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Chapter 4
Decision Support and Artificial
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Management Information Systems
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Chapter Map
Chapter 4: Decision Support and Artificial Intelligence
Decisions
Categories
Decision Support



Decision Support Systems
Collaboration Systems
Geographic Information Systems
Artificial Intelligence




Expert Systems
Neural Networks
Genetics Algorithms
Intelligent Agents
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Management Information Systems
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Introduction


People make decisions all the time.
In a business environment, managers regularly make
complex decisions.
IT?
Whether you are running a hospital or a small retail business,
information technology can help you make decision (while you are in
the decision making process).
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Management Information Systems
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Introduction
Management Review 
Future successes will be for those organizations that are
“Big on brain and small of mass”.
IT?
Increase organizations knowledge and capability of using this
knowledge via intelligent tools such as decision support systems
and artificial intelligence software.
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Introduction
The categories of computer-aided decision support.
Decision Support



Decision Support Systems
Collaboration Systems
Geographic Information Systems
Artificial Intelligence




Expert Systems
Neural Networks
Genetics Algorithms
Intelligent Agents
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Decisions, Decisions, Decisions
Chapter 4: Decision Support and Artificial Intelligence
Decisions
Categories
Decision Support



Decision Support Systems
Collaboration Systems
Geographic Information Systems
Artificial Intelligence




Expert Systems
Neural Networks
Genetics Algorithms
Intelligent Agents
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Management Information Systems
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Decisions, Decisions, Decisions

People make decisions all the time.
Which job offer to take?
Which market to expand into?

Decisions include a number of criteria.





Some decisions include a small number of criteria
Other decision include a large number of criteria some of which are
practically impossible to quantify.
When making decisions, one usually has alternatives. It is difficult
to select the “best” alternative.
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Decisions, Decisions, Decisions
Decision making is one of the most
significant and important activities in
business.
This is why organizations spend tremendous
amount of time and money to the decision
making process.
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Decisions, Decisions, Decisions
How You Make a Decision
Figure 4.2
Four phases of
decision making
page 135
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Decisions, Decisions, Decisions
How You Make A Decision
Step 1: Intelligence
Find or recognize a problem, need, or opportunity.
 This is the diagnostic phase of the decision making
 Involves the detection and interpretation of signs





Consistent customer request for new feature or product
Threat of new competition
Declining sales
Rising costs
Offer from a company to handle your distribution needs
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Decisions, Decisions, Decisions
How You Make A Decision
Step 2: Design
Consider ways to solve the problem, fill the need, or take
advantage of an opportunity.
This is the phase where you would study all possible
solutions and develop those that are most interesting and
feasible.
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Decisions, Decisions, Decisions
How You Make A Decision
Step 3: Choice
Examine and weigh the merits of each solution, estimate the consequences of
each, and choose the best one.
 This is the prescriptive phase of the decision making.
 In this stage a course of action is prescribed and this course of action could
simply be to do nothing.
 Selection of best solution requires selection criteria such as:




Cost
Ease of implementation
Staffing requirements
Timing
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Decisions, Decisions, Decisions
How You Make A Decision
Step 4: Implementation
Carry out the chosen solution, monitor the results, and
make adjustments as necessary.
The implementation of final solution is seldom enough and
will practically always need fine-tuning.
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Decisions, Decisions, Decisions
Types of Decisions You Face
Figure 4.3
Viewing
Structured
Versus
Nonstructured
Decision
Making as a
Continuum
page 136
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Decisions, Decisions, Decisions
Types of Decisions You Face

If you want to decide which cheese to by then the criteria would be based
on:



Expiry date
Price
If you have to decide which job offer is better to accept, then the criteria for
your decision would be based on:







Salary
Intrinsic motivation
Location
Company
Position
Benefits
Many other criteria
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Decisions, Decisions, Decisions
Types of Decisions You Face

In the cheese case, the criteria are pretty
straight forward, hence the decision making is
rather STRUCTURED.

