Transcript 10-22 Using Decision Support Systems

Chapter 10 Decision Support
Systems
James A. O'Brien, and George Marakas
Management Information Systems, 9th ed.
Boston, MA: McGraw-Hill, Inc., 2009
ISBN: 13 9780073376769
McGraw-Hill/Irwin
Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.
Learning Objectives
1. Identify the changes taking place in the form and use of
decision support in business
2. Identify the role and reporting alternatives of management
information systems
3. Describe how online analytical processing can meet key
information needs of managers
4. Explain the decision support system concept and how it
differs from traditional management information systems
5. Explain how the following information systems can support
the information needs of executives, managers, and
business professionals: EIS, Enterprise information portals,
and KMS
6. Identify how neural networks, fuzzy logic, genetic algorithms,
virtual reality, and intelligent agents can be used in business
7. Give examples of several ways expert systems can be used
in business decision-making situations
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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
10-3
Case 1: Hillman Group, Avnet,
and Quaker Chemical
• BI refers to a variety of software applications used to analyze
an organization’s raw data (e.g., sales transactions) and
extract useful insights from them.
• BI projects can transform business processes. BI tools,
coupled with changes to business processes, can have a
significant impact on the bottom line.
• Major impediment to using BI that transforms business
processes is that most companies don’t understand their
business processes well enough to determine how to
improve them.
• Companies that use BI to uncover flawed business
processes are in a much better position to successfully
compete than those companies that use BI merely to monitor
what’s happening.
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Case Questions
1. What are the business benefits of BI deployments such as
those implemented by Avnet and Quaker Chemical? What
roles do data and business processes play in achieving those
benefits?
2. What are the main challenges to the change of mindset
required to extend BI tools beyond mere reporting? What can
companies do to overcome them? Use examples from the
case to illustrate your answer.
3. Both Avnet and Quaker Chemical implemented systems and
processes that affect the practices of their salespeople. In
which ways did the latter benefit from these new
implementations? How important was their buy-in to the
success of these projects? Discuss alternative strategies for
companies to foster adoption of new systems like these.
10-5
Levels of Managerial Decision
Making
10-6
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
Management Information
Systems
Decision Support
Systems
Decision
support
provided
Provide information about
the performance of the
organization
Provide information and
techniques to analyze
specific problems
Information
form and
frequency
Periodic, exception,
demand, and push reports
and responses
Interactive inquiries and
responses
Information
format
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
Information
processing
methodology
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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
10-11
Business Intelligence Applications
10-12
Decision Support Systems
• Decision support systems use the
following to support the making of semistructured business decisions
– 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
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DSS Components
10-14
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
10-15
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
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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
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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
(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
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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
10-20
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
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Data Visualization Systems
(DVS)
• 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 variables
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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
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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
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Analysis of Customer
Demographics
10-26
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
10-27
Executive Information Systems
(EIS)
– 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
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 reports
– Trend analysis
– Drill down capability
10-29
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
10-30
Enterprise Information Portal Components
10-31
Enterprise Knowledge Portal
10-32
Case 2: Goodyear, JEA, OSUMC,
and Monsanto
• Advanced technologies such as AI, mathematical simulations,
and robotics can have dramatic impacts on both business
processes and financial results.
• At Goodyear, designers can perform tests 10 times faster using
simulation, reducing a new tire’s time to market from two years
to as little as nine months.
• Public Utility Company JEA uses neural network technology to
automatically determine the optimal combinations of oil and
natural gas the utility’s boilers need to produce electricity cost
effectively, given fuel prices and the amount of electricity
required.
• The Ohio State University Medical Center (OSUMC) replaced
its overhead rail transport system with 46 self-guided robotic
vehicles to move linens, meals, trash, and medical supplies
throughout the 1,000-bed hospital.
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Case Study Questions
1. Consider the outcomes of the projects discussed in the case. In
all of them, the payoffs are both larger and achieved more rapidly
than in more traditional system implementations. Why do you
think this is the case? How are these projects different from
others you have come across in the past? What are those
differences? Provide several examples.
2. How do these technologies create business value for the
implementing organizations? In which ways are these
implementations similar in how they accomplish this, and how are
they different? Use examples from the case to support your
answer.
3. In all of these examples, companies had an urgent need that
prompted them to investigate these radical, new technologies. Do
you think the story would have been different had the companies
been performing well already? Why or why not? To what extent
are these innovations dependent on the presence of a problem or
crisis?
10-34
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
10-35
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
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
10-37
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
10-38
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
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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
–
–
–
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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 presentations
– Natural language technology
– Database mining
• 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
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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
10-44
Methods of Knowledge Representation
• Case-Based
– Knowledge organized in the form of cases
– Cases are examples of past performance,
occurrences, and experiences
• Frame-Based
– 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 Representation
• Object-Based
– Knowledge represented as a network of objects
– An object is a data element that includes both
data and the methods or processes that act on
those data
• Rule-Based
– 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 Categories
• Decision Management
– Loan portfolio analysis
– Employee performance evaluation
– Insurance underwriting
• Diagnostic/Troubleshooting
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–
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Equipment calibration
Help desk operations
Medical diagnosis
Software debugging
• Design/Configuration
– Computer option installation
– Manufacturability studies
– Communications networks
10-47
Expert System Application
Categories (cont’d)
• Selection/Classification
–
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Material selection
Delinquent account identification
Information classification
Suspect identification
• Process Monitoring/Control
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Machine control (including robotics)
Inventory control
Production monitoring
Chemical testing
10-48
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
• Limited focus
• Inability to learn
• Maintenance problems
• Development cost
• Can only solve specific types of problems in
a limited domain of knowledge
10-50
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
10-51
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
programs
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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
development
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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
processes
10-54
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
10-55
Example of Fuzzy Logic
Rules and Query
10-56
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
10-57
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
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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
10-59
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
– Interface 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
preferences
– 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
10-61
Information Management Agents
– Search 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
10-62
Case 3: Oracle Corporation and
Others: Dashboards for Executives
• Web-based “dashboards”
– Displays critical information in graphic form
– Assembled from data pulled in real time from
corporate software and databases
– Managers see changes almost instantaneously
– Now available to smaller companies
• Potential problems
– Pressure on employees
– Divisions in the office
– Tendency to hoard information
10-63
Case Study Questions
1. What is the attraction of dashboards to CEOs
and other executives? What real business value
do they provide to executives?
2. The case emphasizes that managers of small
businesses and many business professionals
now rely on dashboards. What business benefits
do dashboards provide to this business
audience?
3. What are several reasons for criticism of the use
of dashboards by executives? Do you agree with
any of this criticism?
10-64
Case 4: Harrah’s Entertainment, LendingTree,
DeepGreen Financial, and Cisco Systems
• The promise of AI of automating decision making has
been very slow to materialize.
• The new generation AI applications are easier to create
and manage, do not require anyone to identify the
problems or to initiate the analysis, decision-making
capabilities are embedded into the normal flow of work,
and are triggered without human intervention.
• They sense online data or conditions, apply codified
knowledge or logic and make decisions with minimal
human intervention.
• But they rely on experts and managers to create and
maintain rules and monitor the results.
• Also, managers in charge of automated decision
systems must develop processes for managing
exceptions.
10-65
Case Study Questions
1. 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 finally coming of age?
2. What types of decisions are best suited for automated
decision making? Provide several examples of
successful applications from the companies in this
case to illustrate your answer.
3. What role do humans play in automated decision
making applications? What are some of the
challenges faced by managers where automated
decision-making systems are being used? What
solutions are needed to meet such challenges?
10-66