Download 10-25 Using Decision Support Systems

Chapter
10
Decision Support Systems
McGraw-Hill/Irwin
Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.
Learning Objectives
• 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
10-2
Learning Objectives
• Explain how the following information systems
can support the information needs of
executives, managers, and business
professionals
– Executive information systems
– Enterprise information portals
– Knowledge management systems
• Identify how neural networks, fuzzy logic,
genetic algorithms, virtual reality, and
intelligent agents can be used in business
10-3
Learning Objectives
• Give examples of several ways
expert systems can be used in
business decision-making situations
10-4
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
10-5
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.
10-6
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.
10-7
Levels of Managerial Decision Making
10-8
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
10-9
Attributes of Information Quality
10-10
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
10-11
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
10-12
Decision Support Trends
• The emerging class of applications
focuses on
– Personalized decision support
– Modeling
– Information retrieval
– Data warehousing
– What-if scenarios
– Reporting
10-13
Business Intelligence Applications
10-14
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
10-15
DSS Components
10-16
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
10-17
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
10-18
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
10-19
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
10-22
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
10-23
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
10-24
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
10-25
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
10-26
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
–
–
–
–
–
Regression
Decision tree
Neural network
Cluster detection
Market basket analysis
10-27
Analysis of Customer Demographics
10-28
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
10-29
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
10-30
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
10-31
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
10-32
Enterprise Information Portal Components
10-33
Enterprise Knowledge Portal
10-34
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 selfguided robotic vehicles to move linens, meals, trash, and
medical supplies throughout the 1,000-bed hospital.
10-35
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?
10-36
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
10-37
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
10-38
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
10-39
Domains of Artificial Intelligence
10-40
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
10-41
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
–
–
–
–
–
Sight or visual perception
Touch
Dexterity
Locomotion
Navigation
10-42
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
10-43
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
10-44
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
10-45
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
10-46
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
10-47
Components of an Expert System
10-48
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
10-49
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)
10-50
Expert System Application Categories
• Decision Management
– Loan portfolio analysis
– Employee performance evaluation
– Insurance underwriting
• Diagnostic/Troubleshooting
– Equipment calibration
– Help desk operations
– Medical diagnosis
– Software debugging
10-51
Expert System Application Categories
• Design/Configuration
– Computer option installation
– Manufacturability studies
– Communications networks
• Selection/Classification
–
–
–
–
Material selection
Delinquent account identification
Information classification
Suspect identification
• Process Monitoring/Control
10-52
Expert System Application Categories
• Process Monitoring/Control
– Machine control (including robotics)
– Inventory control
– Production monitoring
– Chemical testing
10-53
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
10-54
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
10-55
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
10-56
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
10-57
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
10-58
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
10-59
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
10-60
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
10-61
Example of Fuzzy Logic Rules and Query
10-62
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
10-63
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
10-64
Typical VR Applications
• Current applications of virtual reality
– Computer-aided design
– Medical diagnostics and treatment
– Scientific experimentation
– Flight simulation
– Product demonstrations
– Employee training
– Entertainment
10-65
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
10-66
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
10-67
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
10-68
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
10-69
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?
10-70
Case Study Questions
3. What are several reasons for
criticism of the use of dashboards
by executives? Do you agree with
any of this criticism?
10-71
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, decisionmaking capabilities are embedded into
the normal flow of work, and are
triggered without human intervention.
10-72
Case 4: Harrah’s Entertainment,
LendingTree, DeepGreen Financial, and
Cisco Systems:
• 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.
10-73
Case Study Questions
1. Why did some previous attempts to use
artificial intelligence technologies fail?
What key differences of the new AIbased 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.
10-74
Case Study Questions
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?
10-75