Transcript Chapter 9 PowerPoint Presentation

Chapter 9
Decision Support Systems
McGraw-Hill/Irwin
Copyright © 2010 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
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Learning Objectives

Explain how these information systems can
support the information needs of executives,
managers, and business professionals
– Executive information systems
– Enterprise information portals
– Knowledge management systems
9-3
Learning Objectives

Identify how neural networks, fuzzy logic,
genetic algorithms, virtual reality, and
intelligent agents can be used in business

Give examples of ways expert systems
can be used in business decision-making
situations
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Decision Support in Business
Companies invest
in data-driven
decision support
application
frameworks to help
them respond to
Changing marketing conditions
Customer needs
Management information
Accomplished by
several types of
Decision support
Other information systems
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Case 1: Hillman Group, Avnet, Quaker Chemical

BI refers to a variety of software applications used to
analyze an organization’s raw data and extract useful
insights from them

BI tools, coupled with business process changes, can
have a significant impact on the bottom line

Most companies don’t understand their business
processes well enough to determine how to improve
them

Companies using BI to uncover flawed business
processes can more successfully compete against
companies using BI merely to monitor what’s happening
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Case Questions

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?

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?
9-7
Case Questions

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
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Levels of Managerial Decision Making
9-9
Information Quality
Information products are made more valuable
by their attributes, characteristics, or qualities
Outdated, inaccurate, or hard to understand
information 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 a
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
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
Pre-specified, fixed format
Ad hoc, flexible, and
adaptable format
Information
processing
methodology
Information produced by
extraction and manipulation
of business data
Information produced by
analytical modeling
of business data
Decision
support
provided
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Decision Support Trends
Personalized
decision
support
Modeling
Information
retrieval
Data
warehousing
What-if
scenarios
Reporting
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Decision Support Trends
9-15
Business Intelligence Applications
9-16
Decision Support Systems

To support the making of semi-structured
business decisions, DSS uses
– Analytical models
– Specialized databases
– Decision-maker’s own insights and judgments
– Interactive, computer-based modeling process

DS systems
– Ad hoc, quick-response systems
– Initiated and controlled by decision makers
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DSS Components
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DSS Model Base

Model Base
– A software component
– Consists of models used in computational
and analytical routines
– Mathematically expresses relationships
among variables

Spreadsheet Examples
– Linear programming
– Multiple regression forecasting
– Capital budgeting present value
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Applications of Statistics and Modeling
Supply Chain
Simulate & optimize supply chain
flows, reduce inventory & stock-outs
Pricing
Identify the price that maximizes
yield or profit
Product & Service
Quality
Detect quality problems early in
order to minimize them
Research &
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, displays, and responses
Satisfies needs of operational and tactical
decision makers who face structured decisions
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Management Reporting Alternatives
Periodic Scheduled
Reports
Pre-specified format, issued
on a regular basis
Exception Reports
Reports about exceptional
conditions, scheduled or on event
Demand Reports &
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
Ex: sales office data, rolled up to the district level
Drill-Down
Display underlying detail data
Ex: 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 (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
System (GPS) devices
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Data Visualization Systems (DVS)
Represents complex data using interactive, threedimensional 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
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Using Decision Support Systems
What-If
Analysis
Sensitivity
Analysis
Basic analytical modeling activities
Goal-seeking
Analysis
Optimization
Analysis
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Data Mining

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
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Analysis of Customer Demographics
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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

Typical applications of MBA
– Cross-selling
– Product placement
– Affinity promotion
– Survey analysis
– Fraud detection
– Customer behavior identification
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Executive Information Systems (EIS)
Combines many
features of
MIS and DSS
Provides top executives
with immediate, easy
access to information
Identifies factors
critical to
accomplishing
strategic objectives
So popular it was
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
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Web-Based Executive Information System
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Enterprise Information Portals

