Transcript Information Technology Software

What is a DSS?
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“Decision Support System”: An
information system that uses the
combination of data, decision models and
algorithms, and human intuition, knowledge
and judgment to aid a decision maker in
reaching a specific decision.
DSS
Humans:
Multiple ways
to make
decisions.
Tools:
Computers and
IT. VB, VBA,
Excel, InterDev,
Etc.
DSS
Data:
Facts pertinent
to the decision
at hand.
Algorithms:
Math/Flow Chart
stuff that helps the
tools help the humans
make decisions.
The Need for Computerized
Decision Support and the
Supporting Technologies
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Speedy computations
Overcoming cognitive limits in processing and
storage
Cognitive limits may restrict an individual’s
problem solving capability
Cost reduction
Technical support
Quality support
Competitive edge: business processes
reengineering and empowerment
Primary Decision Support
Technologies
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Management Support Systems (MSS)
Decision Support Systems (DSS)
Group Support Systems (GSS),
including Group DSS (GDSS)
Executive Information Systems (EIS)
Expert Systems (ES)
Artificial Neural Networks (ANN)
Hybrid Support Systems
Cutting Edge Intelligent Systems
(Genetic Algorithms, Fuzzy Logic,
Intelligent Agents, ...)
Let’s look at some leading
companies
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www.cognos.com
– “the world's leading vendor of Business
Intelligence solutions for e-business”… a DSS
company.
– Handles chunks of the NASDAQ Web site.
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www.datajungle.com
– Provides the data for the much of the above.
DSS
Humans:
Multiple ways
to make
decisions.
Tools:
Computers and
IT. VB, VBA,
Excel, InterDev,
Etc.
DSS
Data:
Facts pertinent
to the decision
at hand.
Algorithms:
Math/Flow Chart
stuff that helps the
tools help the humans
make decisions.
DSS
Humans:
Decision
Making
Process
Tools:
Computers and
IT. VB, VBA,
Excel, InterDev,
Etc.
DSS
Data:
Facts pertinent
to the decision
at hand.
Algorithms:
Math/Flow Chart
stuff that helps the
tools help the humans
make decisions.
DSS
Humans:
Decision
Making
Process
Data:
Facts pertinent
to the decision
at hand.
(2nd Half of Class next two weeks)
Tools:
Computers and
IT. VB, VBA,
Excel, InterDev,
Etc.
DSS
Algorithms:
Math/Flow Chart
stuff that helps the
tools help the humans
make decisions.
Human Cognition and
Decision Styles
Every person makes decisions differently!!!
 As a good consultant / analyst, you need to
research the decision styles of your system’s
target audience / user base.
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Cognitive Style
Two
major types:
–Analytic decision maker
–Heuristic decision maker
Some (other) Decision
Styles
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Heuristic
Analytic
Autocratic
Democratic
Consultative (with individuals or groups)
Combinations and variations
For successful decision making support, an
MSS must fit the
– Decision situation
– Decision style
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The system
–
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should be flexible and adaptable to different users
have what-if and goal-seeking
have graphics
have process flexibility
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An MSS should help decision makers use
and develop their own styles, skills, and
knowledge
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Different decision styles require different
types of support
Major factor: individual or group decision
maker
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The Decision Makers
Individuals
 Groups
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Individuals
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May still have conflicting objectives
Decisions may be fully automated
Groups
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Most major decisions in medium and large organizations
are made by groups
Conflicting objectives are common
Variable size
People from different departments
People from different organizations
The group decision making process can be very
complicated
Consider Group Support Systems (GSS)
Organizational DSS can help in enterprise-wide decision
making situations
The Decision-Making
Process
Systematic Decision-Making Process (Simon
[1977])
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Intelligence
Design
Choice
Implementation
Modeling is Essential to the Process
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Intelligence phase
– Reality is examined
– The problem is identified and defined
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Design phase
– Representative model is constructed
– The model is validated and evaluation criteria
are set
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Choice phase
– Includes a proposed solution to the model
– If reasonable, move on to the
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Implementation phase
– Solution to the original problem
Failure: Return to the modeling process
Often Backtrack / Cycle Throughout the Process
What-If Analysis
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Golf example from last week -- “What if I
changed the price of bags from $105 to $150?”
(we’d make more bags).
Goal Seeking
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Backward solution approach
Example: If Cisco returns 20% per year, how
much would I have to buy now to retire (65)
with a million dollars?
