Management
Management
Information Systems,
Information Systems,
10/e
10/e
Raymond McLeod and George
Raymond McLeod and George
Schell
Chapter 11
Chapter 11
Decision Support Systems
Learning Objectives
Learning Objectives
►
Understand the fundamentals of
Understand the fundamentals of
decision making & problem solving.
decision making & problem solving.
►
Know how the decision support system
Know how the decision support system
(DSS) concept originated.
(DSS) concept originated.
►
Know the fundamentals of
Know the fundamentals of
mathematical modeling.
mathematical modeling.
►
Know how to use an electronic
Know how to use an electronic
spreadsheet as a mathematical model.
Learning Objectives (Cont’d)
Learning Objectives (Cont’d)
►
Be familiar with how artificial
Be familiar with how artificial
intelligence emerged as a computer
intelligence emerged as a computer
application & know its main areas.
application & know its main areas.
►
Know the four basic parts of an expert
Know the four basic parts of an expert
system.
system.
►
Know what a group decision support
Know what a group decision support
system (GDSS) is & the different
system (GDSS) is & the different
Problem-Solving & Decision
Problem-Solving & Decision
Making Review
Making Review
►
Problem solving
Problem solving
consists of response to
consists of response to
things going well & also to things going
things going well & also to things going
badly.
badly.
►
Problem
Problem
is a condition or event that is
is a condition or event that is
harmful or potentially harmful to a firm or
harmful or potentially harmful to a firm or
that is beneficial or potentially beneficial.
that is beneficial or potentially beneficial.
►
Decision making
Decision making
is the act of selecting
is the act of selecting
from alternative problem solutions.
from alternative problem solutions.
Problem-Solving Phases
Problem-Solving Phases
►
Herbert A. Simon’s
Herbert A. Simon’s
four basic phases:
four basic phases:
Intelligence phase
Intelligence phase
– Searching the
– Searching the
environment for conditions calling for a
environment for conditions calling for a
solution.
solution.
Design activity
Design activity
– inventing, developing,
– inventing, developing,
& analyzing possible course of actions.
& analyzing possible course of actions.
Choice activity
Choice activity
– Selecting a particular
– Selecting a particular
course of action from those available.
course of action from those available.
Frameworks & Systems
Frameworks & Systems
Approach
Approach
►
Problem-solving frameworks
Problem-solving frameworks
General systems model of the firm.
General systems model of the firm.
Eight-element environmental model.
Eight-element environmental model.
►
Systems approach to problem-solving,
Systems approach to problem-solving,
involves a series of steps grouped into
involves a series of steps grouped into
three phases – preparation effort,
three phases – preparation effort,
definition effort, & solution effort.
Importance of Systems View
Importance of Systems View
►
Systems view
Systems view
which regards business operations as
which regards business operations as
systems embedded within a larger environmental setting;
systems embedded within a larger environmental setting;
abstract way of thinking; potential value to the manager.
abstract way of thinking; potential value to the manager.
Prevents the manager from getting lost in the
Prevents the manager from getting lost in the
complexity of the organizational structure & details of
complexity of the organizational structure & details of
the job.
the job.
Recognizes the necessity of having good objectives.
Recognizes the necessity of having good objectives.
Emphasizes the importance of all of the parts of the
Emphasizes the importance of all of the parts of the
organization working together.
organization working together.
Acknowledges the interconnections of the organization
Acknowledges the interconnections of the organization
with its environment.
with its environment.
Places a high value on feedback information that can
Places a high value on feedback information that can
only be achieved by means of a closed-loop system.
Building on the Concepts
Building on the Concepts
►
Elements of a problem-solving phase.
Elements of a problem-solving phase.
Desired state
Desired state
– what the system should achieve.
– what the system should achieve.
Current state
Current state
– what the system is now achieving.
– what the system is now achieving.
Solution criterion
Solution criterion
– difference between the current
– difference between the current
state & the desired state.
state & the desired state.
►
Constraints.
Constraints.
Internal
Internal
take the form of limited resources that exist
take the form of limited resources that exist
within the firm.
within the firm.
Environmental
Environmental
take the form of pressures from
take the form of pressures from
various environmental elements that restrict the flow of
various environmental elements that restrict the flow of
resources into & out of the firm.
resources into & out of the firm.