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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Enterprise Information Portal Components
9-36
Enterprise Knowledge Portal
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Case 2: Goodyear, JEA, OSUMC, Monsanto

Advanced technologies (AI, mathematical simulations,
robotics) can have dramatic impacts on business processes
and financial results
– Goodyear designers can perform tests 10 times faster using
simulation, reducing a new tire’s time to market from two
years to 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 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


In all of the project outcomes in the case, 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?
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?
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Case Study Questions

In all of the case examples, companies had an
urgent need that prompted them to investigate
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?
9-40
Artificial Intelligence (AI)
Engineering
Mathematics
Linguistics
AI is
a field of science
and technology
based on…
Computer
science
Biology
Psychology
9-41
Artificial Intelligence (AI)
Think
See
Feel
Ultimate
goal for
computers
Hear
Talk
Walk
9-42
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 situations
Recognize
relative
importance of
situation elements
Handle
ambiguous,
incomplete,
erroneous info
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Domains of Artificial Intelligence
9-44
Expert Systems
An Expert System (ES)
Knowledge-based information system
Contains knowledge about a
specific, complex application area
Acts as an export consultant to end users
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Components of an Expert System
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Methods of Knowledge Representation
Case-based
Frame-based
Object-based
Rule-based
9-47
Expert System Application Categories
Decision Management
Diagnostic/Troubleshooting
Design/Configuration
Selection/Classification
Process Monitoring/Control
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Benefits of Expert Systems
Captures expertise of expert(s) 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

Major limitations of expert systems
– Limited focus
– Inability to learn
– Maintenance problems
– Development and maintenance costs
– 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 welldefined
Expertise
Solutions to the
problem require
the efforts of an
expert
Complexity
Problem
solving is
complex, and
requires logical
inference
processing
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Developing Expert Systems
Suitability Criteria for Expert Systems
Structure… 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
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
– Recognizes patterns and relationships in data
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Fuzzy Logic
Resembles human reasoning
Allows approximate values and inferences,
and incomplete or ambiguous data
Uses terms like “very high” instead of precise measures
Allows processing of incomplete data
Results in quick, approximate solutions
Used in fuzzy process controllers
(subway trains, elevators, cars)
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Example of Fuzzy Logic Rules and Query
9-57
Genetic Algorithms
Uses Darwinian,
randomizing, and
other mathematical
functions
Stimulates an
evolutionary process,
yielding increasingly
better solutions
Genetic algorithm software
Especially useful for
situations in which
thousands of solutions
are possible
Being used to model a
variety of scientific,
technical, and business
processes
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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
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
Computer-aided
design
Entertainment
Medical diagnostics
and treatment
Current
applications
of virtual reality
Scientific
experimentation
Flight
simulation
Employee
training
Product
demonstrations
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Intelligent Agents
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 called 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
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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
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Case 3: Harrah’s, LendingTree, DeepGreen, Cisco

The promise of AI of automating decision
making has been very slow to materialize

The new generation AI applications
– Easier to create and manage
– Don’t require anyone to identify problems
or to initiate analysis
– Decision-making capabilities are embedded
into the normal flow of work, and are triggered
without human intervention
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Case 4: Harrah’s, LendingTree, DeepGreen, Cisco

The new generation AI applications
– Sense online data or conditions, apply codified
knowledge or logic and make decisions with
minimal human intervention
– Rely on experts and managers to create
and maintain rules and monitor the results
– Managers in charge of automated decision
systems must develop processes for
managing exceptions
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Case Study Questions

Why did some previous attempts to use
artificial intelligence technologies fail?
– What differences between the new AI-based
applications versus the old caused the
authors to declare that automated decision
making is finally coming of age?

What types of decisions are best suited for
automated decision making?
– Provide examples of successful applications
from the companies in this case
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Case Study Questions

What role do humans play in automated
decision making applications?
– What challenges face managers where
automated decision-making systems are
being used?
– What solutions are needed to meet such
challenges?
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