In the case of the job offer, the criteria are many
and most are not straight forward and are
difficult to quantify, hence UN-STRUCTURED.
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Decisions, Decisions, Decisions
Types of Decisions You Face
Structured decision
Processes information in a specified way so that you will
always get the right answer.
 Does not involve any “feel” into the process
 Does not require intuition
 These types of decision can be easily programmed



Fixed set of input
Same processing
Produce correct result
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Decisions, Decisions, Decisions
Types of Decisions You Face
Non-structured decision
One for which there may be several “right” answers and
there is no precise way to get a right answer.
 No rules or criteria exist that guarantee you a good
solution.
 Examples



To introduce a new product line or not
To employ a new marketing campaign
To change the corporate image
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Decisions, Decisions, Decisions
Types of Decisions You Face
Recurring decision
One that happens repeatedly, and often periodically.

Usually use the same set of rules each time.
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Decisions, Decisions, Decisions
Types of Decisions You Face
Nonrecurring decision
One that you make infrequently.



Or ad-hoc decision
May have different criteria for determining the best
solution each time
Example

Company merger
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Decision Support Systems
Chapter 4: Decision Support and Artificial Intelligence
Decisions
Categories
Decision Support
Artificial Intelligence
Decision Support System
Expert Systems
Collaboration Systems
Neural Networks
Geographic Information Systems
Genetic Algorithms
Intelligent Agents
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Management Information Systems
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Decision Support Systems
Highly flexible and interactive IT system that
is designed to support decision making
when the problem is
not structured.
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Decision Support Systems

A DSS is an alliance between



you,
the decision maker and
specialized support provided by IT
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Decision Support Systems

A DSS brings



Speed
Vast amounts of information
Sophisticated processing capabilities
To
Help
YOU
Create information USEFUL to make
a DECISION
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Decision Support Systems

You bring the know-how in the form of





Experience
Intuition
Judgment
Knowledge of relevant factors
You must know the right kind of questions to ask
and how to process the information so you may
get useful answers from the IT.
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Decision Support Systems
Figure 4.4
The Alliance
Between You
and a Decision
Support System
page 137
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Decision Support Systems
Components of a Decision Support System

A typical DSS has three components:



Model management
Data management
User interface management
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Decision Support Systems
Components of a Decision Support System
Figure 4.5
Components of a Decision
Support System page 138
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Decision Support Systems
Components of a Decision Support System
How do the components work together?



You tell the DSS using the user interface management component which
model to use
The model is found in the model management component which you had
instructed to use on a set of information found in the data management
component
So the model



Requests the information from the data management component
Analyzes that information
Sends the results to the user interface management component, hence
back to you
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Decision Support Systems
Components of a Decision Support System
The Model management
Consists of both the DSS models and the DSS model management
system.
 A model is a representation of some event, fact or situation to
represent variables and their relationships
 The model you use in DSS depend on the decision you are
making and consequently the kind of analysis you require such
as:
•
•
What-if analysis
Optimization
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Decision Support Systems
Components of a Decision Support System
Data management
Performs the function of storing and maintaining the information that
you want your DSS to use.
 It consists of both the DSS information and the DSS database
management system
 The information you use in your DSS comes from one or more of the
three sources:



Organizational information
External information
Personal information
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Decision Support Systems
Components of a Decision Support System
User interface management
Allows you to communicate with the DSS.

Consists of the user interface and the user interface management system

Allows you to combine your know-how with the storage and processing
capabilities of the computer

Through it you enter




Information
Commands
Models
Best user interface uses your terminology and methods and is:




Flexible
Consistent
Simple
adaptable
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Collaboration Systems
Chapter 4: Decision Support and Artificial Intelligence
Decisions
Categories
Decision Support
Artificial Intelligence
Decision Support System
Expert Systems
Collaboration Systems
Neural Networks
Geographic Information Systems
Genetic Algorithms
Intelligent Agents
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Collaboration system
A system that is designed specifically to
improve the performance of teams by
supporting the sharing and flow of
information.
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Figure 4.6
Collaboration Software
Connects People
page 141
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Collaboration System
Collaboration software takes many forms
with


Many combinations of features
Varying degrees of complexity
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Collaboration System
Three types of collaboration systems are
discussed in the book:



Enterprise-wide collaboration
Supply chain collaboration
Web-based collaboration
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Collaboration System
Enterprise-wide Collaboration

Lotus Notes and Microsoft Exchange are
types of integrated collaboration systems.
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Collaboration System
Supply-Chain Collaboration

Supply chain management means working
with your suppliers and distributors in all
phases of planning, production, and
distribution.
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Collaboration System
Web-Based Collaboration

Web-based collaboration tools use the power of
the Internet to enable people to work together
effectively and efficiently.

