Turban, Aronson, and Liang
Decision Support Systems and Intelligent Systems,
Seventh Edition
Chapter 2
Decision-Making Systems, Models,
and Support
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
2-1
Learning Objectives
• Learn the basic concepts of decision
making.
• Understand systems approach.
• Learn Simon’s four phases of
decision making.
• Learn which factors affect decision
making.
• Learn how DSS supports decision
making in practice.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Standard Motor Products Shifts Gears
Into Team-Based Decision-Making
Vignette
• Team-based decision making
– Increased information sharing
– Daily feedback
– Self-empowerment
• Shifting responsibility towards teams
• Elimination of middle management
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2-3
Typical Business Decision
Aspects
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Decision may be made by a group
Group member biases
Groupthink
Several, possibly contradictory objectives
Many alternatives
Results can occur in the future
Attitudes towards risk
Need information
Gathering information takes time and expense
Too much information
“What-if” scenarios
Trial-and-error experimentation with the real system may result in a loss
Experimentation with the real system - only once
Changes in the environment can occur continuously
Time pressure
4
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Decision Making
• Process of choosing amongst
alternative courses of action for the
purpose of attaining a goal or goals.
• The four phases of the decision
process are:
– Intelligence
– Design
– Choice
– implementation
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What each Phase consists of?
• The Intelligence Phase consists of:
- Organizational objectives. - Search and scanning procedures.
- Data collection.
- Problem identification.
- Problem ownership.
- Problem classification.
- Problem statement.
•
The Design Phase consists of:
- Formulate a model.
- Search for alternatives.
•
- Set criteria for choice.
- Predict and measure outcomes.
The Choice Phase consists of:
- Solution to the model.
- Sensitivity analysis.
- Selection of the best (good) alternative (s).
- Plan for implementation.
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• Managerial Decision Making is synonymous
with the whole process of management
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Systems
•
A SYSTEM is a collection of objects such as
people, resources, concepts, and procedures
intended to perform an identifiable function or to
serve a goal
•
System Levels (Hierarchy): All systems are
subsystems interconnected through interfaces
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Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Systems
• Structure
–
–
–
–
Inputs
Processes
Outputs
Feedback from output to decision maker
• Separated from environment by boundary
• Surrounded by environment
Input
Processes
Output
boundary
Environment
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– Inputs: are elements that enter the system.
– Processes: are all the necessary to convert or
transform inputs into outputs.
– Outputs: are the finished products or the
consequences of being in the system.
– Feedback from output to decision maker; there is
a flow of information from the output component
to the decision-maker concerning the system’s
output or performance. Based on the outputs, the
decision-maker, may decide to modify the inputs,
the processes, or both. the decision-maker
compares the output to the expected output and
adjusts the input and possibly the processes to
move close to the output targets.
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• The environment: Is composed of several
elements that lie outside in in the sense
that they are not inputs, output, or
processes. However they affect the
system’s performance and consequently
the attainment of its goals. Environmental
elements can be social, political, legal,
physical, or economic .
• The Boundary: A system is separated
from its environment by boundary.
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Environmental Elements Can Be
•
•
•
•
•
Social
Political
Legal
Physical
Economical
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Decision
Support
Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Turban, Aronson,
and
Liang
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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The Boundary Separates a
System From Its
Environment
Boundaries may be physical or nonphysical (by definition
of scope or time frame)
Information system boundaries are usually by definition!
14
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
System Types
• Closed system
–
–
–
–
Independent
Takes no inputs
Delivers no outputs to the environment
Black Box: is one which inputs and outputs are
well defined, but the process itself is not
specified.
Such as transaction processing system (TPS).
• Open system
– Very Dependant on it environment.
– Accepts inputs from the environment.
– Delivers outputs to environment
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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An Information System
•
Collects, processes, stores, analyzes, and disseminates
information for a specific purpose
•
Is often at the heart of many organizations
•
Accepts inputs and processes data to provide
information to decision makers and helps decision
makers communicate their results
16
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
System Effectiveness and
Efficiency
Two Major Classes of Performance Measurement
•
Effectiveness is the degree to which goals are achieved
Doing the right thing!
•
Efficiency is a measure of the use of inputs (or
resources) to achieve outputs
Doing the thing right!
