Transcript Intro to Information Systems

McGraw-Hill/Irwin
9-1
Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved.
Chapter
9
Decision Support Systems
Decision Support
MIS and DSS
Artificial Intelligence
Expert Systems
McGraw-Hill/Irwin
Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved.
Learning Objectives
1.
2.
3.
4.
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Identify the changes taking place in the form and
use of decision support in business.
Identify the role and reporting alternatives of
management information systems.
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.
Learning Objectives
5.
Explain how the following information systems can
support the information needs of executives,
managers, and business professionals:
Executive information systems
b. Enterprise information portals
c. Knowledge management systems
a.
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Learning Objectives
Identify how neural networks, fuzzy logic, genetic
algorithms, virtual reality, and intelligent agents
can be used in business.
6. Give examples of several ways expert systems can
be used in business decision-making situations.
5.
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Case 1: Centralized Business
Intelligence at Work
 Starting
each business-intelligence project from
scratch leads to
 Reinventing the
wheel
 High development and support costs
 Incompatible systems
 Some
companies are standardizing on fewer
business-intelligence tools and making them
available throughout the organization and
 Business-intelligence competency centers
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Case Study Questions
What is business intelligence? Why are business
intelligence systems such a popular business
application of IT?
2. What is the business value of the various BI
applications discussed in the case?
3. Is a business-intelligence system an MIS or a DSS?
1.
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Real World Internet Activity
1.
Companies are taking advantage of the competitive
edge they enjoy from high-quality business
intelligence. To meet the demand for applications
to support the process, vendors are developing a
wide variety of offerings. Using the Internet,
See if you can find several examples of software
products to support the management of business
intelligence.
 Do they all take the same approach, or are there
different ways of managing the process?

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Real World Group Activity
 Business
intelligence competency centers can be quite
costly to start and maintain. There prevalence,
however, suggests the benefits are worth the costs.
In small groups,
 Discuss
the various skills and job roles necessary for a
competitive business intelligence competency center.
 Can such centers be considered competitive advantage
or simply competitive necessity?
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Information required at different
management levels
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Levels of Management
Decision Making
 Strategic
management
 Executives
develop organizational goals, strategies,
policies, and objectives
 As part of a strategic planning process
 Tactical
management
 Managers
and business professionals in self-directed
teams
 Develop short- and medium-range plans, schedules
and budgets
 Specify the policies, procedures and business objectives
for their subunits
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Levels of Management
Decision Making
 Operational
 Managers
management
or members of self-directed teams
 Develop short-range plans such as weekly production
schedules
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Information Quality
 Information
products whose characteristics,
attributes, or qualities make the information more
value
 Information has 3 dimensions:
 Time
 Content
 Form
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Attributes of Information Quality
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Decision Structure
 Structured
– situations where the procedures to
follow when a decision is needed can be specified in
advance
 Unstructured – decision situations where it is not
possible to specify in advance most of the decision
procedures to follow
 Semistructured - decision procedures that can be
prespecified, but not enough to lead to a definite
recommended decision
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Information Systems to support
decisions
Decision support
provided
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Management
Information Systems
Decision Support
Systems
Provide information about the
performance of the
organization
Provide information and
techniques to analyze
specific problems
Information form Periodic, exception, demand,
and frequency
and push reports and responses
Interactive inquiries and
responses
Information
format
Prespecified, fixed format
Ad hoc, flexible, and
adaptable format
Information
processing
methodology
Information produced by
extraction and manipulation of
business data
Information produced by
analytical modeling of
business data
Decision Support Trends
 Personalized
proactive decision analytics
 Web-Based applications
 Decisions at lower levels of management and by
teams and individuals
 Business intelligence applications
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Business Intelligence Applications
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Decision Support Systems
 DSS
 Provide
interactive information support to managers
and business professionals during the decisionmaking process
 Use:
 Analytical
models
 Specialized databases
 A decision maker’s own insights and judgments
 Interactive computer-based modeling
 To
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support semistructured business decisions
DSS components
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DSS Model base
 Model
base
A
software component that consists of models used in
computational and analytical routines that
mathematically express relations among variables
 Examples:
 Linear
programming models,
 Multiple regression forecasting models
 Capital budgeting present value models
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Management Information
Systems
 MIS
 Produces
information products that support many of
the day-to-day decision-making needs of managers
and business professionals
 Prespecified reports, displays and responses
 Support more structured decisions
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MIS Reporting Alternatives
 Periodic
Scheduled Reports
 Prespecified
 Exception
format on a regular basis
Reports
 Reports
about exceptional conditions
 May be produced regularly or when exception occurs
 Demand
Reports and Responses
 Information
 Push
Reporting
 Information
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available when demanded
pushed to manager
Online Analytical Processing
 OLAP
 Enables
mangers and analysts to examine and
manipulate large amounts of detailed and consolidated
data from many perspectives
 Done interactively in real time with rapid response
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OLAP Analytical Operations
 Consolidation
 Aggregation
of data
 Drill-down
 Display
 Slicing
detail data that comprise consolidated data
and Dicing
 Ability
to look at the database from different
viewpoints
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OLAP Technology
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Geographic Information
Systems
 GIS
 DSS
that uses geographic databases to construct and
display maps and other graphics displays
 That support decisions affecting the geographic
distribution of people and other resources
 Often used with Global Position Systems (GPS) devices
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Data Visualization Systems
 DVS
 DSS
that represents complex data using interactive
three-dimensional graphical forms such as charts,
graphs, and maps
 DVS tools help users to interactively sort, subdivide,
combine, and organize data while it is in its graphical
form.
