Download Intro to Information Systems

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.
Levels of Management
Decision Making
 Strategic
management
 Executives develop organizational goals,
strategies, policies, and objectives
 Tactical management
 Develop short- and medium-range plans,
schedules and budgets for their subunits
 Operational management
 Develop short-range plans such as weekly
production schedules
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Decision Structure
 Unstructured –
decision situations where it is not
possible to specify in advance
 Semistructured - decision procedures that can be
prespecified, but not enough to lead to a definite
recommended decision
 Structured – situations where the procedures to
follow when a decision is needed can be specified in
advance
 Page 318 Fig 9.4
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Information Quality
 Information
has 3 dimensions:
 Time: currency, frequency
 Content: accuracy, completeness
 Form: clarity, order
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Business Intelligence Applications
9-5
Decision Support Systems
 Provide
interactive information support to business
professionals during the decision-making process
 To support semistructured business decisions
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Using DSS
4 Analytical models
 What-if
Analysis
 Sensitivity
Analysis
 Goal-Seeking
 Optimization
9-7
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
9-8
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
9-9
available when demanded
pushed to manager
Online Analytical Processing
OLAP Analytical Operations in real time
 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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Data Mining
 Main
purpose is to provide decision support to
business professionals through knowledge discovery
 Market Basket Analysis
 The
purpose is to determine what products customers
purchase together with other products
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Executive Information Systems
 Combine
many features of MIS, DSS & OLAP
 Customizable graphical user interfaces
 Exception reporting
 Trend analysis
 Drill down capability
9-12
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
9-13
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.
9-14
Section II: 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
9-17
Cognitive Science
 Based
in biology, neurology, psychology, etc.
 Focuses on researching how the human brain works
and how humans think and learn
9-18
Robotics
 Based
in AI, engineering and physiology
 Robot machines with computer intelligence and
computer controlled, humanlike physical capabilities
9-19
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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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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