►
When all of these elements exist & the manager
When all of these elements exist & the manager
Figure 11.1 Elements of the
Figure 11.1 Elements of the
Selecting the Best Solution
Selecting the Best Solution
►
Henry Mintzberg
Henry Mintzberg
, management theorist,
, management theorist,
has identified three approaches:
has identified three approaches:
►
Analysis
Analysis
– a systematic evaluation of
– a systematic evaluation of
options.
options.
►
Judgment
Judgment
– the mental process of a
– the mental process of a
single manager.
single manager.
►
Bargaining
Bargaining
– negotiations between
– negotiations between
several managers.
Problem vs. Symptoms
Problem vs. Symptoms
►
Symptom
Symptom
is a condition produced by the problem.
is a condition produced by the problem.
►
Structured problem
Structured problem
consists of elements &
consists of elements &
relationships between elements, all of which are
relationships between elements, all of which are
understood by the problem solver.
understood by the problem solver.
►
Unstructured problem
Unstructured problem
is one that contains no
is one that contains no
elements or relationships between elements that
elements or relationships between elements that
are understood by the problem solver.
are understood by the problem solver.
►
Semistructured problem
Semistructured problem
is one that contains
is one that contains
some
some
elements or relationships that are
elements or relationships that are
understood by the problem solver & some that are
understood by the problem solver & some that are
not.
Types of Decisions
Types of Decisions
►
Programmed decisions
Programmed decisions
are
are
“repetitive & routine,
“repetitive & routine,
to the extent that a definite procedure has been
to the extent that a definite procedure has been
worked out for handling them so that they don’t
worked out for handling them so that they don’t
have to be treated de novo (as new) each time
have to be treated de novo (as new) each time
they occur.
they occur.
►
Nonprogrammed decisions
Nonprogrammed decisions
are “
are “
novel,
novel,
unstructured, & unusually consequential.
unstructured, & unusually consequential.
There’s no cut-and-dried method for handling
There’s no cut-and-dried method for handling
the problem because its precise nature &
the problem because its precise nature &
structure are elusive or complex, because it is so
structure are elusive or complex, because it is so
important that it deserves a custom-tailored
important that it deserves a custom-tailored
Decision Support Systems
Decision Support Systems
►
Gorry & Scott Morton (1971) argued that an
Gorry & Scott Morton (1971) argued that an
information system that focused on single problems
information system that focused on single problems
faced by single managers would provide better
faced by single managers would provide better
support.
support.
►
Central to their concept was a table, called the Gorry-
Central to their concept was a table, called the
Gorry-Scott Morton grid (Figure 11.2) that classifies problems
Scott Morton grid (Figure 11.2) that classifies problems
in terms of problem structure & management level.
in terms of problem structure & management level.
►
The top level is called the
The top level is called the
strategic planning level
strategic planning level
, the
, the
middle level - the
middle level - the
management control level
management control level
, & the
, & the
lower level - the
lower level - the
operational control level.
operational control level.
►
Gorry & Scott Morton also used the term
Gorry & Scott Morton also used the term
decision
decision
support system (DSS)
support system (DSS)
to describe the systems that
to describe the systems that
could provide the needed support.
Figure 11.2 The Gorry &
Figure 11.2 The Gorry &
A DSS Model
A DSS Model
►
Originally the DSS was conceived to produce periodic
Originally the DSS was conceived to produce periodic
& special reports (responses to database queries), &
& special reports (responses to database queries), &
outputs from mathematical models.
outputs from mathematical models.
►
An ability was added to permit problem solvers to work
An ability was added to permit problem solvers to work
in groups.
in groups.
►
The addition of groupware enabled the system to
The addition of groupware enabled the system to
function as a group decision support system (GDSS).
function as a group decision support system (GDSS).
►
Figure 11.3 is a model of a DSS. The arrow at the
Figure 11.3 is a model of a DSS. The arrow at the
bottom indicates how the configuration has expanded
bottom indicates how the configuration has expanded
over time.
over time.
►
More recently, artificial intelligence (AI) capability has
More recently, artificial intelligence (AI) capability has
been added, along with an ability to engage in online
been added, along with an ability to engage in online
Mathematical Modeling
Mathematical Modeling
►
Model
Model
is an abstraction of something. It represents
is an abstraction of something. It represents
some object or activity, which is called an
some object or activity, which is called an
entity.
entity.
