chap13.ppt 261KB May 20 2010 09:29:48 PM
MANAGEMENT INFORMATION SYSTEMS 8/E
Raymond McLeod, Jr. and George Schell
Chapter 13
Decision Support Systems
13-1
Copyright 2001 Prentice-Hall, Inc.
Simon’s Types of Decisions
Programmed decisions
– repetitive and routine
– have a definite procedure
Nonprogrammed decisions
– Novel and unstructured
– No cut-and-dried method for handling problem
Types exist on a continuum
13-2
Simon’s Problem Solving
Phases
Intelligence
– Searching environment for conditions calling for a solution
Design
– Inventing, developing, and analyzing possible courses of
action
Choice
– Selecting a course of action from those available
Review
– Assessing past choices
13-3
Definitions of a Decision
Support System (DSS)
General definition - a system providing both
problem-solving and communications capabilities
for semistructured problems
Specific definition - a system that supports a
single manager or a relatively small group of
managers working as a problem-solving team in
the solution of a semistructured problem by
providing information or making suggestions
concerning specific decisions.
13-4
The DSS Concept
Gorry and Scott Morton coined the phrase ‘DSS’ in
1971, about ten years after MIS became popular
Decision types in terms of problem structure
– Structured problems can be solved with algorithms and
decision rules
– Unstructured problems have no structure in Simon’s
phases
– Semistructured problems have structured and
unstructured phases
13-5
The Gorry and Scott Morton Grid
Management levels
Operational
control
Structured
Degree of
problem
structure
Semistructured
Unstructured
Management
control
Strategic
planning
Accounts
receivable
Budget analysis-engineered costs
Tanker fleet
mix
Order entry
Short-term
forecasting
Warehouse and
factory location
Inventory
control
Production
scheduling
Variance analysis-overall budget
Mergers and
acquisitions
Cash
management
Budget
preparation
New product
planning
PERT/COST
systems
Sales and
production
R&D planning
13-6
Alter’s DSS Types
In 1976 Steven Alter, a doctoral student
built on Gorry and Scott-Morton framework
– Created a taxonomy of six DSS types
– Based on a study of 56 DSSs
Classifies DSSs based on “degree of
problem solving support.”
13-7
Levels of Alter’s DSSs
Level of problem-solving support from
lowest to highest
–
–
–
–
–
–
Retrieval of information elements
Retrieval of information files
Creation of reports from multiple files
Estimation of decision consequences
Propose decisions
Make decisions
13-8
Importance of Alter’s
Study
Supports concept of developing systems
that address particular decisions
Makes clear that DSSs need not be
restricted to a particular application type
13-9
Alter’s DSS Types
Retrieve
information
elements
Little
Analyze
entire
files
Prepare
reports
from
multiple
files
Estimate
decision
consequences
Degree of
complexity of the
problem-solving
system
Propose
decisions
Make
decisions
Degree
of
problem
solving
support
Much
13-10
Three DSS Objectives
1. Assist in solving semistructured problems
2. Support, not replace, the manager
3. Contribute to decision effectiveness, rather
than efficiency
Based on studies of Keen and Scott-Morton
13-11
A DSS Model
Environment
Individual
problem
solvers
Other
group
members
Report
writing
software
GDSS
GDSS
software
software
Mathematical
Models
Database
Decision
support
system
Legend:
Environment
Data
Communication
Information
13-12
Database Contents
Used by Three Software Subsystems
– Report writers
» Special reports
» Periodic reports
» COBOL or PL/I
» DBMS
– Mathematical models
» Simulations
» Special modeling languages
– Groupware or GDSS
13-13
Group Decision Support
Systems
Computer-based system that supports groups of
people engaged in a common task (or goal) and
that provides an interface to a shared environment.
Used in problem solving
Related areas
–
–
–
–
Electronic meeting system (EMS)
Computer-supported cooperative work (CSCW)
Group support system (GSS)
Groupware
13-14
How GDSS Contributes
to Problem Solving
Improved communications
Improved discussion focus
Less wasted time
13-15
GDSS Environmental
Settings
Synchronous exchange
– Members meet at same time
– Committee meeting is an example
Asynchronous exchange
–
–
Members meet at different times
E-mail is an example
More balanced participation.
