management (operational, tactical, and strategic).
Figure 10.11

illustrates the concept of online analytical processing. OLAP may involve the use of specialized servers and
mu
ltidimensional databases. It also provides fast answers to complex queries posed by managers and analysts using
management, decision support, and executive information systems.
Figure
10.15
illustrates four basic types of
analytical modelling activities
(1) what
-
if analysis, (2) sensitivity analysis, (3) goal
-
seeking analysis, and (4)
optimisation analysis.
Figure 10.20

outlines the components of an enterprise information portal. It can be used to
identify how e
-
business decision support systems can be
personalized for executives, managers, employees,
suppliers, customers, and other business partners.



Figure
10.23

illustrates some of the attributes of intelligent behavior. AI is attempting to duplicate these capabilities
in the design of computer
-
based systems. The major application areas of artificial intelligence can be explained using
Figure
10.24
. This figure groups AI applications into four major areas
-

cognitive science, computer science,
robotics, and natural interfaces.
Figure
10.25

sum
marizes a few of the many types of intelligent agents that are in
use or currently being developed.
Figure 10.26

outlines some of the major categories and examples of typical expert
systems.
Figure 10.29
gives an excellent example of many major applicati
on categories and examples of typical
expert systems.
Figure
10.36

details the components of an expert system. This figure emphasizes that the software
modules perform inferences on a knowledge base built by an expert and/or knowledge engineer. This mode
l provides
expert answers to an end user’s questions in an interactive process.







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IV. LECTURE NOTES


Section I: Decision Support in Business


Introduction


To succeed in business today, companies need information systems that can support the diverse

information and
decision
-
making needs of their managers and business professionals. This is accomplished by several types of
management information, decision support, and other information systems. The Internet, intranets, and other Web
-
enabled informat
ion technologies have significantly strengthened the role that information systems play in
supporting the decision
-
making activities of every manager and knowledge worker in business.


Analyzing
Allstate Insurance, Aviva Canada, and Others:


We can learn a

lot from this case
about the value of business intelligence
. Take a few minutes to read the case, and
we will discuss it (See
Allstate Insurance, Aviva Canada, and Others: Centralized Business Intelligence at Work

in
section IX).



Information, Decisions
, and Management:

[Figure 10
.2
]


The type of information required by decision
-
makers in a company is directly related to the
level of management
decision
-
making
and the amount of structure in the decision situations they face. The framework of the classic
managerial pyramid

applies even in today’s
downsized

organizations and
flattened

or non
-
hierarchical
organizational structures. Levels of management decision making still exist, but their size, shape, and participants
continue to change as today’s fluid o
rganizational structures evolve. Thus, the levels of managerial decision
-
making
that must be supported by information technology in a successful organization are:




Strategic Management:
-

Typically, a board of directors and an executive committee of the C
EO and top
executives develop overall organizational goals, strategies, policies, and objectives as part of a strategic
planning process.


They monitor the strategic performance of the organization and its overall direction in the political, economic,
and
competitive business environment.


Unstructured Decisions
-

Involve decision situations where it is not possible to specify in advance most of the
decision procedures to follow.


Strategic Decision Makers

-

Require more summarized, ad hoc, unscheduled repo
rts, forecasts, and external
intelligence to support their more unstructured planning and policy
-
making responsibilities.




Tactical Management

-

Increasingly self
-
directed teams as well as middle managers develop short
-

and medium
-
range plans, schedules,
and budgets and specify the policies, procedures, and business objectives for their
subunits of the organization.


They also allocate resources and monitor the performance of their organizational subunits, including
departments, divisions, process teams, a
nd other workgroups.


Semistructured Decisions

-

Some decision procedures can be prespecified, but not enough to lead to a definite
recommended decision.


Tactical Decision
-
Makers

-

Require information from both the operational level and the strategic l
evel to

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support their semistructured decision making responsibilities.




Operational Management

-

The members of self
-
directed teams or supervisory managers develop short
-
range
plans such as weekly production schedules.


They direct the use of resources a
nd the performance of tasks according to procedures and within budgets and
schedules they establish for the teams and other workgroups of the organization.


Structured Decisions
-

Involve situations where the procedures to follow when a decision is needed
can be
specified in advance.


Operational Decision Makers

-

Require more prespecified internal reports emphasizing detailed current and
historical data comparisons that support their more structured responsibilities in day
-
to
-
day operations.



Decision Str
ucture:


Providing information and support for all levels of management decision
-
making is no easy task. Therefore,
information systems must be designed to produce a variety of information products to meet the changing needs of
decision
-
makers throughout a
n organization.



Decision Support Trends


Using information systems to support business decision making has been on of the primary thrusts of the business
use of information technology. The fast pace of new information technologies like PC hardware and s
oftware suites,
client/server networks, and networked PC versions of DSS/EIS software made decision support available to lower
levels of management, as well as to non
-
managerial individuals and self
-
directed team of business professionals.


This trend ha
s accelerated with the dramatic growth of the Internet and intranets and extranets that internetwork
companies and their stakeholder.
The e
-
commerce initiatives that are being implemented by many companies are also
expanding the information and decision s
upport uses and expectations of a company and its business partners.
Today’s businesses are responding to with a variety of personalized and proactive Web
-
based analytical techniques
to support the decision
-
making requirements of all of their constituents
.


The dramatic expansion of DSS growth has opened the door to the user of
business intelligence

(BI) tools by the
suppliers, customers, and other business stakeholders of a company for customer relationship management, supply
chain management, and other e
-
business applications.



Decision Support Systems
:


Decision support systems

are computer
-
based information systems that provide interactive information support to
managers and business professionals during the decision
-
making process.


Decision suppor
t systems use:



Analytical models



Specialized databases



Decision maker’s own insights and judgments



Interactive, computer
-
based modeling process to support the making of semistructured and unstructured
business decisions



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DSS Components:


Decision support s
ystems rely on
model bases

as well as databases as vital system resources. A DSS model base is
a software component that consists of models used in computational and analytical routines that mathematically
express relationships among variables.


Example
s include:



Spreadsheet models



Linear programming models



Multiple regression forecasting models



Capital budgeting present value models



Management Information Systems
:


Management information systems

were the original type of information systems developed

to support managerial
decision
-
making. A management information system produces information products that support many of the day
-
to
-
day decision
-
making needs of managers and business professionals. Reports, displays, and responses produced
by informatio
n systems provide information that managers have specified in advance as adequately meeting their
information needs. Such predefined information products satisfy the information needs of managers at the
operational and tactical levels of the organization
who are faced with more structured types of decision situations.


Management Reporting Alternatives
:


MIS provide a variety of information products to managers. Three major reporting alternatives are provided by such
systems as:



Periodic scheduled report
s
-


-

Traditional form of providing information to managers. Uses a prespecified format designed to provide
managers with information on a regular basis.



Exception Reports

-

Reports that are produced only when exceptional conditions occur.




Demand Reports an
d Responses

-

Information is provided whenever a manager demands it.




