Transcript Document

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
–

Design
–

Inventing, developing, and analyzing possible courses
of action
Choice
–

Searching environment for conditions calling for a
solution
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
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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
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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
Report
writing
software
Other
group
members
GDSS
GDSS
software
software
Mathematical
Models
Database
Decision
support
system
Environment
Legend:
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
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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
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How GDSS Contributes
to Problem Solving
Improved communications
 Improved discussion focus
 Less wasted time

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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
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interaction
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
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Main Groupware Functions
Function
IBM
TeamWARE Lotus
Workgroup Office
Notes
Electronic mail
X
FAX
X
Voice messaging
Internet access
X
Bulletin board system
Personal calendaring
X
Group calendaring
X
Electronic conferencing
O
Task management
X
Desktop video conferencingO
Database access
O
Workflow routing
O
Reengineering
O
Electronic forms
O
Group documents
O
X = standard feature
O = optional feature
X
X
X
X
X
X
X
X
X
O
O
O
3
3
O
3
3
X
X
X
3
X
3
3
3
3
X
Novell
GroupWise
X
X
X
X
O
X
X
3
X
X
O
O
3 = third party offering
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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.
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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
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Areas of Artificial Intelligence
Expert
systems
Natural
language
processing
Learning
AI
hardware
Robotics
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
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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.
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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)
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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
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Rule 1
IF A
THEN B
Rule 2
T
Rule 7
F
IF B OR D
THEN K
IF C
THEN D
Rule 3
T
Rule 10
IF K AND
L THEN N
The Forward
Reasoning
Process
T
T
IF M
THEN E
Rule 8
Rule 12
T
IF N OR O
THEN P
IF E
THEN L
T
Rule 4
IF K
THEN F
T
Legend:
Rule 9
Rule 5
IF G
THEN H
T
IF (F AND H)
OR J
THEN M
T
First pass
Rule 11
IF M
THEN O
T
Second pass
Rule 6
IF I
THEN J
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
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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 Identified
Step 2
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
Identified
Step 8
If N Or O
Then P T
Rule 4
If K
Then F
Rule 5
Rule 12
T
Step 7
Step 6
IF (F And H)
Or J
Then M T
If M
Then O
Step 9
If G
Then H
T
Rule 6
If I
Then J
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
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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 Multi-Layer
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