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Using DSS
 What-if
Analysis
 End
user makes changes to variables, or relationships
among variables, and observes the resulting changes in
the values of other variables
 Sensitivity
 Value
Analysis
of only one variable is changed repeatedly and
the resulting changes in other variables are observed
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Using DSS
 Goal-Seeking
 Set
a target value for a variable and then repeatedly
change other variables until the target value is achieved
 How can analysis
 Optimization
 Goal
is to find the optimum value for one or more
target variables given certain constraints
 One or more other variables are changed repeatedly
until the best values for the target variables are
discovered
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Data Mining
 Main
purpose is to provide decision support to
managers and business professionals through
knowledge discovery
 Analyzes vast store of historical business data
 Tries to discover patterns, trends, and correlations
hidden in the data that can help a company improve
its business performance
 Use regression, decision tree, neural network, cluster
analysis, or market basket analysis
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Market Basket Analysis
 One
of most common data mining for marketing
 The purpose is to determine what products
customers purchase together with other products
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Executive Information Systems
 EIS
 Combine
many features of MIS and DSS
 Provide top executives with immediate and easy access
to information
 About the factors that are critical to accomplishing an
organization’s strategic objectives (Critical success
factors)
 So popular, expanded to managers, analysts and other
knowledge workers
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Features of an EIS
 Information
presented in forms tailored to the
preferences of the executives using the system
 Customizable
graphical user interfaces
 Exception reporting
 Trend analysis
 Drill down capability
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Enterprise Interface Portals
 EIP
 Web-based
interface
 Integration of MIS, DSS, EIS, and other technologies
 Gives all intranet users and selected extranet users
access
 To a variety of internal and external business
applications and services
 Typically
tailored to the user giving them a
personalized digital dashboard
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Enterprise Information Portal
Components
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Knowledge Management
Systems
 The
use of information technology to help gather,
organize, and share business knowledge within an
organization
 Enterprise
 EIPs
Knowledge Portals
that are the entry to corporate intranets that serve
as knowledge management systems
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Enterprise Knowledge Portals
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Case 2 Artificial Intelligence
The Dawn of the Digital Brain
 Numenta
will translate the way the brain works into
an algorithm that can run on a new type of computer
 The human brain does not work like a computer
 Intelligence, according to Hawkins, is pattern
recognition
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Case Study Questions
What is the business value of AI technologies in
business today? What value might exist if Jeff
Hawkins can build a machine to think like
humans?
2. Why has artificial intelligence become so important
to business?
3. Why do you think banks and other financial
institutions are leading users of AI technologies?
What are the benefits and limitations of this
technology?
1.
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Real World Internet Activity
1.
The concept of human thought is still a mystery
despite the development of our understanding of
the fundamental processes of the human brain. For
many years, scientists have worked hard to develop
humanlike machines, but none have been able to
perform as well as the human brain when it comes
to reasoning. Using the Internet,
See if you can find evidence of other projects similar
to that of Hawkins.
 What is the current state of the art in this area of
research and development?

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Real World Group Activity
 The
case ends by asking the question of whether we
can ever build a machine more intelligent than a
human. The real question is what will we do with it,
or with us, if we are successful. In small groups,
 Brainstorm
about a future with machines that can equal
or exceed the intelligence of humans.
 What good would come of such an accomplishment?
 What potential risks might occur?
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Artificial Intelligence (AI)
A
field of science and technology based on
disciplines such as computer science, biology,
psychology, linguistics, mathematics, and
engineering
 Goal is to develop computers that can simulate the
ability to think, as well as see, hear, walk, talk, and
feel
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Attributes of Intelligent Behavior
 Think and reason
 Use reason to solve problems
 Learn or understand from experience
 Acquire and apply knowledge
 Exhibit creativity and imagination
 Deal with complex or perplexing situations
 Respond quickly and successfully to new situations
 Recognize the relative importance of elements in a
situation
 Handle ambiguous, incomplete, or erroneous
information
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Domains of Artificial
Intelligence
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Cognitive Science
 Based
in biology, neurology, psychology, etc.
 Focuses on researching how the human brain works
and how humans think and learn
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Robotics
 Based
in AI, engineering and physiology
 Robot machines with computer intelligence and
computer controlled, humanlike physical capabilities
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Natural Interfaces
 Based
in linguistics, psychology, computer science,
etc.