►There are four basic types of models:
There are four basic types of models:
Physical model
Physical model
is a three-dimensional
is a three-dimensional
representation of its entity.
representation of its entity.
Narrative model
Narrative model
, which describes its entity with
, which describes its entity with
spoken or written words.
spoken or written words.
Graphic model
Graphic model
represents its entity with an
represents its entity with an
abstraction of lines, symbols, or shapes (Figure
abstraction of lines, symbols, or shapes (Figure
11.4).
11.4).
►Economic order quantity (EOQ)Economic order quantity (EOQ) is the optimum is the optimum
quantity of replenishment stock to order from a supplier.
quantity of replenishment stock to order from a supplier.
Mathematical model
Mathematical model
is any mathematical
is any mathematical
Formula to Compute Economic
Formula to Compute Economic
Figure 11.4 Graphical Model of
Figure 11.4 Graphical Model of
Uses of Models
Uses of Models
►
Facilitate Understanding:
Facilitate Understanding:
Once a simple model is
Once a simple model is
understood, it can gradually be made more complex
understood, it can gradually be made more complex
so as to more accurately represent its entity.
so as to more accurately represent its entity.
►
Facilitate Communication:
Facilitate Communication:
All four types of models
All four types of models
can communicate information quickly and accurately.
can communicate information quickly and accurately.
►
Predict the Future:
Predict the Future:
The mathematical model can
The mathematical model can
predict what might happen in the future but a
predict what might happen in the future but a
manager must use judgment & intuition in evaluating
manager must use judgment & intuition in evaluating
the output.
the output.
►
A mathematical model can be classified in terms of
A mathematical model can be classified in terms of
three dimensions: the influence of time, the degree of
three dimensions: the influence of time, the degree of
Classes of Mathematical
Classes of Mathematical
Models
Models
►
Static model
Static model
doesn’t include time as a variable
doesn’t include time as a variable
but deals only with a particular point in time.
but deals only with a particular point in time.
►
Dynamic
Dynamic
model
model
includes time as a variable;
includes time as a variable;
it
it
represents the behavior of the entity over time.
represents the behavior of the entity over time.
►
Probabilistic model
Probabilistic model
includes probabilities.
includes probabilities.
Otherwise, it is a
Otherwise, it is a
deterministic
deterministic
model
model
.
.
Probability
Probability
is the chance that something will happen.
is the chance that something will happen.
►
Optimizing model
Optimizing model
is one that selects the best
is one that selects the best
solution among the alternatives.
solution among the alternatives.
►
Suboptimizing
Suboptimizing
model (satisficing model)
model (satisficing model)
does
does
not identify the decisions that will produce the best
not identify the decisions that will produce the best
outcome but leaves that task to the manager.
Simulation
Simulation
►
The act of using a model is called
The act of using a model is called
simulation
simulation
while the
while the
term
term
scenario
scenario
is used to describe the conditions that
is used to describe the conditions that
influence a simulation.
influence a simulation.
►
For example, if you are simulating an inventory system,
For example, if you are simulating an inventory system,
as shown in Figure 11.5, the scenario specifies the
as shown in Figure 11.5, the scenario specifies the
beginning balance & the daily sales units.
beginning balance & the daily sales units.
►
Models can be designed so that the
Models can be designed so that the
scenario data
scenario data
elements
elements
are variables, thus enabling different values
are variables, thus enabling different values
to be assigned.
to be assigned.
►
The input values the manager enters to gauge their
The input values the manager enters to gauge their
impact on the entity are known as
impact on the entity are known as
decision variables.
decision variables.
►
Figure 11.5 gives an example of decision variables such
Figure 11.5 gives an example of decision variables such
Figure 11.5 Scenario Data &
Figure 11.5 Scenario Data &
Decision Variables from a
Decision Variables from a
Simulation Technique & Format
Simulation Technique & Format
of Simulation Output
of Simulation Output
►
The manager usually executes an optimizing
The manager usually executes an optimizing
model only a single time.
model only a single time.
►
Suboptimizing models, however, are run over &
Suboptimizing models, however, are run over &
over, in a search for the combination of
over, in a search for the combination of
decision variables that produces a satisfying
decision variables that produces a satisfying
outcome (known as playing the
outcome (known as playing the
what-if game
what-if game
).
).