Push Reporting

-

Information is
pushed

to a manager’s networked workstation.



Online Analytical Processing
:

[Figure
10.10
]


Online analytical processing

is a capability of management, de
cision support, and executive information systems
that enables managers and analysts to interactively examine and manipulate large amounts of detailed and
consolidated data from many perspectives (analytical databases, data marts, data warehouses, data min
ing
techniques, and multidimensional database structures, specialized servers and web
-
enabled software products).


Online analytical processing involves several basic analytical operations:



Consolidation
-

Involves the aggregation of data. This can invo
lve simple roll
-
ups or complex groupings
involving interrelated data.




Drill
-
Down
-

OLAP can go in the reverse direction and automatically display detailed data that comprises
consolidated data.



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Slicing and Dicing
-

Refers to the ability to look at the da
tabase from different viewpoints. Slicing and dicing is
often performed along a time axis in order to analyze trends and find patterns.


OLAP applications:



Access very large amounts of data to discover patterns, trends, and exception conditions



Analyze th
e techniques between many types of business elements



Involve aggregated data



Compare aggregated data over hierarchical time periods



Present data in different perspectives



Involve complex calculations between data elements



Are able to respond quickly to use
r requests so that managers or analysts can pursue an analytical or decision
thought process without being hindered by the system



Geographic Information and Data Visualization Systems


Geographic information systems

(GIS) and
data visualization systems

(
DVS) are special categories of DSS that
integrate computer graphics with other DSS features.




Geographic Information System



is a DSS that uses geographic databases to construct and display maps and
other graphics displays that support decisions affecti
ng the geographic distribution of people and other
resources.




Data Visualization Systems


DVS systems represent complex data using interactive three
-
dimensional
graphical forms such as charts, graphs, and maps. DVS tools help users to interactively sor
t, subdivide,
combine, and organize data while it is in its graphical form.



Using Decisi
on Support Systems: [Figure
10.15
]


Using a decision support system involves an interactive
analytical modelling

process. Typically, a manager uses a
DSS software pa
ckage at his workstation to make inquiries, responses and to issue commands. This differs from the
demand responses of information reporting systems, since managers are not demanding prespecified information.
Rather, they are exploring possible alternati
ves. They do not have to specify their information needs in advance.
Instead they use the DSS to find the information they need to help them make a decision.


Using a DSS involves four basic types of analytical modelling activities:




What
-
If Analysis:

-

In what
-
if analysis, an end user makes changes to variables, or relationships among
variables, and observes the resulting changes in the values of other variables.




Sensitivity Analysis:
-

Is a special case of what
-
if analysis. Typically, the value of o
nly one variable is changed
repeatedly, and the resulting changes on other variables are observed. So sensitivity analysis is really a case of
what
-
if analysis involving repeated changes to only one variable at a time. Typically, sensitivity analysis is
used

when decision
-
makers are uncertain about the assumptions made in estimating the value of certain key variables.




Goal
-
Seeking Analysis:

-

Reverses the direction of the analysis done in what
-
if and sensitivity analysis. Instead
of observing how change
s in a variable affect other variables, goal
-
seeking analysis sets a target value for a
variable and then repeatedly changes other variables until the target value is achieved.




Optimization Analysis:

-

Is a more complex extension of goal
-
seeking analysis.

Instead of setting a specific
target value for a variable, the goal is to find the optimum value for one or more target variables, given certain

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constraints. Then one or more other variables are changed repeatedly, subject to the specified constraints,
until
the best values for the target variables are discovered.





Data Mining for Decision Support:


The main purpose of data mining is knowledge discovery, which will lead to decision support.


Characteristics of data mining include:



Data mining softwar
e analyzes the vast stores of historical business data that have been prepared for analysis
in corporate data warehouses.



Data mining attempts to discover patterns, trends, and correlations hidden in the data that can give a company a
strategic business
advantage.



Data mining software may perform regression, decision
-
tree, neural network, cluster detection, or market basket
analysis for a business.



Data mining can highlight buying patterns, reveal customer tendencies, cut redundant costs, or uncover unsee
n
profitable relationships and opportunities.



Executive Information Systems:


Executive information systems

(EIS) are information systems that combine many of the features of management
information systems and decision support systems. EIS focus on meet
ing the strategic information needs of top
management. The goal of EIS is to provide top executives with immediate and easy access to information about a
firm's
critical success factors

(CSFs), that is, key factors that are critical to accomplishing the o
rganization’s strategic
objectives.


Capabilities of EIS include:



More features such as Web browsing, electronic mail, groupware tools, and DSS and expert system capabilities
are being added.



Information is presented in forms tailored to the preferences o
f the executives using the system. Heavy use of
graphical user interface and graphics displays.



Information presentation methods used by an EIS include exception reporting and trend analysis. The ability to
drill down

allows executives to quickly retriev
e displays of related information at lower levels of detail.



Internet and intranet technologies have added capabilities to EIS systems.



EIS’s have spread into the ranks of middle management and business professionals as they have recognized
their feasibili
ty and benefits, and as less
-
expensive systems for client/server and corporate intranets become
available.



Enterprise Portals and Decision Support:


Major changes and expansion are taking place in traditional MIS, DSS, and EIS tools for providing the inf
ormation
and modeling that managers need to support their decision making. Some of these changes include:



Decision support in business is changing, driven by rapid developments in end user computing and networking;
Internet, Web browser, and related techn
ologies, and the explosion of e
-
commerce activity.



Growth of corporate intranets, extranets, as well as the Web, has accelerated the development and use of
“executive class” information delivery and decision support software tools by lower levels of mana
gement and
by individuals and teams of business professionals.



Dramatic expansion of e
-
commerce has opened the door to the use of such e
-
business DSS tools by the
suppliers, customers, and other business stakeholders of a company for customer relationship
management,

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supply chain management, and other e
-
business applications.



Enterprise Information Portals:
[Figure 10
.
20
]




Enterprise information portals

(EIP) are being developed by companies as a way to provide web
-
enabled
information, knowledge, and deci
sion support to executives, managers, employees, suppliers, customers, and
other business partners.




Enterprise information portals
are described as a customized and personalized web
-
based interface for
corporate intranets, that give users easy access to a

variety of internal and external business applications,
databases, and services.



Enterprise Knowledge Portals:


Enterprise information portal is the entry to corporate intranets that serve as the primary
knowledge management
systems

for many companies.

They are often called
enterprise knowledge portals

by some vendors. Knowledge
management systems are defined as the use of information technology to help gather, organize, and share business
knowledge within an organization.



Enterprise information portal
s can play a major role in helping a company use its intranets as knowledge
management systems to share and disseminate knowledge in support of its business decision
-
making.



Knowledge Management Systems


In many organizations, hypermedia databases at cor
porate intranet websites have become the
knowledge bases

for
storage and dissemination of business knowledge. This knowledge frequently takes the form of best practices,
policies, and business solutions at the project, team, business unit, and enterprise
levels of the company.