The peer-to-peer file-sharing feature is
combined with the ability to create and edit
documents collaboratively, and to send and
receive text and voice messages.
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Geographic Information Systems
Chapter 4: Decision Support and Artificial Intelligence
Decisions
Categories
Decision Support
Artificial Intelligence
Decision Support System
Expert Systems
Collaboration Systems
Neural Networks
Geographic Information Systems
Genetic Algorithms
Intelligent Agents
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Geographic Information Systems
Geographic information system (GIS)
A decision support system designed to work
with spatial information.
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Geographic Information Systems
Spatial information
Is any information that can be shown in map
form, such as roads, the distribution of bald
eagle populations, and the layout of
electrical lines.
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Geographic Information Systems
Figure 4.7
Geographic Information
Systems
page 144
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Geographic Information Systems

Today GISs are helping businesses:




Identify the best site to locate a branch office
Target pockets of potential customers
Reposition promotions and advertising
Determine the optimal location of a new
distribution outlet
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Geographic Information Systems
Business Geography
A new type of information created when
businesses combine textual information with
spatial information
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Artificial Intelligence
Chapter 4: Decision Support and Artificial Intelligence
Decisions
Categories
Decision Support
Artificial Intelligence
Decision Support System
Expert Systems
Collaboration Systems
Neural Networks
Geographic Information Systems
Genetic Algorithms
Intelligent Agents
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Artificial Intelligence
Decision Support
Decision Support System
+
Collaboration Systems
Mimic human behavior in
various ways
Geographic Information Systems
Are IT systems that
augment business brain
power.
Artificial Intelligence
Expert Systems
=
Neural Networks
Genetic Algorithms
Intelligent Agents
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Artificial Intelligence
Artificial intelligence (AI)
The science of making machines imitate
human thinking and behavior.
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Artificial Intelligence
Robot
A mechanical device equipped with
simulated human senses and the capability
of taking action on its own.
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Artificial Intelligence

Who uses artificial intelligence?

Financial analysts to:
•
•
•
Manage assets
Invest in the stock market
Perform other financial operations
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Artificial Intelligence

Who uses artificial intelligence?

Hospitals:
•
•
•
•
For scheduling staff
Assigning beds to patients
Diagnose illnesses
Treat conditions and illnesses
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Artificial Intelligence

Who uses artificial intelligence?

Credit card companies
•

To detect credit card fraud
Insurance companies
•
To ferret out fraudulent claims
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Artificial Intelligence

Artificial intelligent systems that businesses
today use most can be classified into the
following major categories




Expert systems
Neural networks
Genetic algorithms
Intelligent agents
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Expert Systems
Chapter 4: Decision Support and Artificial Intelligence
Decisions
Categories
Decision Support
Artificial Intelligence
Decision Support System
Expert Systems
Collaboration Systems
Neural Networks
Geographic Information Systems
Genetic Algorithms
Intelligent Agents
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Expert Systems
Expert system
or knowledge-based system
Is an artificial intelligence system that applies
reasoning capabilities to reach a conclusion.
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Expert Systems
Components of an Expert System

Expert systems are good for


Diagnostic problems: Problems requiring an answer
to the question “what’s wrong?” correspond to the
intelligence phase of decision making and
Prescriptive problems: Problems that require an
answer to the question “what to do?” and correspond
to the choice phase of decision making.
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Expert Systems
Components of an Expert System
Figure 4.9
Traffic-Light Expert System Rules
page 149
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Expert Systems
Components of an Expert System

An expert system is usually built for a specific
application area called domain:







Accounting
Medicine
Process control
Human resource management
Financial management
Production
Forestry management
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Expert Systems
Components of an Expert System

An expert system combines



Information types,
People, and
IT components.
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Expert Systems
Components of an Expert System

Information types include:

Domain expertise
•


The reasoning process that will solve the problem.
“Why” information.
Problem facts.
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Expert Systems
Components of an Expert System
Figure 4.8
Developing and Using an Expert System
page 150
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Expert Systems
Components of an Expert System

People

Domain expert
•

Knowledge engineer
•

provides the domain expertise in the form of problem-solving
strategies.
IT specialist who formulates the domain expertise into an
expert system.
Knowledge worker or user
•
that’s you.
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Expert Systems
Components of an Expert System