13-16
GDSS Types
Decision rooms
–
–
–
Small groups face-to-face
Parallel communication
Anonymity
Local area decision network
– Members interact using a LAN
Legislative session
– Large group interaction
Computer-mediated conference
– Permits large, geographically dispersed group interaction
13-17
Group Size and Location Determine
GDSS Environmental Settings
GROUP SIZE
Face-toface
MEMBER
PROXIMITY
Dispersed
Smaller
Larger
Decision
Room
Legislative
Session
Local Area
Decision
Network
ComputerMediated
Conference
13-18
Groupware
Functions
– E-mail
– FAX
– Voice messaging
– Internet access
Lotus Notes
– Popular groupware product
– Handles data important to managers
13-19
Main Groupware Functions
IBM
TeamWARE Lotus
Function
Workgroup Office
Notes
Electronic mail
X
X
X
FAX
X
X
O
Voice messaging
O
Internet access
X
X
O
Bulletin board system
X
3
Personal calendaring
X
X
3
Group calendaring
X
X
O
Electronic conferencing
O
X
3
Task management
X
X
3
Desktop video conferencingO
Database access
O
X
3
Workflow routing
O
X
3
Reengineering
O
X
3
Electronic forms
O
3
3
Group documents
O
X
X
X = standard feature
O = optional feature
Novell
GroupWise
X
X
X
X
O
X
X
3
X
X
O
O
3 = third party offering
13-20
Artificial Intelligence (AI)
The activity of providing such
machines as computers with the
ability to display behavior that
would be regarded as intelligent if
it were observed in humans.
13-21
History of AI
Early history
– John McCarthy coined term, AI, in 1956, at
Dartmouth College conference.
– Logic Theorist (first AI program. Herbert Simon
played a part)
– General problem solver (GPS)
Past 2 decades
– Research has taken a back seat to MIS and DSS
development
13-22
Areas of Artificial Intelligence
Expert
systems
AI
hardware
Robotics
Natural
language
Learning
Neural
networks
Perceptive
systems
(vision,
hearing)
Artificial Intelligence
13-23
Appeal of Expert Systems
Computer program that codes the
knowledge of human experts in the form of
heuristics
Two distinctions from DSS
– 1. Has potential to extend manager’s problemsolving ability
– 2. Ability to explain how solution was reached
13-24
User
Instructions &
information
Solutions &
explanations
Knowledge
User
interface
Inference
engine
Expert
system
Development
engine
Expert and
knowledge engineer
Knowledge
base
Problem
Domain
An Expert
System Model
13-25
Expert System Model
User interface
– Allows user to interact with system
Knowledge base
– Houses accumulated knowledge
Inference engine
– Provides reasoning
– Interprets knowledge base
Development engine
– Creates expert system
13-26
User Interface
User enters:
– Instructions
– Information
}
Menus, commands, natural language, GUI
Expert system provides:
–
–
Solutions
Explanations of
» Questions
» Problem solutions
13-27
Knowledge Base
Description of problem domain
Rules
–
–
–
Knowledge representation technique
‘IF:THEN’ logic
Networks of rules
» Lowest levels provide evidence
» Top levels produce 1 or more conclusions
» Conclusion is called a Goal variable.
