Chapter 10 Decision Support Systems

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Transcript Chapter 10 Decision Support Systems

Chapter 10 Decision
Support Systems
James A. O'Brien, and George Marakas.
Management Information Systems with MISource
2007, 8th ed. Boston, MA: McGraw-Hill, Inc.,
2007. ISBN: 13 9780073323091
Decision Support in Business
Companies are investing in data-driven decision
support application frameworks to help them
respond to
 Changing market conditions
 Customer needs
This is accomplished by several types of
 Management information
 Decision support
 Other information systems
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Levels of Managerial Decision
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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 Information that is outdated, inaccurate, or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
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Attributes of Information Quality
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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
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Decision Support Systems
Information form
and frequency
Management Information
Decision Support
Provide information about the
performance of the organization
Provide information and
techniques to analyze
specific problems
Periodic, exception, demand,
and push reports and
Interactive inquiries and
Prespecified, fixed format
Ad hoc, flexible, and
adaptable format
Information produced by
extraction and manipulation of
business data
Information produced by
analytical modeling of
business data
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Decision Support Trends
The emerging class of applications focuses on
 Personalized decision support
 Modeling
 Information retrieval
 Data warehousing
 What-if scenarios
 Reporting
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Business Intelligence Applications
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Decision Support Systems
Decision support systems use the following to
support the making of semi-structured business
 Analytical models
 Specialized databases
 A decision-maker’s own insights and judgments
 An interactive, computer-based modeling
DSS systems are designed to be ad hoc,
quick-response systems that are initiated and
controlled by decision makers
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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
Spreadsheet Examples
 Linear programming
 Multiple regression forecasting
 Capital budgeting present value
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Applications of Statistics and
 Supply
Chain: simulate and optimize supply
chain flows, reduce inventory, reduce stockouts
 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
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Management Information
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
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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
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Online Analytical Processing
 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
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Online Analytical Operations
 Aggregation of data
 Example: data about sales offices rolled up
to the district level
 Display underlying detail data
 Example: sales figures by individual product
Slicing and Dicing
 Viewing database from different viewpoints
 Often performed along a time axis
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Geographic Information Systems
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
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Data Visualization Systems
Represents complex data using interactive,
three-dimensional graphical forms
(charts, graphs, maps)
Helps users interactively sort, subdivide,
combine, and organize data while it is in its
graphical form
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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
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
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Data Mining
Provides decision support through knowledge
 Analyzes vast stores of historical business data
 Looks for patterns, trends, and correlations
 Goal is to improve business performance
Types of analysis
 Regression
 Decision tree
 Neural network
 Cluster detection
 Market basket analysis
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Analysis of Customer
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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
 Market products
 Place merchandise in the store
 Lay out catalogs and order forms
 Determine what new products to offer
 Customize solicitation phone calls
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Executive Information Systems
Combines many features of MIS and DSS
Provide top executives with immediate and
easy access to information
Identify factors that are critical to accomplishing
strategic objectives (critical success factors)
So popular that it has been expanded to
managers, analysis, and other knowledge
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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 reports
 Trend analysis
 Drill down capability
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Enterprise Information Portals
An EIP is a Web-based interface and integration
of MIS, DSS, EIS, and other technologies
 Available to all intranet users and select
extranet users
 Provides access to a variety of internal and
external business applications and services
 Typically tailored or personalized to the user
or groups of users
 Often has a digital dashboard
 Also called enterprise knowledge portals
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Dashboard Example
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Enterprise Knowledge Portal
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Case 2 Automated Decision Making
Automated decision making has been slow
to materialize
 Early applications were just solutions looking
for problems, contributing little to improved
organizational performance
A new generation of AI applications
 Easier to create and manage
 Decision making triggered without human
 Can translate decisions into action quickly,
accurately, and efficiently
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Case 2 Automated Decision Making
AI is best suited for
 Decisions that must be made quickly and
frequently, using electronic data
 Highly structured decision criteria
 High-quality data
Common users of AI
 Transportation industry
 Hotels
 Investment firms and lenders
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Case Study Questions
Why did some previous attempts to use artificial
intelligence technologies fail?
 What key differences of the new AI-based
applications versus the old cause the authors
to declare that automated decision making is coming
of age?
What types of decisions are best suited for automated
decision making?
What role do humans plan in automated decision-making
 What are some of the challenges faced by managers
where automated decision-making systems are being
 What solutions are needed to meet such challenges?