For many companies, enterprise information portals are the entry to corporate intranets that serve as their knowledge
management systems.
Enterprise information portals

play an essential role in helping companies use their intranet
s
as knowledge management systems to share and disseminate knowledge in support of business decision making by
managers and business professionals.





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IV. LECTURE NOTES

(con’t)


Section II: Artificial Intelligence Technologies in Business


Business and

AI


Business and other organizations are significantly increasing their attempts to assist the human intelligence and
productivity of their knowledge workers with artificial intelligence tools and techniques.


AI includes:



Natural languages



Industrial ro
bots



Expert systems



Intelligent agents



Analyzing
Wal
-
Mart, BankFinancial, and HP


We can learn a lot about

the business vale of artificial intelligence technologies from this case
. Take a few minutes to
read it, and we will discuss it (
See Wal
-
Mart, Ban
kFi
nancial, and HP:

The Business Value of AI
in Section IX).



An Overview of Artificial Intelligence

[Figure
10.23
]
:


Artificial intelligence

(AI) is a science and technology based on disciplines such as computer science, biology,
psychology, linguistics
, mathematics, and engineering. The goal of AI is to develop computers that can think, as
well as see, hear, walk, talk, and feel. A major thrust of AI is the development of computer functions normally
associated with human intelligence, such as reasonin
g, learning, and problem solving.



The Domains of Artificial Intelligence:

[Figure
10.24

& Figure
10.25
]


AI applications can be grouped into three major areas:




Cognitive Science
-

This area of artificial intelligence is based on research in biology, n
eurology, psychology,
mathematics, and many allied disciplines. It focuses on researching how the human brain works and how
humans think and learn. The results of such research in human information processing are the basis for the
development of a variet
y of computer
-
based applications in artificial intelligence.


Applications in the cognitive science area of AI include:


Expert Systems
-

A computer
-
based information system that uses its knowledge about a specific complex
application area to act as an exp
ert consultant to users. The system consists of knowledge base and software
modules that perform inferences on the knowledge, and communicate answers to a user’s questions.


Knowledge
-
Based Systems
-

An information system, which adds a knowledge base and

some, reasoning
capability to the database and other components, found in other types of computer
-
based information systems.


Adaptive Learning Systems
-

An information system that can modify its behavior based on information acquired
as it operates.


F
uzzy Logic Systems
-

Computer
-
based systems that can process data that are incomplete or only partially

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correct. Such systems can solve unstructured problems with incomplete knowledge by developing approximate
inferences and answers.


Neural Network

-

sof
tware can learn by processing sample problems and their solutions. As neural nets start to
recognize patterns, they can begin to program themselves to solve such problems on their own.


Genetic Algorithm

-

software uses Darwinian (survival of the fittest)
, randomizing, and other mathematical
functions to simulate evolutionary processes that can generate increasingly better solutions to problems.


Intelligent Agents

-

Use expert system and other AI technologies to serve as software surrogates for a variety

of

end user applications.




Robotics:

-

AI, engineering, and physiology are the basic disciplines of robotics. This technology produces
robot machines with computer intelligence and computer
-
controlled, humanlike physical capabilities.


Robotics applica
tions include:

1. Visual perception (sight)

2. Tactility (touch)

3. Dexterity (skill in handling and manipulation)

4. Locomotion (ability to move over any terrain)

5. Navigation (properly find one’s way to a destination)




Natural Interface:
-

The de
velopment of natural interfaces is considered a major area of AI applications and is
essential to the natural use of computers by humans. For example, the developments of natural languages and
speech recognition are major thrusts of this area. Being able

to talk to computers and robots in conversational
human languages and have them “understand” us is the goal of AI researchers. This application area involves
research and development in linguistics, psychology, computer science, and other disciplines.
Efforts in this
area include:


Natural Language
-

A programming language that is very close to human language. Also, called very high
-
level language.


Multisensory Interfaces
-

The ability of computer systems to recognize a variety of human body movements
,
which allows them to operate.


Speech Recognition
-

The ability of a computer system to recognize speech patterns, and to operate using these
patterns.


Virtual Reality
-

The use of multisensory human/computer interfaces that enables human users to exper
ience
computer
-
simulated objects, entities, spaces, and “worlds” as if they actually existed.



Expert Systems


One of the most practical and widely implemented application of artificial intelligence in business is the development
of expert systems and ot
her
knowledge
-
based information systems.





Knowledge
-
based information system

-

adds a knowledge base to the major components found in other types
of computer
-
based information systems.




Expert System
-

A computer
-
based information system that uses its kn
owledge about a specific complex
application area to act as an expert consultant to users. ES provide answers to questions in a very specific
problem area by making humanlike inferences about knowledge contained in a specialized knowledge base.

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They must

also be able to explain their reasoning process and conclusions to a user.



Components of Expert Systems:

[Figure 10.26]


The components of an expert system include a knowledge base and software modules that perform inferences on the
knowledge and commun
icate answers to a user’s question.


The interrelated components of an expert system include:




Knowledge base
:
-

the knowledge base of an ES contains:

1.

Facts about a specific subject area

2.

Heuristics (rule of thumb) that express the reasoning procedur
es of an expert on the subject




Software resources
:
-

An ES software package contains:

1.

Inference engine

that processes the knowledge related to a specific problem

2.

User interface program that communicates with end users

3.

Explanation program to expla
in the reasoning process to the user

4.

Software tools for developing expert systems include knowledge acquisition programs and expert system
shells




Hardware resources:
-

These include:

1.

Stand alone microcomputer systems

2.

Microcomputer workstations an
d terminals connected to minicomputers or mainframes in a
telecommunications network

3.

Special
-
purpose computers




People resources:
-

People resources include:

1.

Knowledge engineers

2.

End
-
users



Expert System Applications:

[Figure 10.29]


Using an e
xpert system involves an interactive computer
-
based session, in which:



The solution to a problem is explored with the expert system acting as a consultant.



Expert system asks questions of the user, searches its knowledge base for facts and rules or other

knowledge.



Explains its reasoning process when asked.



Gives expert advice to the user in the subject area being explored.



Examples include: credit management, customer service, and productivity management.




Expert System Applications:
[Figure 9.34]


E
xpert systems typically accomplish one or more generic uses. Six activities include:



Decision management



Diagnostic/troubleshooting



Maintenance scheduling



Design/configuration



Selection/classification



Process monitoring/control



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Developing Expert Syste
ms


The easiest way to develop an expert system is to use an
expert system

shell

as a developmental tool. An expert
system shell is a software package consisting of an expert system without a kernel, that is, its knowledge base. This
leaves a
shell

of so
ftware (the inference engine and user interface programs) with generic inferencing and user
interface capabilities. Other development tools (such as rule editors and user interface generators) are added in
making the shell a powerful expert system develop
ment tool.