►
Each time the model is run, only one decision
Each time the model is run, only one decision
variable should be changed, so its influence can
variable should be changed, so its influence can
be seen.
be seen.
►
This way, the problem solver systematically
This way, the problem solver systematically
discovers the combination of decisions leading
discovers the combination of decisions leading
to a desirable solution.
A Modeling Example
A Modeling Example
►
A firm’s executives may use a math model to assist
A firm’s executives may use a math model to assist
in making key decisions & to simulate the effect of:
in making key decisions & to simulate the effect of:
1.
1.
Price
Price
of the product;
of the product;
2.
2.
Amount of
Amount of
plant investment;
plant investment;
3.
3.
Amount to be invested in
Amount to be invested in
marketing
marketing
activity;
activity;
4.
4.
Amount to be invested in
Amount to be invested in
R & D.
R & D.
►
Furthermore, executives want to simulate 4 quarters
Furthermore, executives want to simulate 4 quarters
of activity & produce 2 reports: an operating
of activity & produce 2 reports: an operating
statement & an income statement.
statement & an income statement.
►
Figures 11.6 and 11.7 shows the input screen used to
Figures 11.6 and 11.7 shows the input screen used to
enter the scenario data elements for the prior
enter the scenario data elements for the prior
quarter & next quarter, respectively.
Figure 11.6 Model Input Screen
Figure 11.6 Model Input Screen
for Entering Scenario Data for
for Entering Scenario Data for
Figure 11.7 Model Input Screen
Figure 11.7 Model Input Screen
for Entering Scenario Data for
for Entering Scenario Data for
Model Output
Model Output
►
The next quarter’s activity (Quarter 1) is
The next quarter’s activity (Quarter 1) is
simulated, & the after-tax profit is displayed on
simulated, & the after-tax profit is displayed on
the screen.
the screen.
►
The executives then study the figure & decide on
The executives then study the figure & decide on
the set of decisions to be used in Quarter 2.
the set of decisions to be used in Quarter 2.
These decisions are entered & the simulation is
These decisions are entered & the simulation is
repeated.
repeated.
►
This process continues until all four quarters
This process continues until all four quarters
have been simulated. At this point the screen
have been simulated. At this point the screen
has the appearance shown in Figure 11.8.
has the appearance shown in Figure 11.8.
►
The operating statement in Figure 11.9 & the
The operating statement in Figure 11.9 & the
income statement in Figure 11.10 are displayed
income statement in Figure 11.10 are displayed
Figure 11.8 Summary Output
Figure 11.8 Summary Output
Figure 11.9 Operating
Figure 11.9 Operating
Statement Shows Nonmonetary
Statement Shows Nonmonetary
Figure 11.10 Income Statement
Figure 11.10 Income Statement
Modeling Advantages &
Modeling Advantages &
Disadvantages
Disadvantages
►
Advantages:
Advantages:
The modeling process is a
The modeling process is a
learning experience.
learning experience.
The speed of the simulation process enables the
The speed of the simulation process enables the
consideration of a larger number of alternatives.
consideration of a larger number of alternatives.
Models provide a
Models provide a
predictive power
predictive power
- a look into the future -
- a look into the future -
that no other information-producing method offers.
that no other information-producing method offers.
Models are
Models are
less expensive
less expensive
than the trial-and-error method.
than the trial-and-error method.
►
Disadvantages:
Disadvantages:
The difficulty of modeling a business system
The
difficulty of modeling a business system
will produce a
will produce a
model that does not capture all of the influences on the
model that does not capture all of the influences on the
entity.
entity.
A high degree of mathematical skill
A
high degree of mathematical skill
is required to develop
is required to develop
Mathematical Modeling Using
Mathematical Modeling Using
Electronic Spreadsheets
Electronic Spreadsheets
►
The technological breakthrough that enabled problem
The technological breakthrough that enabled problem
solvers to develop their own math models was the electronic
solvers to develop their own math models was the electronic
spreadsheet.
spreadsheet.
►
Static model
Static model
: Figure 11.11 shows an operating budget in
: Figure 11.11 shows an operating budget in
column form. The columns are for: the budgeted expenses,
column form. The columns are for: the budgeted expenses,
actual expenses, & variance, while rows are used for the
actual expenses, & variance, while rows are used for the
various expense items.
various expense items.