13-28
A Rule Set That
Produces One Final
Conclusion
Conclusion
Conclusion
Evidence
Evidence
Conclusion
Evidence
Evidence
Evidence
Evidence
Evidence
Evidence
13-29
Rule Selection
Selecting rules to efficiently solve a
problem is difficult
Some goals can be reached with only a few
rules; rules 3 and 4 identify bird
13-30
Inference Engine
Performs reasoning by using the contents of
knowledge base in a particular sequence
Two basic approaches to using rules
– 1. Forward reasoning (data driven)
– 2. Reverse reasoning (goal driven)
13-31
Forward Reasoning
(Forward Chaining)
Rule is evaluated as:
– (1) true, (2) false, (3) unknown
Rule evaluation is an iterative process
When no more rules can fire, the reasoning
process stops even if a goal has not been
reached
Start with inputs and
work to solution
13-32
Rule 1
IF A
THEN B
Rule 2
IF C
THEN D
Rule 3
IF M
THEN E
Rule 4
IF K
THEN F
Rule 5
IF G
THEN H
Rule 6
IF I
THEN J
T
Rule 7
F
T
IF B OR D
THEN K
T
Rule 8
IF E
THEN L
The
Forward
Rule 10
Reasoning
IF K AND
L THEN N
Process
T
Rule 12
IF N OR O
THEN P
T
T
Legend:
Rule 9
T
T
IF
IF(F
(FAND
ANDH)
H)
OR
ORJJ
THEN
THENMM
T
Rule 11
IF M
THEN O
First pass
T
Second pass
Third pass
F
13-33
Reverse Reasoning Steps
(Backward Chaining)
Divide problem into subproblems
Try to solve one subproblem
Then try another
Start with solution
and work back to
inputs
13-34
Step 4
Rule 1
IF A THEN
B
T
Rule 2
Step 3
Rule 7
IF B OR D
THEN K
T
IF C
THEN D
The First Five
Problems
Are
Step 2 Identified
Rule 10
IF K AND L
THEN N
Step 5
Rule 3
IF N OR O
THEN P
Rule 8
IF M
THEN E
IF E
THEN L
Rule 11
Rule 9
13-35
Step 1
Rule 12
IF (F AND H)
OR J
THEN M
IF M
M
IF
THEN O
THEN
O
Legend:
Problems to
be solved
The Next Four Problems Are
Rule 12
Identified
Step 8
If N Or O
Then P T
Rule 4
If K
Then F
Rule 5
T
Step 9
If G
Then H
Rule 6
If I
Then J
T
Step 7
Step 6
IF (F And H)
Or J
Then M T
If M
Then O
Rule 9
T
Rule 11
Legend:
Problems to
be solved
13-36
Forward Versus Reverse
Reasoning
Reverse reasoning is faster than forward
reasoning
Reverse reasoning works best under certain
conditions
–
–
–
Multiple goal variables
Many rules
All or most rules do not have to be examined in
the process of reaching a solution
13-37
Development Engine
Programming languages
– Lisp
– Prolog
Expert system shells
– Ready made processor that can be tailored to a
particular problem domain
Case-based reasoning (CBR)
Decision tree
13-38
Expert System Advantages
For managers
– Consider more alternatives
– Apply high level of logic
– Have more time to evaluate decision rules
– Consistent logic
For the firm
– Better performance from management team
– Retain firm’s knowledge resource
13-39
Expert System
Disadvantages
Can’t handle inconsistent knowledge
Can’t apply judgment or intuition
13-40
Keys to Successful ES
Development
Coordinate ES development with strategic planning
Clearly define problem to be solved and understand
problem domain
Pay particular attention to ethical and legal feasibility
of proposed system
Understand users’ concerns and expectations
concerning system
Employ management techniques designed to retain
developers
13-41
Neural Networks
Mathematical model of the human brain
– Simulates the way neurons interact to process
data and learn from experience
Bottom-up approach to modeling human
intuition
13-42
The Human Brain
Neuron -- the information processor
– Input -- dendrites
– Processing -- soma
– Output -- axon
Neurons are connected by the synapse
13-43
Simple Biological Neurons
Soma
(processor)
Axonal Paths
(output)
Synapse
Axon
Dendrites
(input)
13-44
Evolution of Artificial
Neural Systems (ANS)
McCulloch-Pitts mathematical neuron
function (late 1930s) was the starting point
Hebb’s learning law (early 1940s)
Neurocomputers
– Marvin Minsky’s Snark (early 1950s)
– Rosenblatt’s Perceptron (mid 1950s)
13-45
Current Methodology
Mathematical models don’t duplicate
human brains, but exhibit similar abilities
Complex networks
Repetitious training
– ANS “learns” by example
13-46
Single Artificial Neuron
y1
w1
y2
w2
y3
w3
wn-1
yn-1
y
13-47
OUT1
OUTn
The MultiLayer
Perceptron
Input
Layer
Y1
Yn2
OutputL
ayer
IN1
INn
13-48
Knowledge-based Systems
in Perspective
Much has been accomplished in neural nets
and expert systems
Much work remains
Systems abilities to mimic human
intelligence are too limited and regarded as
primitive
13-49
Summary [cont.]