Knowledge Engineering


A
knowledge engineer

is a professional who works with experts to capture the knowledge (facts and rules of thumb)
they possess. The knowledge engineer then builds the knowledge base using an interactive, prototyping pro
cess
until the expert system is acceptable. Thus, knowledge engineers perform a role similar to that of systems analysts in
conventional information systems development. Obviously, knowledge engineers must be able to understand and
work with experts in m
any subject areas. Therefore, this information systems speciality requires good people skills,
as well as a background in artificial intelligence and information systems.



Neural Networks:


Neural networks

are computing systems modelled on the human bra
in's mesh
-
like network of interconnected
processing elements, called
neurons
. Of course, neural networks are much simpler than the human brain (estimated
to have more than 100 billion neuron brain cells). Like the brain, however, such networks can proces
s many pieces of
information simultaneously and can learn to recognize patterns and program themselves to solve related problems on
their own.


Neural networks can be implemented on microcomputers and other computer systems via software packages, which
sim
ulate the activities of a neural network of many processing elements. Specialized neural network coprocessor
circuit boards are also available. Special
-
purpose neural net microprocessor chips are used in some application areas.


Uses include:



Military we
apons systems



Voice recognition



Check signature verification



Manufacturing quality control



Image processing



Credit risk assessment



Investment forecasting



Data mining



Fuzzy Logic Systems


Fuzzy Logic

is a method of reasoning that resembles human reasoning

since it allows for approximate values and
inferences (fuzzy logic) and incomplete or ambiguous data (fuzzy data) instead of relying only on
crisp

data, such as
binary (yes/no) choices.



Fuzzy Logic in Business:


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Examples of applications of fuzzy logic
are numerous in Japan, but rate in the United States. The United States has
tended to prefer using AI solutions like expert systems or neural networks. Japan has implemented many fuzzy logic
applications, especially the use of special
-
purpose fuzzy logic

microprocessors chips, called fuzzy process
controllers.


Examples of fuzzy logic applications in Japan include:



Riding in subway trains and elevators



Riding in cars that are guided or supported by fuzzy process controllers



Trading shares on the Tokyo S
tock Exchange using a stock
-
trading program based on fuzzy logic



Japanese
-
made products that use fuzzy logic microprocessors include auto
-
focus cameras, auto
-
stabilizing,
camcorders, energy
-
efficient air conditioners, self
-
adjusting washing machines, and a
utomatic transmissions.



Genetic Algorithms:


The use of genetic algorithms is a growing application of artificial intelligence. Genetic algorithm software uses
Darwinian (survival of the fittest); randomizing, and other mathematical functions to simulat
e an evolutionary process

that can yield increasingly better solutions to a problem. Genetic algorithms were first used to simulate millions of
years in biological, geological, and ecosystem evolution in just a few minutes on a computer. Now genetic algo
rithm
software is being used to model a variety of scientific, technical, and business processes.


Genetic algorithms are especially useful for situations in which thousands of solutions are possible and must be
evaluated to produce an optimal solution. G
enetic algorithm software uses sets of mathematical process rules
(algorithms) that specify how combinations of process components or steps are to be formed. This may involve:



Trying random process combinations (mutation)



Combining parts of several good p
rocesses (crossover)



Selecting good sets of processes and discarding poor ones (selection)



Virtual Reality (VR)


Virtual reality

(VR) is computer
-
simulated reality. VR is the use of multisensory human/computer interfaces that
enable human users to expe
rience computer
-
simulated objects, entities, spaces, and "worlds" as if they actually
existed (also called
cyberspace

and
artificial reality
).


VR Applications:




Computer
-
aided design (CAD)



Medical diagnostics and treatment



Scientific experimentation in ma
ny physical and biological sciences



Flight simulation for training pilots and astronauts



Product demonstrations



Employee training



Entertainment (3
-
D video games)



VR Limitations:


The use of virtual reality seems limited only by the performance and cost o
f its technology. For example, some VR
users develop:



Cybersickness
-

eye strain, motion sickness, performance problems



Cost of VR is quite expensive


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Intelligent Agents

[Figure
10.36
]
:


An
intelligent agent

(also called intelligent assistants/wizards
) is a
software surrogate

for an end user or a process
that fulfils a stated need or activity. An intelligent agent uses a built
-
in and learned knowledge base about a person
or process to make decisions and accomplish tasks in a way that fulfils the inten
tions of a user. One of the most well
known uses of intelligent agents is the wizards found in Microsoft Office and other software suites.


The use of intelligent agents is expected to grow rapidly as a way for users to:



Simplify software use



Search web
sites on the Internet and corporate intranets



Help customers do comparison
-
shopping among the many e
-
commerce sites on the Web.






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IV. LECTURE NOTES (con’t)


Summary


● Information, Decisions, and Management.
Information systems can support a variety of

management decision
-
making levels and decision. These include the three levels of management activity (strategic, tactical, and operational

decision making) and three types of decision structures (structures, semistructured, and unstructured). Informatio
n
systems provide a wide range of information products to support these types of decisions at all levels of the
organization.


● Decision Support Trends.
Major changes are taking place in traditional MIS, DSS, and EIS tools for providing the
information and modelling managers need to support their decision making. Decision support in business is
changing, driven by rapid developm
ents in end user computing and networking; Internet and Web technologies; and
Web
-
enables business applications. The growth of corporate intranets, extranets, as well as the Web, has
accelerated the development of “executive class” interfaces like enterpr
ise information portals and Web
-
enabled
business professionals. In addition, the growth of e
-
commerce and e
-
business applications has expanded the use of
enterprise portals and DSS tools by the suppliers, customers, and other business stakeholders of a co
mpany.


● Management Information Systems.
Management information systems provide pre
-
specified reports and
responses to managers of a periodic, exception, demand, or push reporting basis, to meet their need for information to

support decision making.


● O
LAP and Data Mining.
Online analytical processing interactively analyzes complex relationships among large
amounts of data stored in multidimensional databases. Data mining analyzes the vast amounts of historical data that
have been prepared for analysis
in data warehouses. Both technologies discover patterns, trends, and exceptional
conditions in a company’s data that support their business analysis and decision making.


● Decision Support Systems.
Decision support systems are interactive, computer
-
bases information systems that
use DSS software and a model base and database to provide information tailored to support semistructured and
unstructured decision faced by indivi
dual managers. They are designed to use a decision maker’s own insights and
judgements in an ad hoc, interactive, analytical modelling process leading to a specific decision.


● Executive Information Systems.
Executive information systems are information
systems originally designed to
support the strategic information needs of top management. However, their use is spreading to lower levels of
management and business professionals. EIS are easy to use and enable executives to retrieve information tailored

to

their needs and preferences. Thus, EIS can provide information about a company’s critical success factors to
executives to support their planning and control responsibilities.


● Enterprise Information and Knowledge Portals.
Enterprise information portals provide a customized and
personalized Web
-
based interface for corporate intranets to give their users easy access to a variety of internal and
external business applications, da
tabases, and information services that are tailored to their individual preferences
and information needs. Thus, an EIP can supply personalized Web
-
enabled information, knowledge, and decision
support to executives, managers, and business professionals, a
s well as customers, suppliers, and other business
partners. As enterprise knowledge portal is a corporate intranet portal that extends the use of an EIP to include
knowledge management functions and knowledge base resources so that it becomes a major for
m of knowledge
management system for a company.