 Includes natural language and speech recognition
 Development of multisensory devices that use a
variety of body movements to operate computers
 Virtual reality
 Using
multisensory human-computer interfaces that
enable human users to experience computer-simulated
objects, spaces and “worlds” as if they actually exist
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Expert Systems
 ES
A
knowledge-based information system (KBIS) that
uses its knowledge about a specific, complex
application to act as an expert consultant to end users
 KBIS
is a system that adds a knowledge base to the
other components on an IS
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Expert System Components
 Knowledge
Base
 Facts
about specific subject area
 Heuristics that express the reasoning procedures of an
expert (rules of thumb)
 Software
Resources
 Inference
engine processes the knowledge and makes
inferences to make recommend course of action
 User interface programs to communicate with end user
 Explanation programs to explain the reasoning process
to end user
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Expert System Components
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Methods of Knowledge
Representation
 Case-Based –
knowledge organized in form of cases
 Cases:
examples of past performance, occurrences and
experiences
 Frame-Based
– knowledge organized in a hierarchy
or network of frames
 Frames:
entities consisting of a complex package of
data values
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Methods of Knowledge
Representation
 Object-Based –
objects
knowledge organized in network of
 Objects:
data elements and the methods or processes
that act on those data
 Rule-Based
– knowledge represented in rules and
statements of fact
 Rules:
statements that typically take the form of a
premise and a conclusion
 Such as, If (condition) then (conclusion)
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Expert System Benefits
 Faster
and more consistent than an expert
 Can have the knowledge of several experts
 Does not get tired or distracted by overwork or stress
 Helps preserve and reproduce the knowledge of
experts
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Expert System Limitations
 Limited
focus
 Inability to learn
 Maintenance problems
 Developmental costs
 Can only solve specific types of problems in a limited
domain of knowledge
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Suitability Criteria for Expert
Systems
 Domain:
subject area relatively small and limited to
well-defined area
 Expertise: solutions require the efforts of an expert
 Complexity: solution of the problem is a complex
task that requires logical inference processing (not
possible in conventional information processing)
 Structure: solution process must be able to cope with
ill-structured, uncertain, missing and conflicting data
 Availability: an expert exists who is articulate and
cooperative
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Development Tool
 Expert
System Shell
 Software
package consisting of an expert system
without its knowledge base
 Has inference engine and user interface programs
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Knowledge Engineer
A
professional who works with experts to capture
the knowledge they possess
 Builds the knowledge base using an iterative,
prototyping process
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Neural Networks
 Computing
systems modeled after the brain’s meshlike network of interconnected processing elements,
called neurons
 Interconnected processors operate in parallel and
interact with each other
 Allows network to learn from data it processes
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Fuzzy Logic
 Method
of reasoning that resembles human
reasoning
 Allows for approximate values and inferences and
incomplete or ambiguous data instead of relying only
on crisp data
 Uses terms such as “very high” rather than precise
measures
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Genetic Algorithms
 Software
that uses
 Darwinian
(survival of the fittest), randomizing, and
other mathematical functions
 To simulate an evolutionary process that can yield
increasingly better solutions to a problem
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Virtual Reality (VR)
 Computer-simulated
reality
 Relies on multisensory input/output devices such as
a
tracking headset with video goggles and stereo
earphones,
 a data glove or jumpsuit with fiber-optic sensors that
track your body movements, and
 a walker that monitors the movement of your feet
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Intelligent Agents
A
software surrogate for an end user or a process
that fulfills a stated need or activity
 Uses its built-in and learned knowledge base
 To make decisions and accomplish tasks in a way
that fulfills the intentions of a user
 Also
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called software robots or bots
User Interface Agents
 Interface
Tutors – observe user computer operations,
correct user mistakes, and provide hints and advice
on efficient software use
 Presentation – show information in a variety of
forms and media based on user preferences
 Network Navigation – discover paths to information
and provide ways to view information based on user
preferences
 Role-Playing – play what-if games and other roles to
help users understand information and make better
decisions
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Information Management
Agents
 Search
Agents – help users find files and databases,
search for desired information, and suggest and find
new types of information products, media, and
resources
 Information Brokers – provide commercial services to
discover and develop information resources that fit
the business or personal needs of a user
 Information Filters – receive, find, filter, discard,
save, forward, and notify users about products
received or desired
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Case 3: Robots are the common
denominator
 Telerobotic-assisted
medical procedures
 Flexible automobile body shop with wireless
inventory replenishment
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Case Study Questions
1.
2.
3.
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What is the current and future business value of
robotics?
Would you be comfortable with a robot performing
surgery on you? Why or why not?
The robots being used by Ford Motor Co. are
contributing to a streamlining of their supply chain.
What other applications of robots can you envision
to improve supply chain management beyond
those described in the case?
Real World Internet Activity
1.
Applications for robots are being explored in every
possible setting. Using the Internet,

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See if you can find some examples where robots have
been used to improve a process, reduce costs, or make
the impossible possible.
Real World Group Activity
 The
previous case in the chapter described the
development of a machine that could think just like
humans. Combined with advanced robotics, such a
machine could conceivably perform most actions as
well, or possibly better, than humans. In small
groups,
 Discuss
how the combination of advanced AI and
robotics could be used to create business value.
 What would we want such machines to be able to do or
not do?
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