IT Components

Knowledge base
•

Knowledge acquisition
•

used to enter the rules.
Inference engine
•

stores the rules of the expert system.
takes the problem facts and searches the knowledge base for rules
that fit.
User interface
•
used to run the consultation.
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Expert Systems
What Expert Systems Can and Can’t Do

An expert system can:




Reduce errors
Improve customer service
Reduce costs
An expert system can’t:


On Your Own
Use common sense
Automate all processes
Traffic Lights
Revisited
(p. 151)
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Neural Networks
Chapter 4: Decision Support and Artificial Intelligence
Decisions
Categories
Decision Support
Artificial Intelligence
Decision Support System
Expert Systems
Collaboration Systems
Neural Networks
Geographic Information Systems
Genetic Algorithms
Intelligent Agents
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Management Information Systems
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Second Canadian Edition
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Neural Networks
Neural network
(often called an artificial neural network or ANN)
an artificial intelligence system that is
capable of finding and differentiating
patterns.
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Neural Networks





A NN simulates the human ability to classify things
without taking prescribed steps leading to the
solution
A NN can learn by example
A NN can adapt to new concepts and knowledge
NN are widely used for visual pattern and speech
recognition systems
A PDA probably uses NN to decipher your handwriting
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Neural Networks

NN are used for many other applications
such as



In airports to detect bombs
In police departments to identify corruption
In medicine to
•
•
•
Check for drug interaction
Detect anomalies in tissue samples
Heart attacks
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Management Information Systems
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Neural Networks

In business NN are used





Security trading
Fraud detection
Real estate appraisal
Evaluating loan applications
Target marketing
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Management Information Systems
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Second Canadian Edition
Copyright 2004
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All rights reserved
Neural Networks
Types of Neural Networks

Self-organizing neural
network - finds patterns and
relationships in vast amounts
of data by itself.

Back-propagation neural
network - a neural network
trained by someone.
Team Work
How Would You
Classify People?
(p. 154)
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Management Information Systems
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Neural Networks
Inside a Neural Network
Figure 4.11
The Layers of a
Neural Network
page 155
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Management Information Systems
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Neural Networks
Inside a Neural Network


NNs attempt to mimic the human brain
They consist of three layers of a
virtual neuron



Input layer
Hidden layer
Output layer
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Management Information Systems
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Neural Networks
Inside a Neural Network

Advantages of a neural network





Can learn and adjust to new circumstances on their
own
Lend themselves to massive parallel processing
Can function without complete and well structured
information
Can cope with huge volumes of information with
many dependent variables
Can analyze nonlinear relationships in information
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Management Information Systems
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Neural Networks
Inside a Neural Network

The biggest problem with NN

The hidden layers are hidden
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Genetic Algorithms
Chapter 4: Decision Support and Artificial Intelligence
Decisions
Categories
Decision Support
Artificial Intelligence
Decision Support System
Expert Systems
Collaboration Systems
Neural Networks
Geographic Information Systems
Genetic Algorithms
Intelligent Agents
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Management Information Systems
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Second Canadian Edition
Copyright 2004
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All rights reserved
Genetic Algorithms
Genetic algorithm
An artificial intelligence system that mimics
the evolutionary, survival-of-the-fittest
process to generate increasingly better
solutions to a problem.
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Management Information Systems
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All rights reserved
Genetic Algorithms


Genetic algorithms are artificial intelligence
software capable of following trial-and-error
processes leading to the evolution of a good
result.
In other words, genetic algorithms are optimizing
systems such that they find the combination of
inputs that give the best outputs.
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Management Information Systems
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Genetic Algorithms

Genetic algorithms use three concepts of
evolution:



Selection — survival of the fittest.
Crossover — combining portions of good outcomes
in the hope of creating an even better outcome.
Mutation — randomly trying combinations and
evaluating the success (or failure) of the outcome.
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All rights reserved
Genetic Algorithms

Who uses genetic algorithms?