AI
– Neural networks
– Expert systems
Limitations and promise
13-50
Raymond McLeod, Jr. and George Schell
Chapter 13
Decision Support Systems
13-1
Copyright 2001 Prentice-Hall, Inc.
Simon’s Types of Decisions
Programmed decisions
– repetitive and routine
– have a definite procedure
Nonprogrammed decisions
– Novel and unstructured
– No cut-and-dried method for handling problem
Types exist on a continuum
13-2
Simon’s Problem Solving
Phases
Intelligence
– Searching environment for conditions calling for a solution
Design
– Inventing, developing, and analyzing possible courses of
action
Choice
– Selecting a course of action from those available
Review
– Assessing past choices
13-3
Definitions of a Decision
Support System (DSS)
General definition - a system providing both
problem-solving and communications capabilities
for semistructured problems
Specific definition - a system that supports a
single manager or a relatively small group of
managers working as a problem-solving team in
the solution of a semistructured problem by
providing information or making suggestions
concerning specific decisions.
13-4
The DSS Concept
Gorry and Scott Morton coined the phrase ‘DSS’ in
1971, about ten years after MIS became popular
Decision types in terms of problem structure
– Structured problems can be solved with algorithms and
decision rules
– Unstructured problems have no structure in Simon’s
phases
– Semistructured problems have structured and
unstructured phases
13-5
The Gorry and Scott Morton Grid
Management levels
Operational
control
Structured
Degree of
problem
structure
Semistructured
Unstructured
Management
control
Strategic
planning
Accounts
receivable
Budget analysis-engineered costs
Tanker fleet
mix
Order entry
Short-term
forecasting
Warehouse and
factory location
Inventory
control
Production
scheduling
Variance analysis-overall budget
Mergers and
acquisitions
Cash
management
Budget
preparation
New product
planning
PERT/COST
systems
Sales and
production
R&D planning
13-6
Alter’s DSS Types
In 1976 Steven Alter, a doctoral student
built on Gorry and Scott-Morton framework
– Created a taxonomy of six DSS types
– Based on a study of 56 DSSs
Classifies DSSs based on “degree of
problem solving support.”
13-7
Levels of Alter’s DSSs
Level of problem-solving support from
lowest to highest
–
–
–
–
–
–
Retrieval of information elements
Retrieval of information files
Creation of reports from multiple files
Estimation of decision consequences
Propose decisions
Make decisions
13-8
Importance of Alter’s
Study
Supports concept of developing systems
that address particular decisions
Makes clear that DSSs need not be
restricted to a particular application type
13-9
Alter’s DSS Types
Retrieve
information
elements
Little
Analyze
entire
files
Prepare
reports
from
multiple
files
Estimate
decision
consequences
Degree of
complexity of the
problem-solving
system
Propose
decisions
Make
decisions
Degree
of
problem
solving
support
Much
13-10
Three DSS Objectives
1. Assist in solving semistructured problems
2. Support, not replace, the manager
3. Contribute to decision effectiveness, rather
than efficiency
Based on studies of Keen and Scott-Morton
13-11
A DSS Model
Environment
Individual
problem
solvers
Other
group
members
Report
writing
software
GDSS
GDSS
software
software
Mathematical
Models
Database
Decision
support
system
Legend:
Environment
Data
Communication
Information
13-12
Database Contents
Used by Three Software Subsystems
– Report writers
» Special reports
» Periodic reports
» COBOL or PL/I
» DBMS
– Mathematical models
» Simulations
» Special modeling languages
– Groupware or GDSS
13-13
Group Decision Support
Systems
Computer-based system that supports groups of
people engaged in a common task (or goal) and
that provides an interface to a shared environment.
Used in problem solving
Related areas
–
–
–
–
Electronic meeting system (EMS)
Computer-supported cooperative work (CSCW)
Group support system (GSS)
Groupware
13-14
How GDSS Contributes
to Problem Solving
Improved communications
Improved discussion focus
Less wasted time
13-15
GDSS Environmental
Settings
Synchronous exchange
– Members meet at same time
– Committee meeting is an example
Asynchronous exchange
–
–
Members meet at different times
E-mail is an example
More balanced participation.
