Download O'Brien MIS, 6th ed. - Dr. Leopoldo Gemoets

1
Chapter
8
Decision Support Systems
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Learning Objectives
Identify
the changes taking place in the form
and use of decision support in e-business
enterprises.
Identify
the role and reporting alternatives of
management information systems.
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Learning Objectives (continued)
Describe
how online analytical processing can
meet key information needs of managers.
Explain
the decision support system concept
and how it differs from traditional
management information systems.
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Learning Objectives (continued)
Explain
how the following information systems
can support the information needs of
executives, managers, and business
professionals:
Executive information systems
Enterprise information portals
Enterprise knowledge portals
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Learning Objectives (continued)
Identify
how neural networks, fuzzy logic,
genetic algorithms, virtual reality, and
intelligent agents can be used in business.
How
can expert systems be used in business
decision-making situations?
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6
Section I
Decision Support in Business
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Business and Decision Support
To
succeed, companies need information
systems that can support the diverse
information and decision-making needs of
their managers and business professionals.
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Business and Decision Support (continued)
Information,
Decisions, & Management
The
type of information required by decision
makers is directly related to the level of
management and the amount of structure in
the decision situations.
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Business and Decision Support (continued)
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Business and Decision Support (continued)
Information
Quality
Timeliness
Provided
WHEN it is needed
Up-to-date when it is provided
Provided as often as needed
Provided about past, present, and future
time periods as necessary
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Business and Decision Support (continued)
 Information
Quality (continued)
 Content
 Free
from errors
 Should be related to the information needs of a
specific recipient for a specific situation
 Provide all the information that is needed
 Only the information that is needed should be
provided
 Can have a broad or narrow scope, or an internal
or external focus
 Can reveal performance
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Business and Decision Support (continued)
 Information
Quality (continued)
 Form
 Provided
in a form that is easy to understand
 Can be provided in detail or summary form
 Can be arranged in a predetermined sequence
 Can be presented in narrative, numeric, graphic,
or other forms
 Can be provided in hard copy, video, or other
media.
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Business and Decision Support (continued)
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Business and Decision Support (continued)
Decision
Structure
Structured decisions
Involve situations where the procedures to
be followed can be specified in advance
Unstructured decisions
Involve situations where it is not possible
to specify most of the decision procedures
in advance
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Business and Decision Support (continued)
Decision
structure (continued)
Semistructured
decisions
Some decision procedures can be specified
in advance, but not enough to lead to a
definite recommended decision
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Business and Decision Support (continued)
Amount
of structure is typically tied to
management level
Operational – more structured
Tactical – more semistructured
Strategic – more unstructured
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Decision Support Trends
The
growth of corporate intranets, extranets
and the Web has accelerated the development
and use of “executive class” information
delivery & decision support software tools to
virtually every level of the organization.
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Management Information Systems
The
original type of information system
Produces many of the products that support
day-to-day decision-making
These information products typically take the
following forms:
Periodic scheduled reports
Exception reports
Demand reports and responses
Push reports
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Management Information Systems (continued)
Management
reporting alternatives
Periodic scheduled reports
Prespecified format
Provided on a scheduled basis
Exception reports
Produced only when exceptional
conditions occur
Reduces information overload
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Management Information Systems (continued)
Management
reporting alternatives
(continued)
Demand reports and responses
Available when demanded.
Ad hoc
Push reports
Information is sent to a networked PC
over the corporate intranet.
Not specifically requested by the recipient
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Online Analytical Processing
Enables
managers and analysts to interactively
examine & manipulate large amounts of
detailed and consolidated data from many
perspectives
Analyze complex relationships to discover
patterns, trends, and exception conditions
Real-time
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Online Analytical Processing (continued)
Involves..
Consolidation
The
aggregation of data.
From simple roll-ups to complex
groupings of interrelated data
Drill-Down
Display detail data that comprise
consolidated data
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Online Analytical Processing (continued)
Slicing
and Dicing
The ability to look at the database from
different viewpoints.