● Artificial Intelligence.
The major application domains of artificial intelligence (AI) include a variety of applications
in cognitive science, robotics, and natural interfaces. The goal of AI is the devel
opment of computer functions
normally associated with human physical and mental capabilities, such as robots that see, hear, talk, feel, and move,
and software capable of reasoning, learning, and problem solving. Thus, AI is being applied to many applicat
ions in
business operations and managerial decision making, as well as in many other fields.



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● AI Technologies.
The many application areas of AI are summarized in Figure 10.24, including neural networks,
fuzzy logic, genetic algorithms, virtual reality, and intelligent agents. Neural nets are hardware or software systems
based on simple models of

the brain’s neuron structure that can learn to recognize patterns in data. Fuzzy logic
systems use rules of approximate reasoning to solve problems where data are incomplete or ambiguous. Genetic
algorithms use selection, randomizing, and other mathemat
ics functions to simulated an evolutionary process that
can yield increasingly better solutions to problems. Virtual reality systems are multisensory systems that enable
human users to experience computer
-
simulated environments as if they actually existed
. Intelligent agents are
knowledge
-
bases software surrogates for a user of a process in the accomplishment of selected tasks.


● Expert Systems.
Expert systems are knowledge
-
based information systems that use software and a knowledge base

about a specific
, complex application area to act as expert consultants to users in many business and technical
applications. Software includes an inference engine program that makes inferences based on the facts and rules
stored in the knowledge base. A knowledge base
consists of facts about a specific subject area and heuristics (rules
of thumb) that express the reasoning procedures of an expert. The benefits of expert systems (such as preservation
and replication of expertise) must be balance with their limited appli
cability in many problem situations.


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V. KEY TERMS AND CONCEPTS
-

DEFINED


Analytical Modeling

(333
)
:

Interactive use of computer
-
based mathematical models to explore decision alternatives using what
-
if analysis,
sensitivity analysis, goal
-
seeking ana
lysis, and optimization analysis.


Analytical Modeling


Goal
-
Seeking Analysis

(335
)
:

Making repeated changes to selected variables until a chosen variable reaches a target value.


Analytical Modeling
-

Optimization Analysis

(335
)
:

Finding an optimum value

for selected variables in a mathematical model, given certain constraints.


Analytical Modeling
-

Sensitivity Analysis

(334
)
:

Observing how repeated changes to a single variable affects other variables in a mathematical model.


Analytical Modeling
-

What
-
if Analysis

(333
)
:

Observing how changes to selected variables affect other variables in a mathematical model.


Artificial Intelligence

(343
)
:

A science and technology, whose goal is to develop computers that can think, as well as see, hear, walk, talk, an
d
feel.


Artificial Intelligence
-

Application Areas

(345
)
:

Major areas of AI research and development include cognitive science, computer science, robotics, and natural
interface applications.


Artificial Intelligence


Domains

(345
)
:

The major domains o
f AI intelligence are grouped under three major areas: Cognitive science applications, robotics
applications, and natural interface applications.


Business Intelligence (325
):

A term primarily used in industry that incorporates a range of analytical and de
cision support applications in
business including data mining, decision support systems, knowledge management systems, and online analytical
processing.


Data Mining

(336
)
:

Using special
-
purpose software to analyze data from a data warehouse to find hidd
en patterns and trends.


Data Visualization Systems

(331
)
:

DVS systems represent complex data using interactive three
-
dimensional graphical forms such as charts, graphs, and
maps. DVS tools help users to interactively sort, subdivide, combine, and organi
ze data while it is in its graphical
form.


Decision Structure

(323
)
:

Information systems can support a variety of management levels and decisions. These include the three levels of
management activity (strategic, tactical, and operational), and three ty
pes of decision structures (structured,
semistructured, and unstructured).


Decision Support System

(326
)
:

An information system that utilizes decision models, a database, and a decision maker’s own insights in an ad hoc,
interactive analytical modelling p
rocess to reach a specific decision by a specific decision maker.



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Decision Support Trends (324
):

Major changes are taking place in traditional MIS, DSS, and EIS tools for providing the information and modeling
managers need to support their decision
-
mak
ing.


DSS
Components (326
):

Decision support systems rely on model bases as well as databases as vital system resources.


Enterprise Information Portal

(339
)
:

Enterprise information portals are being developed by companies as a way to provide web
-
enable
d information,
knowledge, and decision support to executives, managers, employees, suppliers, customers, and other business
partners.


Enterprise Knowledge Portal

(341
)
:

An enterprise information portal that serves as a knowledge management system by provi
ding users with access to
enterprise knowledge bases.


Executive Information System

(338
)
:

An information system that provides strategic information tailored to the needs of top management.


Expert System

(348
)
:

A computer
-
based information system that us
es its knowledge about a specific complex application area to act as an
expert consultant to users.


Expert System


Applications

(349
)
:

Includes applications such as diagnosis, design, prediction, interpretation, and repair.


Expert System
-

Benefits and

Limitations

(3
50
)
:

Benefits include the preservation and replication of

expertise. They have limited applicability in many

problem
situations.


Expert System


Components

(3
48
)
:

The system consists of a knowledge base and software modules that perform in
ferences on the knowledge, and
communicate answers to a user’s questions.


Expert System


System Development

(3
52
)
:

Expert systems can be purchased or developed if a problem situation exists that is suitable for solution by expert
systems rather than by
conventional experts and information processing.


Expert System Shell

(3
53
)
:

An expert system without its knowledge base.


Fuzzy Logic

(3
55
)
:

A computer
-
based system that can process data that are incomplete or only partially correct, i.e., fuzzy data. Su
ch
systems can solve unstructured problems with incomplete knowledge as humans do.


Genetic Algorithms

(3
56
)
:

Genetic algorithms use sets of mathematical process rules (algorithms) that specify how combinations of process
components or steps are to be form
ed.


Geographic Information System

(3
31
)
:

A GIS is a DSS that constructs and displays maps and other graphics displays that support decisions affecting the
geographic distribution of people and other resources.


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Inference Engine

(3
48
)
:

The software com
ponent of an expert system, which processes the rules and facts, related to a specific problem and
makes associations and inferences resulting in recommended sources of action.


Intelligent Agent

(3
59
)
:

A knowledge base software surrogate for a user or pro
cess in the accomplishment of selected tasks.


Knowledge Base

(3
48
)
:

A computer
-
accessible collection of knowledge about a subject in a variety of forms, such as facts and rules of
inference, frames, and objects.


Knowledge Engineer

(3
53
)
:

A specialist who

works with experts to capture the knowledge they possess in order to develop a knowledge base for
expert systems and other knowledge
-
based systems.


Knowledge Management System

(3
41
)
:

Knowledge management systems are defined as the use of information tec
hnology to help gather, organize, and
share business knowledge within an organization.


