Transcript 10-15 Using Decision Support Systems

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Chapter
10
Decision Support Systems
McGraw-Hill/Irwin
Copyright © 2008
2008,The
TheMcGraw-Hill
McGraw-HillCompanies,
Companies,Inc.
Inc.All
Allrights
rightsreserved.
reserved.
Levels of Managerial Decision Making
10-3
Information Quality
• Information products made more valuable by
their attributes, characteristics, or qualities
• Information that is outdated, inaccurate, or
hard to understand has much less value
• Information has three dimensions
• Time
• Content
• Form
10-4
Attributes of Information Quality
10-5
Decision Structure
• Structured (operational)
• The procedures to follow when decision
is needed can be specified in advance
• Unstructured (strategic)
• It is not possible to specify in advance
most of the decision procedures to follow
• Semi-structured (tactical)
• Decision procedures can be pre-specified,
but not enough to lead to the correct decision
10-6
Decision Support Systems
• Decision support systems use the following to
support the making of semi-structured business
decisions
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Analytical models
Specialized databases
A decision-maker’s own insights and judgments
An interactive, computer-based modeling process
• DSS systems are designed to be ad hoc,
quick-response systems that are initiated and
controlled by decision makers
10-7
DSS Model Base
• Model Base
• A software component that consists of
models used in computational and analytical
routines that mathematically express relations
among variables
• Spreadsheet Examples
• Linear programming
• Multiple regression forecasting
• Capital budgeting present value
10-8
Applications of Statistics and Modeling
• Supply Chain: simulate and optimize supply
chain flows, reduce inventory, reduce stock-outs
• Pricing: identify the price that maximizes
yield or profit
• Product and Service Quality: detect quality
problems early in order to minimize them
• Research and Development: improve quality,
efficacy, and safety of products and services
10-9
Management Information Systems
• The original type of information system
that supported managerial decision making
• Produces information products that support
many day-to-day decision-making needs
• Produces reports, display, and responses
• Satisfies needs of operational and tactical
decision makers who face structured decisions
10-10
Management Reporting Alternatives
• Periodic Scheduled Reports
• Prespecified format on a regular basis
• Exception Reports
• Reports about exceptional conditions
• May be produced regularly or when an
exception occurs
• Demand Reports and Responses
• Information is available on demand
• Push Reporting
• Information is pushed to a networked computer
10-11
Online Analytical Processing
• OLAP
• Enables managers and analysts to examine
and manipulate large amounts of detailed and
consolidated data from many perspectives
• Done interactively, in real time, with rapid
response to queries
10-12
Online Analytical Operations
• Consolidation
• Aggregation of data
• Example: data about sales offices rolled up
to the district level
• Drill-Down
• Display underlying detail data
• Example: sales figures by individual product
• Slicing and Dicing
• Viewing database from different viewpoints
• Often performed along a time axis
10-13
Geographic Information Systems
• GIS
• DSS uses geographic databases to construct
and display maps and other graphic displays
• Supports decisions affecting the geographic
distribution of people and other resources
• Often used with Global Positioning Systems
(GPS) devices
10-14
Using Decision Support Systems
• Using a decision support system involves
an interactive analytical modeling process
• Decision makers are not demanding
pre-specified information
• They are exploring possible alternatives
• What-If Analysis
• Observing how changes to selected variables
affect other variables
10-15
Using Decision Support Systems
• Sensitivity Analysis
• Observing how repeated changes to a single
variable affect other variables
• Goal-seeking Analysis
• Making repeated changes to selected variables
until a chosen variable reaches a target value
• Optimization Analysis
• Finding an optimum value for selected variables,
given certain constraints
10-16
Data Mining
• Provides decision support through knowledge
discovery
• Analyzes vast stores of historical business data
• Looks for patterns, trends, and correlations
• Goal is to improve business performance
• Types of analysis
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Regression
Decision tree
Neural network
Cluster detection
Market basket analysis
10-17
Market Basket Analysis
• One of the most common uses for data mining
• Determines what products customers purchase
together with other products
• Results affect how companies
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Market products
Place merchandise in the store
Lay out catalogs and order forms
Determine what new products to offer
Customize solicitation phone calls
10-18
Artificial Intelligence (AI)
• AI is a field of science and technology based on
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Computer science
Biology
Psychology
Linguistics
Mathematics
Engineering
• The goal is to develop computers than can