They are used by business executives to help them decide
which combination of projects a firm should invest in.
They are used by investment firms to help then in trading
choices and associated decisions.
Used in the garment industry to help them solve the problem of
laying out the pieces of the garment and cutting fabric in a way
that leaves as little waste as possible.
Used to determine the optimal configuration of fiber optic cable
in a network that may include as many as 100,000 connection
points.
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Genetic Algorithms
On Your Own
Be a Genetic Algorithm
And Put Nails in Boxes
(p. 158)
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Management Information Systems
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Intelligent Agents
Chapter 4: Decision Support and Artificial Intelligence
Decisions
Categories
Decision Support
Artificial Intelligence
Decision Support System
Expert Systems
Collaboration Systems
Neural Networks
Geographic Information Systems
Genetic Algorithms
Intelligent Agents
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Management Information Systems
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Second Canadian Edition
Copyright 2004
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All rights reserved
Intelligent Agents
Intelligent agent
Software that assists you, or acts on your
behalf, in performing repetitive computerrelated tasks.
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Management Information Systems
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Intelligent Agents

Four types of intelligent agents include:




Buyer agents or shopping bots
User or personal agents
Monitoring-and-surveillance or predictive agents
Data-mining agents
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Management Information Systems
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Second Canadian Edition
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Intelligent Agents
Buyer Agents
Buyer agent or shopping bot
an intelligent agent on a Web site that helps
the customer find products and services.
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Intelligent Agents
Buyer Agents

Types of filtering include:

Collaborative filtering - a method of placing
you in an affinity group of people with the
same characteristics.

Profile filtering - requires that you choose
terms or enter keywords.
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Management Information Systems
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Second Canadian Edition
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All rights reserved
Intelligent Agents
Buyer Agents

Types of filtering continued:

Psychographic filtering - anticipates your
preferences based on the answers you give to a
questionnaire.

Adaptive filtering - asks you to rate products or
situations and also monitors your actions over time to
find out what you like and dislike.
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Management Information Systems
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Second Canadian Edition
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Intelligent Agents
User Agents

User agents
(sometimes called
personal agents) intelligent agents that
take action on your
behalf.
On Your Own
Go Bargain
Hunting Online
(p. 161)
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Management Information Systems
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Second Canadian Edition
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Intelligent Agents
Monitoring-and-Surveillance Agents

Monitoring-and-surveillance agents
(also called predictive agents) - observe
and report on equipment.
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Management Information Systems
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Intelligent Agents
Data-Mining Agents

Data-mining agent - operates in a data
warehouse discovering information.
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Intelligent Agents
Components of an Intelligent Agent

Autonomy - act without your telling them every
step to take.

Adaptivity - discovering, learning, and taking
action independently.

Sociability - conferring with other agents.
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Closing Case Study One
Using Neural Networks To Categorize People

Using neural network software, businesses now
have the ability to look for patterns in their
customer information.

How accurate is it for a business to predict the
future behavior of customers on the basis of past
behavior?
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Closing Case Study Two
Decision Support and Artificial
Intelligence in Health Care

Good health care is based largely on good
information.

How can DSS and AI be used to track
symptoms, treatment, and outcomes that require
the collection and maintenance of a huge
amount of qualitative and quantitative
information?
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Management Information Systems
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Second Canadian Edition
Copyright 2004
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All rights reserved
Summary
Student Learning Outcomes
1. Define decision support system, list its
components, and understand its applications.
2. Define collaboration systems along with their
features and uses.
3. Define geographic information systems and
state how they differ from other decision
support tools.
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Management Information Systems
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Second Canadian Edition
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Summary
Student Learning Outcomes
4. Define artificial intelligence and list the
different types that are used in business.
5. Define expert systems, and the type of
problems to which they are applicable.
6. Define neural networks, their uses, and
the major strength and weaknesses.
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Management Information Systems
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Second Canadian Edition
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Summary
Student Learning Outcomes
7. Define genetic algorithms and list the
concepts on which they are based, and
the types of problems they solve.
8. Define intelligent agents, list the four
types, and identify the types of problems
they solve.
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Second Canadian Edition
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Summary
Assignments & Exercises
1.
2.
3.
4.
Make a GIS
Collaboration Work
Choose a Financing Option
Which Software Would You Use?
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Management Information Systems
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Real Hot Electronic Commerce
Finding Investment Opportunities on the
Internet





Learning about Investing
Researching the Company behind the Stock
Finding other Sources of Company Financials
Making Trades Online
Retrieving Stock Quotes
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Management Information Systems
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Second Canadian Edition
Copyright 2004
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All rights reserved
Visit the Web to Learn More
www.mcgrawhill.ca/college/haag





Learning about Investing
Researching the Company behind the Stock
Finding other Sources of Company Financials
Making Trades Online
Retrieving Stock Quotes
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Management Information Systems
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Second Canadian Edition
Copyright 2004
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