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Chapter
10
Decision Support Systems
McGraw-Hill/Irwin
Copyright © 2008, 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
10-3
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
10-4
Learning Objectives
• Give examples of several ways expert systems
can be used in business decision-making
situations
10-5
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
10-6
Case 1: 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
10-7
Case Study Questions
• What is the attraction of dashboards to CEOs
and other executives?
• What real business value do they provide
to executives?
• 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?
10-8
Case Study Questions
• What are several reasons for criticism of
the use of dashboards by executives?
• Do you agree with any of this criticism?
10-9
Levels of Managerial Decision Making
10-10
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• Information that is outdated, inaccurate, or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
10-11
Attributes of Information Quality
10-12
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
10-13
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
10-14
Decision Support Trends
• The emerging class of applications focuses on
•
•
•
•
•
•
Personalized decision support
Modeling
Information retrieval
Data warehousing
What-if scenarios
Reporting
10-15
Business Intelligence Applications
10-16
Decision Support Systems
• Decision support systems use the following to
support the making of semi-structured 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
10-17
DSS Components
10-18
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
10-19
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
10-20
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
10-21
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
10-22
Example of Push Reporting
10-23
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
10-24
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
10-25
OLAP Configuration
10-26
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
10-27
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
10-28
DVS Example
10-29
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
10-30
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
10-31
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
10-32
Analysis of Customer Demographics
10-33
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
10-34
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
10-35
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
10-36
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
10-37
Dashboard Example
10-38
Enterprise Information Portal Components
10-39
Enterprise Knowledge Portal
10-40
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
intervention
• Can translate decisions into action quickly,
accurately, and efficiently
10-41
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
10-42
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?
10-43
Case Study Questions
• What role do humans plan 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?
10-44
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
10-45
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
10-46
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
10-47
Domains of Artificial Intelligence
10-48
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
10-49
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
10-50
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
10-51
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
10-52
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
10-53
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
10-54
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
10-55
Components of an Expert System
10-56
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
10-57
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)
10-58
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
10-59
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
10-60
Expert System Application Categories
• Process Monitoring/Control
•
•
•
•
Machine control (including robotics)
Inventory control
Production monitoring
Chemical testing
10-61
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
10-62
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
10-63
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
10-64
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
10-65
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
10-66
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
10-67
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
10-68
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
10-69
Example of Fuzzy Logic Rules and Query
10-70
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
10-71
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
10-72
Typical VR Applications
• Current applications of virtual reality
•
•
•
•
•
•
•
Computer-aided design
Medical diagnostics and treatment
Scientific experimentation
Flight simulation
Product demonstrations
Employee training
Entertainment
10-73
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
10-74
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
10-75
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
10-76
Case 3: Centralized Business Intelligence
• 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 tools
• Make them available throughout the organization,
even before projects are planned
10-77
Case 3: Centralized Business Intelligence
• 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 issue
10-78
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?
10-79
Case 4: Robots, the Common Denominator
• 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
procedures
• Left arm, right arm, camera arm
10-80
Case 4: Robots, the Common Denominator
• Automakers heavily use robotics
• Ford has a completely wireless assembly factory
• It also have a completely automated body shop
• 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
10-81
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 case?
10-82