simulate the ability to think
• And see, hear, walk, talk, and feel as well
10-19
Attributes of Intelligent Behavior
• Some of the attributes of intelligent behavior
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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
10-20
Attributes of Intelligent Behavior
• Attributes of intelligent behavior (continued)
• Respond quickly and successfully to new
situations
• Recognize the relative importance of
elements in a situation
• Handle ambiguous, incomplete, or
erroneous information
10-21
Domains of Artificial Intelligence
10-22
Cognitive Science
• Applications in the cognitive science of AI
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Expert systems
Knowledge-based systems
Adaptive learning systems
Fuzzy logic systems
Neural networks
Genetic algorithm software
Intelligent agents
• Focuses on how the human brain works
and how humans think and learn
10-23
Robotics
• AI, engineering, and physiology are the basic
disciplines of robotics
• Produces robot machines with computer
intelligence and humanlike physical capabilities
• This area include applications designed to
give robots the powers of
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Sight or visual perception
Touch
Dexterity
Locomotion
Navigation
10-24
Natural Interfaces
• Major thrusts in the area of AI and the
development of natural interfaces
• Natural languages
• Speech recognition
• Virtual reality
• Involves research and development in
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Linguistics
Psychology
Computer science
Other disciplines
10-25
Latest Commercial Applications of AI
• Decision Support
• Helps capture the why as well as the what of
engineered design and decision making
• Information Retrieval
• Distills tidal waves of information into simple
presentations
• Natural language technology
• Database mining
10-26
Latest Commercial Applications of AI
• Virtual Reality
• X-ray-like vision enabled by enhanced-reality
visualization helps surgeons
• Automated animation and haptic interfaces
allow users to interact with virtual objects
• Robotics
• Machine-vision inspections systems
• Cutting-edge robotics systems
• From micro robots and hands and legs, to cognitive
and trainable modular vision systems
10-27
Expert Systems
• An Expert System (ES)
• A knowledge-based information system
• Contain knowledge about a specific, complex
application area
• Acts as an expert consultant to end users
10-28
Benefits of Expert Systems
• Captures the expertise of an expert or group of
experts in a computer-based information system
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Faster and more consistent than an expert
Can contain knowledge of multiple experts
Does not get tired or distracted
Cannot be overworked or stressed
Helps preserve and reproduce the knowledge
of human experts
10-29
Limitations of Expert Systems
• The major limitations of expert systems
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Limited focus
Inability to learn
Maintenance problems
Development cost
Can only solve specific types of problems
in a limited domain of knowledge
10-30
Developing Expert Systems
• Suitability Criteria for Expert Systems
• Domain: the domain or subject area of
the problem is small and well-defined
• Expertise: a body of knowledge, techniques,
and intuition is needed that only a few people
possess
• Complexity: solving the problem is a complex
task that requires logical inference processing
10-31
Developing Expert Systems
• Suitability Criteria for Expert Systems
• Structure: the solution process must be able
to cope with ill-structured, uncertain, missing,
and conflicting data and a changing problem
situation
• Availability: an expert exists who is articulate,
cooperative, and supported by the management
and end users involved in the development
process
10-32
Neural Networks
• Computing systems modeled after the brain’s
mesh-like network of interconnected processing
elements (neurons)
• Interconnected processors operate in parallel
and interact with each other
• Allows the network to learn from the data it
processes
10-33
Fuzzy Logic
• Fuzzy logic
• Resembles human reasoning
• Allows for approximate values and
inferences and incomplete or ambiguous data
• Uses terms such as “very high” instead of
precise measures
• Used more often in Japan than in the U.S.
• Used in fuzzy process controllers used in
subway trains, elevators, and cars
10-34
Example of Fuzzy Logic Rules and Query
10-35
Virtual Reality (VR)
• Virtual reality is a computer-simulated reality
• Fast-growing area of artificial intelligence
• Originated from efforts to build natural, realistic,
multi-sensory human-computer interfaces
• Relies on multi-sensory input/output devices
• Creates a three-dimensional world through
sight, sound, and touch
• Also called telepresence
10-36
Typical VR Applications
• Current applications of virtual reality
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Computer-aided design
Medical diagnostics and treatment
Scientific experimentation
Flight simulation
Product demonstrations
Employee training
Entertainment
10-37
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
to make decisions and accomplish tasks in
a way that fulfills the intentions of a user
• Also call software robots or bots
10-38