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Artificial Intelligence (AI)
AI is a field of science and technology based on
 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
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Attributes of Intelligent Behavior
Some of the 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
 Recognize the relative importance of elements in
a situation
 Handle ambiguous, incomplete, or erroneous
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Domains of Artificial Intelligence
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Cognitive Science
Applications in the cognitive science of AI
 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
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AI, engineering, and physiology are the basic
disciplines of robotics
 Produces robot machines with computer
intelligence and humanlike physical
This area include applications designed to
give robots the powers of
 Sight or visual perception
 Touch
 Dexterity
 Locomotion
 Navigation
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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
 Linguistics
 Psychology
 Computer science
 Other disciplines
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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
 Natural language technology
 Database mining
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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
 Machine-vision inspections systems
 Cutting-edge robotics systems
 From micro robots and hands and legs, to
cognitive and trainable modular vision
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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
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Components of an Expert System
Knowledge Base
 Facts about a specific subject area
 Heuristics that express the reasoning
procedures of an expert (rules of thumb)
Software Resources
 An inference engine processes the knowledge
and recommends a course of action
 User interface programs communicate with
the end user
 Explanation programs explain the reasoning
process to the end user
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Components of an Expert System
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Methods of Knowledge
 Knowledge organized in the form of cases
 Cases are examples of past performance,
occurrences, and experiences
 Knowledge organized in a hierarchy or
network of frames
 A frame is a collection of knowledge about
an entity, consisting of a complex package
of data values describing its attributes
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Methods of Knowledge
 Knowledge represented as a network of
 An object is a data element that includes both
data and the methods or processes that act
on those data
 Knowledge represented in the form of rules
and statements of fact
 Rules are statements that typically take the
form of a premise and a conclusion (If, Then)
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Expert System Application
Decision Management
 Loan portfolio analysis
 Employee performance evaluation
 Insurance underwriting
 Equipment calibration
 Help desk operations
 Medical diagnosis
 Software debugging
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Expert System Application
 Computer option installation
 Manufacturability studies
 Communications networks
 Material selection
 Delinquent account identification
 Information classification
 Suspect identification
Process Monitoring/Control
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Expert System Application
Process Monitoring/Control
 Machine control (including robotics)
 Inventory control
 Production monitoring
 Chemical testing
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Benefits of Expert Systems
Captures the expertise of an expert or group of
experts in a computer-based information system
 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
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Limitations of Expert Systems
The major limitations of expert systems
 Limited focus
 Inability to learn
 Maintenance problems
 Development cost
 Can only solve specific types of problems
in a limited domain of knowledge
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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
 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
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Development Tool
Expert System Shell
 The easiest way to develop an expert system
 A software package consisting of an expert
system without its knowledge base
 Has an inference engine and user interface
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Knowledge Engineering
A knowledge engineer
 Works with experts to capture the knowledge
(facts and rules of thumb) they possess
 Builds the knowledge base, and if necessary,
the rest of the expert system
 Performs a role similar to that of systems
analysts in conventional information systems
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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
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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
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Example of Fuzzy Logic Rules
and Query
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Genetic Algorithms
Genetic algorithm software
 Uses Darwinian, randomizing, and other
mathematical functions
 Simulates an evolutionary process, yielding
increasingly better solutions to a problem
 Being uses to model a variety of scientific,
technical, and business processes
 Especially useful for situations in which
thousands of solutions are possible
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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
 Relies on multi-sensory input/output devices
 Creates a three-dimensional world through
sight, sound, and touch
 Also called telepresence
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Typical VR Applications
Current applications of virtual reality
 Computer-aided design
 Medical diagnostics and treatment
 Scientific experimentation
 Flight simulation
 Product demonstrations
 Employee training
 Entertainment
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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
to make decisions and accomplish tasks in
a way that fulfills the intentions of a user
 Also call software robots or bots
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User Interface Agents
Tutors – observe user computer
operations, correct user mistakes, provide
hints/advice on efficient software use
 Presentation Agents – show information in a
variety of forms/media based on user
 Network Navigation Agents – discover paths
to information, provide ways to view it based
on user preferences
 Role-Playing – play what-if games and other
roles to help users understand information and
make better decisions
 Interface
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Information Management Agents
Agents – help users find files and
databases, search for information, and suggest
and find new types of information products,
media, resources
 Information Brokers – provide commercial
services to discover and develop information
resources that fit business or personal needs
 Information Filters – Receive, find, filter,
discard, save, forward, and notify users about
products received or desired, including e-mail,
voice mail, and other information media
 Search
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Case 3 Centralized Business
A reinventing-the-wheel approach to business
intelligence implementations can result in
 High development costs
 High support costs
 Incompatible business intelligence systems
A more strategic approach
 Standardize on fewer business intelligence
 Make them available throughout the
organization, even before projects are
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Case 3 Centralized Business
About 10 percent of the 2,000 largest companies
have a business intelligence competency center
 Centralized or virtual
 Part of the IT department or independent
Cost reduction is often the driving force behind
creating competency centers and consolidating
business intelligence systems
 Despite the potential savings, funding for
creating and running a BI center can be an
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Case Study Questions
What is business intelligence?
 Why are business intelligence systems such
a popular business application of IT?
What is the business value of the various
BI applications discussed in the case?
Is the business intelligence system an MIS
or a DSS?
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Case 4 Robots, the Common
In early 2004, 22 patients underwent complex
laparoscopic operations
 The operations included colon cancer
procedures and hernia repairs
 The primary surgeon was 250 miles away
 A three-armed robot was used to perform the
 Left arm, right arm, camera arm
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Case 4 Robots, the Common
Automakers heavily use robotics
 Ford has a completely wireless assembly
 It also have a completely automated body
 BMW has two wireless plants in Europe and
is setting one up in the U.S.
 Vehicle tracking and material replenishment
are automated as well
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Case Study Questions
What is the current and future business value
of robotics?
Would you be comfortable with a robot
performing surgery on you?
The robotics being used by Ford Motor Co. are
contributing to a streamlining of its supply chain
 What other applications of robots can you
envision to improve supply chain
management beyond those described in the
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