Level of Management Decision Making

(320
)
:

Information systems can support a variety of management levels and decisions. These include the three levels of
management a
ctivity (strategic, tactical, and operational), and three types of decision structures (structured,
semistructured, and unstructured).


Management Information System

(
328
)
:

A management support system that produces prespecified reports, displays, and resp
onses on a periodic, exception,
or demand basis.


Model Base

(326)
:

An organized collection of conceptual, mathematical, and logical models that express business relationships,
computational routines, or analytical techniques. Such models are stored in th
e form of programs and program
subroutines, command files, and spreadsheets.


Neural Network

(354
)
:

Massively parallel neurocomputer systems whose architecture is based on the human brain’s mesh
-
like neuron
structure. Such networks can process many pieces

of information simultaneously and can learn to recognize patterns
and programs themselves to solve related problems on their own.


Online Analytical Processing

(3
29
)
:

Management, decision support, and executive information systems can be enhanced with an
online analytical
processing capability. Through OLAP, managers are able to analyze complex relationships in order to discover
patterns, trends, and exception conditions in an online, realtime process that supports their business analysis and
decision
-
mak
ing.


Reporting Alternatives

(
328
)
:

Three major reporting alternatives include periodic scheduled reports, exception reports, and demand reports and
responses.


Robotics

(3
47
)
:

The technology of building machines (robots) with computer intelligence and hum
an like physical capabilities.



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Virtual Reality

(3
56
)
:

The use of multisensory human/computer interfaces that enable human users to experience computer
-
simulated
objects, entities, spaces, and “worlds” as if they actually existed.




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VI. REVIEW QUIZ

-

Mat
ch one of the key terms and concepts


1

8

Decision support trends

20

22

Knowledge management system

2

9

DSS components

21

11

Enterprise knowledge portal

3

23

Level of management decision making

22

2

Artificial intelligence

4

6

Decision structure

23

2a

A
I


Application areas

5

12

Executive information system

24

29

Robotics

6

24

Management information system

25

30

Virtual reality

7

28

Reporting alternatives

26

17

Geographic information system

8

7

Decision support system

27

13

Expert system

9

3

Busines
s intelligence

28

13a

Expert system


Applications

10

25

Model base

29

13b

Expert system


Benefits & limitations

11

1

Analytical modelling

30

20

Knowledge base

12

1d

What
-
if analysis

31

18

Inference engine

13

1c

Sensitivity analysis

32

14

Expert syste
m shell

14

1a

Goal
-
seeking analysis

33

13d

Expert system


System development

15

1b

Optimization analysis

34

21

Knowledge engineer

16

27

Online analytical processing

35

26

Neural network

17

4

Data mining

36

15

Fuzzy logic

18

5

Data visualization syste
m

37

19

Intelligent agent

19

10

Enterprise information portal

38

16

Genetic algorithms







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VII. ANSWERS TO DISCUSSION QUESTIONS



1.

Is the form and use of information and decision support systems for managers and business professionals
changi
ng and expanding? Why or why not?


Yes, the form and use of information and decision support in e
-
business is changing and expanding. Certainly
changes are taking place in traditional MIS, DSS, and EIS tools, and these changes are being driven by the rap
id
developments in end user computing and networking. Internet, web browser, and related technologies, and the
explosion of e
-
commerce activities are also causing rapid change. The growth of corporate intranets, extranets, as
well as the Web, has acceler
ated the development of “executive class” interfaces like enterprise information portals,
and Web enabled decision support software tools and their use by lower of management and by individuals and
teams of business professionals. The expansion of e
-
comme
rce has increased the use of enterprise portals and DSS
tools by the suppliers, customers, and other business stakeholders of a company.


2.

Has the growth of self
-
directed teams to manage work in organizations changed the need for strategic,
tactic
al, and operational decision making in business?


Although there has been tremendous growth in the use of self
-
directed teams in organizations in order to manage the
work, the basics for decision making have not changed that much. Strategic, tactical, and

operational decision
making continue to be carried out in organizations regardless of how the work is completed. What has changed is
the way in which the work is being completed. Through technology, self
-
directed teams now have new and creative
ways of
completing their duties.


3.

What is the difference between the ability of a manager to retrieve information instantly on demand using
an MIS, and the capabilities provided by a DSS?


Managers have traditionally relied on the capabilities of MIS to

obtain the data that they required. However, the
information for these requests had traditionally been structured in advance, and was of the structured type of
request. In a DSS support system, the capabilities are much broader. Now managers can query
the information in a
number of ways, and these systems can handle the ad hoc queries that come about. DSS provide the capabilities for
a manager to participate in interactive analytical modeling in order to make more informed decision. DSS software is
ca
pable of supporting semistructured and unstructured decisions faced by individual managers. They are designed
to use decision maker’s own insights and judgments in an ad hoc, interactive, analytical modeling process which will
lead them to a specific deci
sion.


4.

Refer to the Real World Case on Allstate Insurance, Aviva Canada, and others in the chapter. Companies
appear to believe that business intelligence is a “business issue” and not a “technology issue.” If this is
the case, why does it appear th
at companies are placing more and more responsibility for BI in the hands of
the IT department?


5.

In what ways does using an electronic spreadsheet package provide you with the capabilities of a decision
support system?


An electronic spreadsheet package

can be thought of as one of the earlier forms of decision support systems.
Spreadsheets allow users to complete “what
-
if”, sensitivity, goal seeking, and optimization analysis. They also
provide some features of database management and dialog management

support.


6.

Are enterprise information portals making executive information systems unnecessary? Explain your
reasoning.



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First of all, in answering the question students’ should explain what an EIP system is versus an EIS system. As such,

EIPs are
dev
eloped by companies as a way to provide web
-
enabled information, knowledge, and decision support to
executives, managers, employees, suppliers, customers, and other business partners. EISs on the other hand, are
designed to provide strategic information t
hat are tailored to the needs of top management.


Whether or not EIP’s will eventually make EIS systems unnecessary is a matter of debate. Students’ may agree that
as more and more enriched features are added to EIP systems that their importance will be

heightened. On the other
hand, EIS systems are also being developed with enriched features such as Web browsing, electronic mail,
groupware tools, and DSS and expert systems capabilities to make them even more useful to managers and business
professional
s.


7.

Refer to the Real World Case on Wal
-
Mart, BankFinancial, and HP in the chapter. Why are neural
network and expert system technologies used in many data
-
mining applications?


Reasons could include:




Neural networks can “learn” from the data it p
rocesses, thereby learning to recognize patterns and
relationships in the data it processes. Thus neural networks can change the strengths of the
interconnections between the elements in response to changing patterns in the data it receives and
the result
s that occur. The neural network technology can be used to evaluate or “make decisions”

on its own. An example is that of BankFinancial using neural networks to more accurately target
promotions to customers and prospects.