13-16
GDSS Types
Decision rooms
–
–
–
Small groups face-to-face
Parallel communication
Anonymity
Local area decision network
– Members interact using a LAN
Legislative session
– Large group interaction
Computer-mediated conference
– Permits large, geographically dispersed group interaction
13-17
Group Size and Location Determine
GDSS Environmental Settings
GROUP SIZE
Face-toface
MEMBER
PROXIMITY
Dispersed
Smaller
Larger
Decision
Room
Legislative
Session
Local Area
Decision
Network
ComputerMediated
Conference
13-18
Groupware
Functions
– FAX
– Voice messaging
– Internet access
Lotus Notes
– Popular groupware product
– Handles data important to managers
13-19
Main Groupware Functions
IBM
TeamWARE Lotus
Function
Workgroup Office
Notes
Electronic mail
X
X
X
FAX
X
X
O
Voice messaging
O
Internet access
X
X
O
Bulletin board system
X
3
Personal calendaring
X
X
3
Group calendaring
X
X
O
Electronic conferencing
O
X
3
Task management
X
X
3
Desktop video conferencingO
Database access
O
X
3
Workflow routing
O
X
3
Reengineering
O
X
3
Electronic forms
O
3
3
Group documents
O
X
X
X = standard feature
O = optional feature
Novell
GroupWise
X
X
X
X
O
X
X
3
X
X
O
O
3 = third party offering
13-20
Artificial Intelligence (AI)
The activity of providing such
machines as computers with the
ability to display behavior that
would be regarded as intelligent if
it were observed in humans.
13-21
History of AI
Early history
– John McCarthy coined term, AI, in 1956, at
Dartmouth College conference.
– Logic Theorist (first AI program. Herbert Simon
played a part)
– General problem solver (GPS)
Past 2 decades
– Research has taken a back seat to MIS and DSS
development
13-22
Areas of Artificial Intelligence
Expert
systems
AI
hardware
Robotics
Natural
language
Learning
Neural
networks
Perceptive
systems
(vision,
hearing)
Artificial Intelligence
13-23
Appeal of Expert Systems
Computer program that codes the
knowledge of human experts in the form of
heuristics
Two distinctions from DSS
– 1. Has potential to extend manager’s problemsolving ability
– 2. Ability to explain how solution was reached
13-24
User
Instructions &
information
Solutions &
explanations
Knowledge
User
interface
Inference
engine
Expert
system
Development
engine
Expert and
knowledge engineer
Knowledge
base
Problem
Domain
An Expert
System Model
13-25
Expert System Model
User interface
– Allows user to interact with system
Knowledge base
– Houses accumulated knowledge
Inference engine
– Provides reasoning
– Interprets knowledge base
Development engine
– Creates expert system
13-26
User Interface
User enters:
– Instructions
– Information
}
Menus, commands, natural language, GUI
Expert system provides:
–
–
Solutions
Explanations of
» Questions
» Problem solutions
13-27
Knowledge Base
Description of problem domain
Rules
–
–
–
Knowledge representation technique
‘IF:THEN’ logic
Networks of rules
» Lowest levels provide evidence
» Top levels produce 1 or more conclusions
» Conclusion is called a Goal variable.