•
MSS emphasize effectiveness
Often: several non-quantifiable, conflicting goals
17
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Models
•
•
•
•
•
Major component of DSS
Use models instead of experimenting on the real
system
A model is a simplified representation or
abstraction of reality.
Reality is generally too complex to copy exactly
Much of the complexity is actually irrelevant in
problem solving
18
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Models Used for DSS
• Iconic
– Small physical replication of system, it may be
three dimensional such as that of an airplane,
car, or production line. Or two-dimensional such
as photographs.
• Analog
– Behavioral representation of system
– May not look like system
Ex. Stock market charts that represent the price movements
of stocks. Animations, videos, and movies.
• Quantitative (mathematical)
– Demonstrates relationships between systems
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© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Benefits of Models
1. Time compression
2. Easy model manipulation
3. Low cost of construction
4. Low cost of execution (especially that of errors)
5. Can model risk and uncertainty
6. Can model large and extremely complex systems
with possibly infinite solutions
7. Enhance and reinforce learning, and enhance
training.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Decision
Support
Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Turban, Aronson,
and
Liang
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
2-21
Phases of Decision-Making
• Simon’s original three phases:
– Intelligence
– Design
– Choice
• He added fourth phase later:
– Implementation
• Book adds fifth stage:
– Monitoring
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Decision-Making Intelligence
Phase
•
•
•
•
•
Scan the environment
Analyze organizational goals
Collect data
Identify problem
Categorize problem
– Programmed and non-programmed (p55)
– Decomposed into smaller parts (p55)
• Assess ownership and responsibility for
problem resolution
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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The Intelligence Phase
Scan the environment to identify problem situations or
opportunities
Find the Problem
•
•
•
Identify organizational goals and objectives
Determine whether they are being met
Explicitly define the problem
24
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Problem Classification
Structured versus Unstructured
Programmed versus Nonprogrammed Problems
Simon (1977)
Nonprogrammed
Problems
Programmed
Problems
25
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
•
Problem Decomposition: Divide a complex problem
into (easier to solve) subproblems
Chunking (Salami)
•
Some seemingly poorly structured problems may
have some highly structured subproblems
•
Problem Ownership
Outcome: Problem Statement
26
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Decision-Making Design Phase
•
•
•
•
•
•
Develop alternative courses of action
Analyze potential solutions
Create model
Test for feasibility
Validate results
Select a principle of choice
– Establish objectives
– Risk assessment and acceptance
– Criteria and constraints
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Decision-Making Choice Phase
• Principle of choice
– Is a criterion that Describes acceptability of a
solution approach
• Normative Models
– Optimization
• Effect of each alternative
– Rationalization
• More of good things, less of bad things
• Courses of action are known quantity
• Options ranked from best to worse
– Suboptimization
• Decisions made in separate parts of organization
without consideration of whole
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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• Normative Models:
are those in which the chosen
alternative is demonstrably the best of
all possible alternatives. To find it,
one should examine all alternatives
and prove that one selected is indeed
the best, which is what one would
normally want.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Normative Models
•
The chosen alternative is demonstrably the best of
all (normally a good idea)
•
Optimization process
•
Normative decision theory based on rational decision
makers
30
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
The Principle of Choice
•
•
•
What criteria to use?
Best solution?
Good enough solution?