When performed along a time axis, helps
analyze trends and find patterns
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Decision Support Systems
Computer-based
information systems that
provide interactive information support
during the decision-making process
DSS’s use
Analytical models
Specialized databases
The decision maker’s insights & judgments
An interactive, computer-based modeling
process to support making semistructured
and unstructured business decisions
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Decision Support Systems (continued)
Designed
to be ad hoc, quick-response systems
that are initiated and controlled by the
decision maker
DSS
Models and Software
Rely on model bases as well as databases
Might include models and analytical
techniques used to express complex
relationships
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Decision Support Systems (continued)
DSS
models and software (continued)
Can combine model components to create
integrated models in support of specific
types of business decisions
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Decision Support Systems (continued)
Geographic
Information & Data Visualization
Systems
Special categories of DSS that integrate
computer graphics with other DSS features
GIS
A DSS that uses geographic databases to
construct and display maps and other
graphics displays
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Decision Support Systems (continued)
Geographic
information and data visualization
systems (continued)
Data
visualization systems
Represent complex data using interactive
three-dimensional graphic forms
Helps discover patterns, links, and
anomalies
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Using Decision Support Systems
An
interactive modeling process
Four types of analytical modeling
What-if analysis
Sensitivity analysis
Goal-seeking analysis
Optimization analysis
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Using Decision Support Systems (continued)
What-If Analysis
End
user makes changes to variables, or
relationships among variables, and observes
the resulting changes in the values of other
variables
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Using Decision Support Systems (continued)
Sensitivity Analysis
A
special case of what-if analysis
The value of only one variable is changed
repeatedly, and the resulting changes on
other variables are observed
Typically used when there is uncertainty
about the assumptions made in estimating
the value of certain key variables
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Using Decision Support Systems (continued)
Goal-Seeking Analysis
Instead
of observing how changes in a
variable affect other variables, goal-seeking
sets a target value (a goal) for a variable,
then repeatedly changes other variables until
the target value is achieved
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Using Decision Support Systems (continued)
Optimization Analysis
A
more complex extension of goal-seeking
The goal is to find the optimum value for one
or more target variables, given certain
constraints
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Using Decision Support Systems (continued)
Data
Mining for Decision Support
Software analyzes vast amounts of data
Attempts to discover patterns, trends, &
correlations
May perform regression, decision tree,
neural network, cluster detection, or market
basket analysis
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Executive Information Systems
EIS’s
combine many of the features of MIS
and DSS
Originally intended to provide top executives
with immediate, easy access to information
about the firm’s “critical success factors”
Alternative names
Enterprise information systems
Executive support systems
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Executive Information Systems (continued)
Features
of an EIS
Information presented in forms tailored to
the preferences of the users
Most stress use of graphical user interface
and graphics displays
May also include exception reporting and
trend analysis
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Enterprise Portals and Decision Support
A Web-based
interface and integration of
intranet and other technologies that gives all
intranet users and selected extranet users
access to a variety of internal & external
business applications and services
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Enterprise Portals and Decision Support (continued)
Business
benefits
More specific and selective information
Easy access to key corporate intranet
website resources
Industry and business news
Access to company data for stakeholders
Less time spent on unproductive surfing
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Knowledge Management Systems
IT
that helps gather, organize, and share
business knowledge within an organization
Hypermedia databases that store and
disseminate business knowledge. May also be
called knowledge bases
Best practices, policies, business solutions
Entered through the enterprise knowledge
portal
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Section II
Artificial Intelligence Technologies in Business
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Business and AI
“Designed
to leverage the capabilities of
humans rather than replace them,…AI
technology enables an extraordinary array of
applications that forge new connections among
people, computers, knowledge, and the
physical world.”
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Artificial Intelligence
A
field of science and technology based on
disciplines such as computer science, biology,
psychology, linguistics, mathematics, &
engineering
Goal is to develop computers that can think,
see, hear, walk, talk, and feel
Major thrust – development of computer
functions normally associated with human
intelligence – reasoning, learning, problem
solving
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Artificial Intelligence (continued)
Domains
of AI
Three major areas
Cognitive science
Robotics
Natural interfaces
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Artificial Intelligence (continued)
Cognitive
science
Focuses on researching how the human
brain works & how humans think and learn
Applications
Expert systems
Adaptive learning systems
Fuzzy logic systems
Neural networks
Intelligent agents
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Artificial Intelligence (continued)
Robotics
Produces
robot machines with computer
intelligence and computer controlled,
humanlike physical capabilities
Natural interfaces
Natural language and speech recognition
Talking to a computer and having it
understand
Virtual reality
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Neural Networks
Computing
systems modeled after the brain’s
meshlike network of interconnected processing
elements, called neurons
Goal – the neural network learns from data it
processes
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Fuzzy Logic Systems
A
method of reasoning that resembles human
reasoning
Allows for approximate values and inferences
Allows for incomplete or ambiguous data
Allows “fuzzy” systems to process incomplete
