03Sdss13.ppt 303KB May 20 2010 09:29:42 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
–



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

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.
2.
3.

Assist in solving semistructured problems
Support, not replace, the manager
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

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
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
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