Expert system technologies act
as a consultant to end users in very specific problem areas by
making humanlike inferences about knowledge contained in a specialized knowledge base. Expert
systems must be able to explain their reasoning process and conclusions to a user. An example
wou
ld be the “If
-
Ten” analysis used by Wal
-
Mart in managing its inventory.


8.


Can computers think? Will they ever be able to? Explain why or why not.


Computers will probably never be able to reason in the same way that humans do. However, computers are

likely to
be able to perform more and more tasks that up until now could only be performed by humans. Experimentation
continues to develop in the field of artificial intelligence, and improvements are ongoing. Will a computer ever pass
the Turing test i
s questionable.


9.


What are some of the most important applications of AI in business? Defend your choices.


In business, expert systems are probably the most important application of artificial intelligence, though the use of
such systems is still quit
e limited. In other areas, robotics is widely used in manufacturing, and natural interface
applications are becoming more and more a part of information systems for many different applications.
Major areas
of AI research and development include cognitive
science, computer science, robotics, and natural interface
applications.


10.

What are some of the limitations or dangers you see in the use of AI technologies such as expert systems,
virtual reality, and intelligent agents? What could be done to minimize

such effects?


Students’ will suggest a number of answers to this question. However, one possible solution could deal with the
ethical issues of these systems. Are they being used for the good of society or is the potential for their misuse
growing incr
easingly with the more complex developments taking place. The design of these systems is both
complex and powerful. We must begin to ask ourselves what is the harmful potential of these systems, and how far
will we be willing to go to use them to supplem
ent the human reasoning process that we are born with.





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VIII. ANSWERS TO
ANALYSIS

EXERCISES



1.

BizRate.com: eCommerce Website Reviews


a.

Use BizRate.com to check out retailers for a product you want to buy. How thorough, valid, and valuable
w
ere the reviews to you? Explain.


Many of the sites had received high ratings. Ratings in the 4


4.5 category are common, and gives the
indication that they are relatively good sites to shop from.


b.

How could other businesses use a similar web
-
enabl
ed review system? Give an example.


Similar web
-
enabled reporting systems could be used in a large number of business situations. This could
include reporting systems on automotive dealerships, hotels, restaurants, airlines, amusement parks, and car
re
ntal agencies.


c.

How is BizRate.com similar to a web enabled decision support system (DSS)?


Decision support systems provide summaries of critical information, real
-
time monitoring, and exception
reporting to decision makers. They also allow decisio
n makers to drill down into the information in order to
receive more detailed information on specific topics. BizRate.com is similar to a DSS in those regards. One
could easily imagine a similarly designed system that provided information about authorized

vendors,
products, bids, availability, performance, order tracking, and account status to an organization's purchasing
agents.


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2.

Enterprise Application Integration


a.

Using
Figure 10.22
, indicate whether or not

each of the attributes of artificially intelligent behavior
applies to Amazon.com's case based reasoning system.


Students' answers will vary, however the results should differ meaningfully from the portal's default
settings.


b.

For those attributes tha
t apply as indicated by your answers above, explain how Amazon.com's system
creates that behavior. For example, Amazon.com handles ambiguous, incomplete, or erroneous
information by linking its recommendations to a specific book rather than to the user's
search terms. In
short, Amazon.com's system works to reduce ambiguity by forcing a user to select a specific book first.


Students' answers will vary depending on the product and the review. One business EAI provider,
www.sunopsis.com
, allows not only access to disparate systems but also allows analysis between data
elements across these systems. For example, an executive may choose to see if a relationship exists between

overdue accounts (accounts receivable) and

shipping delays (shipping).


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3.

Case Based System Sells Books on Amazon


a.

Using
Figure 10.22
, indicate whether or not each of the attributes of artificially intelligent behavior
applies to Amazon.com's case bas
ed reasoning system.

Attribute

Applies to Amazon

Think and reason

no

Use reason to solve problems

no


or very rudimentary

Learn from experience

yes

Acquire and apply knowledge

yes

Exhibit creativity

nothing outside the bounds of its rules

Deal with
complex issues

no

Respond quickly to
new

situations

no (but does OK for
almost

new)

Recognize relative importance

no (simple tallies)

Handle ambiguous information

yes (sort of)


b.

For those attributes that apply as indicated by your answers above, exp
lain how Amazon.com's system
creates that behavior. For example, Amazon.com handles ambiguous, incomplete, or erroneous
information by linking its recommendations to a specific book rather than to the user's search terms. In
short, Amazon.com's system wo
rks to reduce ambiguity by forcing a user to select a specific book first.

Attribute

Applies to Amazon

Think and reason

Does not apply. Case
-
based systems typically use very simple rules for
evaluating cases with modestly sophisticated systems for interp
olating
between near misses when no case matches exactly.

Use reason to solve
problems

Does not apply. At best, a case
-
base system might do a bit of
interpolation.

Learn from experience

Yes! Case
-
based systems learn from experience by storing example
s of
past behavior and then matching these examples to the current situation.

The more examples, the better the results.

Acquire and apply
knowledge

Yes! Amazon.com acquires knowledge through sales and applies this
knowledge through its case based reaso
ning engine.

Exhibit creativity

Does not apply. The system is no more creative than the evaluation
rules provided by the programmer. However, if customers have shown
creativity in the past (purchasing paper making books along with origami
books), then t
he systems results will also reflect this case driven bit of
creativity.

Respond quickly to
new

situations

Does not apply. The first time Amazon.com offers a title for sale, the
case
-
based system has no prior cases to use and so produces no results.

How
ever, as soon as Amazon.com completes a few sales, the system
can begin offering recommendations.

Recognize relative
importance

Does not apply. Amazon's system uses simple tallies to determine
relative importance.

Handle ambiguous
information

Rather

than relying on a natural language search, Amazon simply
provides results for a very specific book title. The system does not
permit ambiguous information. However, if the user has selected the
wrong book, Amazon's system will provide results appropriat
e to the
incorrect selection. That is, Amazon's results probably won't help much.







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4.

Palm City Police Department


a.

Build a spreadsheet to perform the analysis described above and print it out.


b.

Currently, no funds are available to hire additi
onal officers. Based on the citywide ratios, the department
has decided to develop a plan to shift resources as needed in order to ensure that no precinct has more than
1,100 residents per police officer and no precinct has more than seven violent crimes
per police officer.
The department will transfer officers from precincts that easily meet these goals to precincts that violate
one or both of these ratios. Use "goal seeking" on your spreadsheet to move police officers between
precincts until the goals
are met. You can use the goal seek function to see how many officers would be
required to bring each precinct into compliance and then judgmentally reduce officers in precincts that
are substantially within the criteria. Print out a set of results that a
llow the departments to comply with
these ratios and a memorandum to your instructor summarizing your results and the process you used to
develop them.


[See Data/Solutions File


Ch 10


Exercise 4.xls
]






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IX. ANSWERS TO REAL WORLD CASES



RWC 1: KeyCorp, Allstate Insurance, Aviva Canada, and Others:

Centralized Business Intelligence At Work


1.