31
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Selection of a
Principle of Choice
Not the choice phase
A decision
regarding the acceptability
of a solution approach
•
•
Normative
Descriptive
32
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
The Modeling Process-A Preview
Solution Approaches
•
•
•
•
Trial-and-Error
Simulation
Optimization
Heuristics
33
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Components of
Quantitative Models
•
•
•
•
•
Decision Variables
Uncontrollable Variables (and/or Parameters)
Result (Outcome) Variables
Mathematical Relationships
or
Symbolic or Qualitative Relationships
(Figure 2.3)
34
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Results of Decisions are
Determined by the
•
•
•
Decision
Uncontrollable Factors
Relationships among Variables
35
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Decision Variables
•
•
•
Describe alternative courses of action
The decision maker controls them
Examples - Table 2.2
36
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Uncontrollable Variables or
Parameters
•
•
•
•
•
Factors that affect the result variables
Not under the control of the decision maker
Generally part of the environment
Some constrain the decision maker and are called
constraints
Examples - Table 2.2
Intermediate Result Variables
• Reflect intermediate outcomes
37
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Rationality Assumptions
•
Humans are economic beings whose objective is to
maximize the attainment of goals; that is, the decision
maker is rational
•
In a given decision situation, all viable alternative
courses of action and their consequences, or at least the
probability and the values of the consequences, are
known
•
Decision makers have an order or preference that
enables them to rank the desirability of all
consequences of the analysis
38
Descriptive Models
• Describe things as they are,or how things
are believed to be
• These Model are Typically, mathematically
based
• Applies single set of alternatives
• Examples:
– Simulations
– What-if scenarios
– Cognitive map: understand issues better, focus better
and reach closure
– Narratives: is a story that, when told, helps a decision
maker uncover the important aspects of the situation and
leads to better understanding and framing.
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Descriptive Models
•
•
•
•
•
Describe things as they are, or as they are believed
to be
Extremely useful in DSS for evaluating the
consequences of decisions and scenarios
No guarantee a solution is optimal
Often a solution will be good enough
Simulation: Descriptive modeling technique
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Satisficing (Good Enough)
•
Most human decision makers will settle for a good
enough solution
•
Tradeoff: time and cost of searching for an
optimum versus the value of obtaining one
•
Good enough or satisficing solution may meet a
certain goal level is attained
(Simon, 1977)
41
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Why Satisfice?
Bounded Rationality (Simon)
•
•
•
•
•
Humans have a limited capacity for rational thinking
Generally construct and analyze a simplified model
Behavior to the simplified model may be rational
But, the rational solution to the simplified model may
NOT BE rational in the real-world situation
Rationality is bounded by
– limitations on human processing capacities
– individual differences
•
Bounded rationality: why many models are descriptive,
not normative
42
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Predicting the Outcome of
Each Alternative
•
•
•
Must predict the future outcome of each proposed
alternative
Consider what the decision maker knows (or
believes) about the forecasted results
Classify Each Situation as Under
– Certainty
– Risk
– Uncertainty
43
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Decision Making Under
Certainty
•
•
•
•
Assumes complete knowledge available
(deterministic environment)
Example: U.S. Treasury bill investment
Typically for structured problems with short
time horizons
Sometimes DSS approach is needed for certainty
situations
44
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Decision Making Under Risk
(Risk Analysis)
•
•
•
•
Probabilistic or stochastic decision situation
Must consider several possible outcomes for each
alternative, each with a probability
Long-run probabilities of the occurrences of the
given outcomes are assumed known or estimated
Assess the (calculated) degree of risk associated with
each alternative
45
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Risk Analysis
•
Calculate the expected value of each alternative
•
Select the alternative with the best expected value
•
Example: poker game with some cards face up (7
card game - 2 down, 4 up, 1 down)
46
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Decision Making Under
Uncertainty
•
•
•
•
•
Several outcomes possible for each course of action
BUT the decision maker does not know, or cannot
estimate the probability of occurrence
More difficult - insufficient information
Assessing the decision maker's (and/or the
organizational) attitude toward risk
Example: poker game with no cards face up (5 card
stud or draw)
47
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Measuring Outcomes
•
•
•
•
•
Goal attainment
Maximize profit
Minimize cost
Customer satisfaction level (minimize number of
complaints)
Maximize quality or satisfaction ratings (surveys)
48
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Scenarios
Useful in
•
•
Simulation
What-if analysis
49
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Importance of Scenarios in
MSS
•
•
•
•
•
Help identify potential opportunities and/or
problem areas
Provide flexibility in planning
Identify leading edges of changes that management
should monitor
Help validate major assumptions used in modeling
Help check the sensitivity of proposed solutions to
changes in scenarios
50
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Decision-Making Choice Phase
• Decision making with commitment to
act
• Determine courses of action
– Analytical techniques ( solving a formula)
– Algorithms( step-by-step procedures)
– Heuristics (rules of thumb)