data and provide approximate, but acceptable,
solutions to problems
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Genetic Algorithms
Uses
Darwinian, randomizing, & other
mathematical functions to simulate an
evolutionary process that can yield
increasingly better solutions
Especially useful for situations in which
thousands of solutions are possible & must be
evaluated
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Virtual Reality
Computer-simulated
reality
Relies on multisensory input/output devices
Allows interaction with computer-simulated
objects, entities, and environments in three
dimensions
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Intelligent Agents
A “software
surrogate” for an end user or a
process that fulfills a stated need or activity
Uses built-in and learned knowledge base
about a person or process to make decisions
and accomplish tasks
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Expert Systems
A
knowledge-based information system that
uses its knowledge about a specific, complex
application area to act as an expert consultant
Provides
answers to questions in a very
specific problem area
Must
be able to explain reasoning process and
conclusions to the user
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Expert Systems (continued)
Components
Knowledge
base
Software resources
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Expert Systems (continued)
Knowledge
base
Contains
Facts
about a specific subject area
Heuristics that express the reasoning
procedures of an expert on the subject
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Expert Systems (continued)
Software
Resources
Contains an inference engine and other
programs for refining knowledge and
communicating
Inference engine processes the
knowledge, and makes associations
and inferences
User interface programs, including an
explanation program, allows
communication with user
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Developing Expert Systems
Begin
with an expert system shell
Add the knowledge base
Built
by a “knowledge engineer”
Works with experts to capture their
knowledge
Works with domain experts to build the
expert system
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The Value of Expert Systems
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The Value of Expert Systems (continued)
Benefits
Can
outperform a single human expert in
many problem situations
Helps preserve and reproduce knowledge of
experts
Limitations
Limited
focus, inability to learn,
maintenance problems, developmental costs
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Discussion Questions
Is
the form and use of information and
decision support in e-business changing and
expanding?
Has
the growth of self-directed teams to
manage work in organizations changed the
need for strategic, tactical, and operational
decision making in business?
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Discussion Questions (continued)
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?
In
what ways does using an electronic
spreadsheet package provide you with the
capabilities of a decision support system?
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Discussion Questions (continued)
Are
enterprise information portals making
executive information systems unnecessary?
Can
computers think? Will they EVER be
able to?
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Discussion Questions (continued)
What
are some of the most important
applications of AI in business?
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?
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Real World Case 1 – AmeriKing & Others
 AmeriKing’s
old system
 Relied on an antiquated corporate information
system.
 Involved mailing or faxing paper reports to
managers.
 AmeriKing’s new system
 An intranet-based enterprise information portal
 Enables employees to use Web browsers to
instantly access financial, marketing, human
resource, and other reports.
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Real World Case 1 (continued)
What
is the business value to a company of an
enterprise portal like AmeriKing’s?
What
are several ways AmeriKing could
improve the business value of its portal?
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Real World Case 1 (continued)
How
might an enterprise portal help you as a
business professional or manager in your work
activities?
Is
it becoming necessary for all companies to
provide an enterprise information portal to
their employees?
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Real World Case 2 – BAE Systems
Problems
Wasted
time trying to find information to do
the job.
Duplication of effort
Information overload
Inadequate search capability
Solution
An intranet-based knowledge management
system
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Real World Case 2 (continued)
What
problems was BAE having in knowledge
sharing? Are such problems common to many
companies?
How
does BAE’s knowledge management
system help solve such problems?
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Real World Case 2 (continued)
What
are some of the business benefits and
potential limitations of BAE’s knowledge
management system?
What
is the difference between a corporate
intranet and a knowledge management
system? What is the difference in their
business value?
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Real World Case 3 – Cisco Systems, NetFlix, & Office Depot
What
are the business benefits and limitations
of Cisco’s Web-based system for its channel
managers?
Do
you agree that NetFlix’s real-time
personalization system is critical to their
success?
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Real World Case 3 (continued)
Do
you think salespeople will appreciate and
benefit from the real-time alert system
envisioned for Office Depot?
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Real World Case 4 – Producers Assistance, Kinko’s, &
Champion Printing
Using
Spatial Information Systems to…
Find workers
Find services
Find customers
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Real World Case 4 (continued)
What
is the business value of spatial
information systems?
How
else could spatial information systems be
used in business?
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Real World Case 4 (continued)
How
helpful is Kinko’s location finder service?
What else can they do to improve this spatial
information management application?
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Real World Case 5 – Schneider National
The
business value of business intelligence (BI)
“We
were drowning in data but starving for
information.”
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Real World Case 5 (continued)
What
problem was Schneider National having
with their business data?
How
did business intelligence solve the
problem?
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Real World Case 5 (continued)
What
are the benefits and limitations of
business intelligence software as demonstrated
by Schneider National?
What
is the business value of business
intelligence as defined by Cognos?
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