13-28
A Rule Set That
Produces One Final
Conclusion
Conclusion
Conclusion
Evidence
Evidence
Conclusion
Evidence
Evidence
Evidence
Evidence
Evidence
Evidence
13-29
Rule Selection
Selecting rules to efficiently solve a
problem is difficult
Some goals can be reached with only a few
rules; rules 3 and 4 identify bird
13-30
Inference Engine
Performs reasoning by using the contents of
knowledge base in a particular sequence
Two basic approaches to using rules
– 1. Forward reasoning (data driven)
– 2. Reverse reasoning (goal driven)
13-31
Forward Reasoning
(Forward Chaining)
Rule is evaluated as:
– (1) true, (2) false, (3) unknown
Rule evaluation is an iterative process
When no more rules can fire, the reasoning
process stops even if a goal has not been
reached
Start with inputs and
work to solution
13-32
Rule 1
IF A
THEN B
Rule 2
IF C
THEN D
Rule 3
IF M
THEN E
Rule 4
IF K
THEN F
Rule 5
IF G
THEN H
Rule 6
IF I
THEN J
T
Rule 7
F
T
IF B OR D
THEN K
T
Rule 8
IF E
THEN L
The
Forward
Rule 10
Reasoning
IF K AND
L THEN N
Process
T
Rule 12
IF N OR O
THEN P
T
T
Legend:
Rule 9
T
T
IF
IF(F
(FAND
ANDH)
H)
OR
ORJJ
THEN
THENMM
T
Rule 11
IF M
THEN O
First pass
T
Second pass
Third pass
F
13-33
Reverse Reasoning Steps
(Backward Chaining)
Divide problem into subproblems
Try to solve one subproblem
Then try another
Start with solution
and work back to
inputs
13-34
Step 4
Rule 1
IF A THEN
B
T
Rule 2
Step 3
Rule 7
IF B OR D
THEN K
T
IF C
THEN D
The First Five
Problems
Are
Step 2 Identified
Rule 10
IF K AND L
THEN N
Step 5
Rule 3
IF N OR O
THEN P
Rule 8
IF M
THEN E
IF E
THEN L
Rule 11
Rule 9
13-35
Step 1
Rule 12
IF (F AND H)
OR J
THEN M
IF M
M
IF
THEN O
THEN
O
Legend:
Problems to
be solved
The Next Four Problems Are
Rule 12
Identified
Step 8
If N Or O
Then P T
Rule 4
If K
Then F
Rule 5
T
Step 9
If G
Then H
Rule 6
If I
Then J
T
Step 7
Step 6
IF (F And H)
Or J
Then M T
If M
Then O
Rule 9
T
Rule 11
Legend:
Problems to
be solved
13-36
Forward Versus Reverse
Reasoning
Reverse reasoning is faster than forward
reasoning
Reverse reasoning works best under certain
conditions
–
–
–
Multiple goal variables
Many rules
All or most rules do not have to be examined in
the process of reaching a solution
13-37
Development Engine
Programming languages
– Lisp
– Prolog
Expert system shells
– Ready made processor that can be tailored to a
particular problem domain
Case-based reasoning (CBR)
Decision tree
13-38
Expert System Advantages
For managers
– Consider more alternatives
– Apply high level of logic
– Have more time to evaluate decision rules
– Consistent logic
For the firm
– Better performance from management team
– Retain firm’s knowledge resource
13-39
Expert System
Disadvantages
Can’t handle inconsistent knowledge
Can’t apply judgment or intuition
13-40
Keys to Successful ES
Development
Coordinate ES development with strategic planning
Clearly define problem to be solved and understand
problem domain
Pay particular attention to ethical and legal feasibility
of proposed system
Understand users’ concerns and expectations
concerning system
Employ management techniques designed to retain
developers
13-41
Neural Networks
Mathematical model of the human brain
– Simulates the way neurons interact to process
data and learn from experience
Bottom-up approach to modeling human
intuition
13-42
The Human Brain
Neuron -- the information processor
– Input -- dendrites
– Processing -- soma
– Output -- axon
Neurons are connected by the synapse
13-43
Simple Biological Neurons
Soma
(processor)
Axonal Paths
(output)
Synapse
Axon
Dendrites
(input)
13-44
Evolution of Artificial
Neural Systems (ANS)
McCulloch-Pitts mathematical neuron
function (late 1930s) was the starting point
Hebb’s learning law (early 1940s)
Neurocomputers
– Marvin Minsky’s Snark (early 1950s)
– Rosenblatt’s Perceptron (mid 1950s)
13-45
Current Methodology
Mathematical models don’t duplicate
human brains, but exhibit similar abilities
Complex networks
Repetitious training
– ANS “learns” by example
13-46
Single Artificial Neuron
y1
w1
y2
w2
y3
w3
wn-1
yn-1
y
13-47
OUT1
OUTn
The MultiLayer
Perceptron
Input
Layer
Y1
Yn2
OutputL
ayer
IN1
INn
13-48
Knowledge-based Systems
in Perspective
Much has been accomplished in neural nets
and expert systems
Much work remains
Systems abilities to mimic human
intelligence are too limited and regarded as
primitive
13-49
Summary [cont.]
AI
– Neural networks
– Expert systems
Limitations and promise
13-50