– Blind searches( shooting in the dark, ideally in a logical way)
• Analyze for robustness
2-51
Decision-Making
Implementation Phase
• Putting solution to work
• Vague (unknown) boundaries which
include:
– Dealing with resistance to change
– User training
– Upper management support
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Source: Based on Sprague, R.H., Jr., “A Framework for the Development of DSS.” MIS Quarterly, Dec. 1980, Fig. 5, p. 13.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Decision Support Systems
• Intelligence Phase
– Automatic
• Data Mining
– Expert systems, CRM, neural networks
– Manual
• OLAP
• KMS
– Reporting
• Routine and ad hoc
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Decision Support Systems
• Design Phase
– Financial and forecasting models
– Generation of alternatives by expert
system
– Business process models from CRM,
ERP, and SCM
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Decision Support Systems
• Choice Phase
– Identification of best alternative
– Identification of good enough alternative
– What-if analysis
– Goal-seeking analysis
– May use KMS, GSS, CRM, ERP, and
SCM systems
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Decision Support Systems
• Implementation Phase
– Improved communications
– Collaboration
– Training
– Supported by KMS, expert systems,
GSS
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Other Important DecisionMaking Issues
•
•
•
•
Personality types
Gender
Human cognition
Decision styles
58
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Personality (Temperament)
Types
•
•
•
Strong relationship between personality and
decision making
Type helps explain how to best attack a
problem
Type indicates how to relate to other types
– important for team building
•
Influences cognitive style and decision style
59
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Gender
•
•
Sometimes empirical testing indicates
gender differences in decision making
Results are overwhelmingly
inconclusive
60
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Bias and Heuristics in DSSs
• Heuristics are often built through trail-and-error
experience
• If heuristics are well tested, they can serve as a
reliable tool for reducing the search space for
alternatives
• Search directed by heuristics is usually less costly
and more efficient than blind search
• Heuristics can provide solutions close to those
produced by a comprehensive blind search with
regards to quality
Advantages of using heuristics in problem solving
– Simple to understand
– Easy to implement
– Requires less time
– Require less cognitive effort
– Can produce multiple solutions
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When to use Heuristics
– The input data are limited
– The computation time for the optimal solution is excessive
– Problems that are being solved frequently
– The efficiency of an optimisation process can be improved
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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Decision Styles
The manner in which decision makers think and react to
problems
•
Varies from individual to individual and from situation to
situation
64
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Types of Decision styles
–
Directive: low tolerance of context ambiguity. Does not
requires large volumes of information and verbal
communication
is preferable on writing methods for managers
– Analytical: High tolerance of context ambiguity and
requires
great values of information. Not quick in taking
decisions.
– Conceptual: The “people person” and they tend to be
thinkers
rather than doers.
– Behavioural: It requires low amount of input data and
demonstrate a short-rang vision
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The Decision Makers
•
•
Individuals
Groups
66
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Individuals
•
•
May still have conflicting objectives
Decisions may be fully automated
67
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Groups
•
•
•
•
•
•
•
•
Most major decisions 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 decisionmaking situations
68
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
• Technology is used to access information and
data. Describe how information technology can
help the teams.
Information technology is used to provide immediate
access to information to each team member.
Information technology is used for group support
group discussions directly, through technologies
such as interactive chat or indirectly such as
through the use of email. Information technology
can also help team disseminate information
through technologies such as Web portals.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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•
Review what is meant by decision-making
versus problem-solving. Compare the two, and
determine whether or not it makes sense to
distinguish between them..
It is a matter of definition. Some people
consider decision-making as a step in
problem-solving,. Some people refer to
decision-making as the process of making
a recommendation, whereas problemsolving includes the implementation of the
recommendation.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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2-70
•
•
•
Compare the normative ( standard) and
descriptive approaches to decisionmaking.
Normative refers to models that tell you
what you should do. These are
prescriptive models that usually utilize
optimization.
Descriptive models are those that tell you
"what-if." These are usually simulation
models.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
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2-71
•
What is the impact on decision-making of
giving people responsibility for their own work?
Why are self-directed team members happier
than workers under a traditional hierarchy?
•
• Responsibility for their work will allow people to
feel they are truly empowered to make decisions
and therefore will be more willing to do so. Selfdirected teams feel more in control of their own
destiny, they have more control over their work
activities.
•
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