What is business intelligence? Why are business intelligence systems such a popular business
application of IT?



Business intelligence (BI) is a broad category of applications and technologies for gathering, storing,
analyzing, and providing access to data to help enterprise users make better business decisions. One reason
for its popularity is the high visibility

of the data it makes available to business units to be used in decision
making. Data from BI centers supports sales forecasting, financial projections, CRM solutions, new product
development, etc.


2.

What is the business value of the various BI applications

discussed in the case?


Examples in the case include:



Information integration across business units or applications



Support of new initiatives and customer relationship management



Support for integration in an M&A environment



Reuse data
-
mappings (links be
tween data and its source)



Provide a comprehensive picture of the competitive environment, with information from both
company and competitors



3.

Is a business intelligence system an MIS or a DSS?


To the extent that Decision Support Systems provide one or m
ode logical models embedded within them to
support decision making, business intelligence is an MIS. BI applications support the processes of
organizing, categorizing and accessing data; however, all decision making capabilities rest within the user.



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RWC

2: Wal
-
Mart, BankFinancial, and HP: The Business Value of AI


1.

What is the business value of AI technologies in business today? Use several examples from the case to
illustrate your answer.


Business values of AI technologies would be illustrated by these

examples:




AI software helps engineers create better jet engines.



AI technology boosts productivity by monitoring equipment and signaling when preventive
maintenance is needed.



It is used to gain new insights into the tremendous amount of data on the huma
n genome.



Use of neural networks for detecting credit
-
card fraud.



Used to qualify for debit card insurance.



Shifts through a deluge of data to uncover patterns and relationships that would elude an army of
human searches.



Predicting customer behavior for c
ompanies such as banks.


2.

What are some of the benefits and limitations of data mining for business intelligence? Use
BankFinancial’s experience to illustrate your answer.


Benefits would include:




Potential for mining cost
-
savings and revenue
-
boosting idea
s.



More accurately target promotions to customers and prospects.



Helping users set up predictive models.



Reduce the time it takes the bank to develop a model by 50% to 70%.



Developing applications such as a model to predict customer “churn,” the rate at wh
ich customers
come and go.



3.

Why have banks and other financial institutions been leading users of AI technologies like neural
networks? What are the benefits and limitations of this technology?


Why banks and other financial institutions have used AI (
benefits) would include:




Detecting credit
-
card fraud.



Use of predictive models to understand customer behavior.



Revenue enhancing.



Cost reduction.


Limitations would include:



Biggest stumbling block is getting the data.



Accessing the correct data needed f
or predictive models (limited to only account data prepared
weekly and monthly when daily customer activity data is needed).



Dealing with disparate data sources.



Systems integration and interface work is needed.


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RWC 3: Proctor & Gamble and Others: Usin
g Agent
-
Based Modeling

for Supply Chain Management


1.

Do you agree with Proctor & Gamble that a supply chain should be called a supply network? Why or why
not?


Discussion points that students should develop would include:




A “supply network” is more complex

than what was intended when “supply chain” was coined to
describe the activities of a company with its customers, suppliers, and other business partners.



Agent
-
based modeling is more sophisticated and involves more advanced applications of IT that
“chain”

no longer adequately describes the supply management for a company such as P&G.



Computer modeling adds an additional dimension to a traditional supply chain management
system.


2.

What is the business value of agent
-
based modeling? Use P&G and other companie
s in this case as
examples.


Discussion points would include:




P&G performs what
-
if simulations to test the impact of new logistics rules on three key metrics:
inventory levels, transportation costs and in
-
store stock
-
outs.



The model convinced P&G to rela
x rigid rules in order to improve overall performance of the supply
network.



The model convinced P&G that cultural changes, such as convincing freight managers that it’s
sometimes OK to let a truck go half full, is good.



There is a need for more flexibilit
y in the manufacturing operations to reduce stock
-
outs and keep
customers happier.



There is a need for more flexibility in distribution.



Southwest Airlines used agent
-
based modeling to optimize cargo routing.



Air Liquide America LP reduced production and

distribution costs with agent
-
based modeling.



Merck & Co. used it to help find more efficient ways to distribute anti
-
HIV drugs in Zimbabwe.



Ford Motor Co. used it to simulate buyer preferences to optimize the trade
-
offs between production
costs and custo
mer demands.



Edison Chouest Offshore LLC optimized its deployment of service and supply vessels in the Gulf
of Mexico.


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3.

Visit the website of NuTech Solutions. How does NuTech use AI techniques to help companies gain
“adaptive” business intelligence? Give

several examples from the website case studies.



Examples from site could include:




Branch Banking and Trust


ARROW.



ChevronTexaco


scheduling optimization..



DaimlerChrysler Aerospace


engineering design optimization.



Dutch Ministry of Traffic


scheduling optimization.



F. E. Bording


supply network optimization.



General Motors


vehicle distribution system.



U. S. Internal Revenue Service


expert system development.



Kraftwerksunion (KWU)


scheduling optimization.



Major Automaker


marketing diffusion.



Major Producer


resource allocation optimization.



Major Telecom Company


engineering design optimization.



Major U. S. Automaker


data mining.



Major U. S. Bank


vehicle distribution system.



NASA


modeling and simu
lation.



Nasdaq


modeling and simulation.


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RWC 4: Boehringer Ingelheim: Using Web
-
Based Tools for Financial Analysis and Reporting


1.

What are the business benefits and limitations of Boehringer’s Web
-
based financial analysis and
reporting systems?


Discuss
ion points would include:



Rapidly consolidate and present key financial data on a daily, weekly or monthly basis which
allows it to drill down and draw conclusions based on the latest available financial and operational
data.



Boehringer is now able to clos
e its books for most of its divisions just two hours after the close of
business at the end of each month vs. a three day requirement in the past.



The accounting department can spot product sales trends and track expenses quickly.



Boehringer can create mul
tidimensional views of profit and loss data.



2.

Which of Boehringer’s financial analysis and reporting systems are MIS tools? DSS tools? Why?


Students should present discussion that would include consideration of:



Decision support provided

o

MIS


provide in
formation about the performance of Boehringer.

o

DSS


provide information and decision support techniques to analyze specific
problems or opportunities.



Information form and frequency

o

MIS


periodic, exception, demand, and push reports and responses.

o

DSS


interactive inquiries and responses.



Information format

o

MIS
-

Prespecified, fixed format

o

DSS


ad hoc, flexible, and adaptable format



Information processing methodology

o

MIS


information produced by extraction and manipulation of business data.

o

DSS


infor
mation produced by analytical modeling of business data.



3.

How could the Cognos tools used by Boehringer be used for marketing and other business analysis and
reporting applications? Visit the Cognos website to help you answer.


Discussion points would inc
lude:



Use of the Cognos tools by the marketing staff to increase Boehringer’s competitive position.



Use of the Cognos tools by the marketing staff to improve their customer relationship management
system.



Training of the marketing staff to use the Cognos t
ools.



Ability of Boehringer’s IT staff to implement the system in all divisions.