In a DSS the what-if and the goalseeking options must be easy to
perform
Which systems support which phases?
DSS
Humans:
Multiple ways
to make
decisions.
Many different
names!
Tools:
Computers and
IT. VB, VBA,
Excel, InterDev,
Etc.
DSS
Data:
Facts pertinent
to the decision
at hand.
Algorithms:
Math/Flow Chart
stuff that helps the
tools help the humans
make decisions.
Acronym Heaven
DSS, EIS, ES.
 DSS, EIS differ in sensitivity of information
being offered (EIS is more sensitive).
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ES: Expert Systems
Knowledge Base (facts, rules)
 Inference Engine (software)
 User Interface
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EXPERT SYSTEMS
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Expert system: Information system that applies
reasoning capabilities and stored knowledge to reach
a conclusion (low level “AI”).
Collect, store, formalize and use large stores of task
specific expertise
What is expertise?
– Knowledge: Structured information
– Heuristics: Rules of thumb compiled from experience
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Simple example: “Diagnosing” whales
http://www.aiinc.ca/demos/whale.html
EXPERT SYSTEM COMPONENTS
The Expert System
Expert
Advice
User
User
Interface
Programs
Inference
Engine
Program
Knowledge
Base
Workstation
Expert System Development
Knowledge
Engineering
Knowledge
Acquisition
Program
Workstation
Drivers
with Pagers
Expert and/or
Knowledge Engineer
Expert Systems Example
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ITT Commercial Finance Corp., Expert Credit
System (ECS)
Uses experience and knowledge of senior credit
managers.
Analyzes credit information, identifies credit
proposal strengths and weaknesses, makes
recommendations.
Available to all decision-making managers (userfriendly, as well).
23 offices, 250 users.
$500,000 savings in hard costs, $1 M bad loan
write off savings estimated.
EXAMPLE: CREDIT CARDS
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American Express does not have fixed
account limits, but instead decides each
credit authorization on a case-by-case
basis. What might be some rules or
heuristics for this decision process?
Expert Systems Examples
Karl Irwin gets engaged.
 Diagnosing illnesses.
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Expert Systems: Leading
Companies
EXSYS (EXSYS, Inc.)
http://www.exsys.com
 K-Vision (Ginesys Corp.)
http://www.ginesys.com
 KnowledgePro (Knowledge Garden, Inc.)
http://www.kgarden.com
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EXAMPLE: CHOOSING WINES
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What are relevant facts about wines and
meals?
What are some example rules of thumb for
pairing different kinds of wine with
different kinds of meals?
WINE EXAMPLE: FACTS
Facts about meals (8
facts)
Facts about wines (8 facts)
Main_course: Meat, Fish,
Poultry, Cajun
Suggested_color: Red, White
Sauce: Tomato, Nontomato
Suggested _wine: Chablis,
Chardonnay, Burgundy,
Beaujolis
Flavor: Strong, Delicate
Suggested _body: Full, Light
WINE EXAMPLE: IF-THEN
RULES
Intermediate Conclusions
If Main_course is Meat or Sauce is Tomato
then Suggested_color is Red
If Main_course is Fish or Poultry and Sauce is
Not_Tomato
then
Suggested_color is White
If Flavor is Strong
then Suggested_body is Full
If Flavor is Delicate
then Suggested_body is Light
WINE EXAMPLE: IF-THEN
RULES
Final Conclusions
If Suggested_body is Light and Suggested_color is
White then Suggested_wine is Chablis
If Suggested_body is Full and Suggested_color is
White then Suggested_wine is Chardonnay
If Suggested_body is Full and Suggested_color is
Red then Suggested_wine is Burgundy
If Suggested_body is Light and Suggested_color is
Red then Suggested_wine is Beaujolis
WHEN IS AN EXPERT
SYSTEM APPROPRIATE?
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Domain: Narrow and well defined
Expertise: Requires true expertise in short
supply
Complexity: Problem is too complex for
conventional programming
Structure: Solution process must cope with
ill-structure, uncertainty, missing data
Availability: Have a willing, articulate
expert!!
DSS vs. Expert System
DSS:
Expert system
– Fixed models and
formulas
– Mimics human
reasoning abilities
– Usually user driven;
user has expertise,
user asks the questions
– Usually machine
driven; machine has
expertise; machine
asks the questions
– Does not explain
answers
– Has explanation
facility