►
A spreadsheet is especially well-suited for use as a
A spreadsheet is especially well-suited for use as a
dynamic
dynamic
model
model
. The columns are excellent for the time periods, as
. The columns are excellent for the time periods, as
illustrated in Figure 11.12.
illustrated in Figure 11.12.
►
A spreadsheet also lends itself to playing the “what-if”
A spreadsheet also lends itself to playing the “what-if”
game, where the problem solver manipulates 1 or more
game, where the problem solver manipulates 1 or more
Figure 11.11 Spreadsheet Rows
Figure 11.11 Spreadsheet Rows
& Columns Provide Format for
& Columns Provide Format for
Figure 11.12 Spreadsheet
Figure 11.12 Spreadsheet
Columns are Excellent for Time
Columns are Excellent for Time
Spreadsheet Model Interface
Spreadsheet Model Interface
►
When using a spreadsheet as a mathematical model,
When using a spreadsheet as a mathematical model,
the user can enter data or make changes directly to
the user can enter data or make changes directly to
the spreadsheet cells, or by using a GUI
the spreadsheet cells, or by using a GUI
►
The pricing model described earlier in Figures 11.6-
The pricing model described earlier in Figures
11.6-11.10 could have been developed using a
11.10 could have been developed using a
spreadsheet, and had the graphical user interface
spreadsheet, and had the graphical user interface
added
added
►
The interface could be created using a programming
The interface could be created using a programming
language such as Visual Basic and would likely require
language such as Visual Basic and would likely require
an information specialist to develop
an information specialist to develop
►
A development approach would be for the user to
A development approach would be for the user to
develop the spreadsheet and then have the interface
develop the spreadsheet and then have the interface
Artificial Intelligence
Artificial Intelligence
►
Artificial intelligence (AI)
Artificial intelligence (AI)
is the activity of
is the activity of
providing such machines as computers with the
providing such machines as computers with the
ability to display behavior that would be regarded as
ability to display behavior that would be regarded as
intelligent if it were observed in humans.
intelligent if it were observed in humans.
►
AI is being applied in business in
AI is being applied in business in
knowledge-
knowledge-based systems
based systems
, which use human knowledge to
, which use human knowledge to
solve problems.
solve problems.
►
The most popular type of knowledge-based system
The most popular type of knowledge-based system
are
are
expert systems
expert systems
, which are computer programs
, which are computer programs
that try to represent the knowledge of human
that try to represent the knowledge of human
experts in the form of heuristics.
experts in the form of heuristics.
►
These heuristics allow an expert system to consult
These heuristics allow an expert system to consult
on how to solve a problem: called a consultation
on how to solve a problem: called a consultation
-
-
the user consults the expert system for advice.
Areas of AI
Areas of AI
►
Expert system
Expert system
is a computer program that
is a computer program that
attempts to represent the knowledge of
attempts to represent the knowledge of
human experts in the form of heuristics.
human experts in the form of heuristics.
►
Heuristic
Heuristic
is a rule of thumb or a rule of
is a rule of thumb or a rule of
good guessing.
good guessing.
►
Consultation
Consultation
is the act of using an expert
is the act of using an expert
system.
system.
►
Knowledge engineer
Knowledge engineer
has special expertise
has special expertise
in artificial intelligence; adept in obtaining
in artificial intelligence; adept in obtaining
Areas of AI (Cont’d)
Areas of AI (Cont’d)
►
Neural networks
Neural networks
mimic the
mimic the
physiology of the human brain.
physiology of the human brain.
►
Genetic algorithms
Genetic algorithms
apply the
apply the
“survival of the fittest” process to
“survival of the fittest” process to
enable problem solvers to produce
enable problem solvers to produce
increasingly better problem solutions.
increasingly better problem solutions.
►
Intelligent agents
Intelligent agents
are used to
are used to
perform repetitive computer-related
perform repetitive computer-related
tasks; i.e. data mining.
Expert System Configuration
Expert System Configuration
►
User interface
User interface
enables the manager to
enables the manager to
enter instructions & information into the
enter instructions & information into the
expert system & to receive information from
expert system & to receive information from
it.
it.
►
Knowledge base
Knowledge base
contains both facts that
contains both facts that
describe the problem area & knowledge
describe the problem area & knowledge
representation techniques that describe how
representation techniques that describe how
the facts fit together in a logical manner.
the facts fit together in a logical manner.
►
Problem domain
Problem domain
is used to describe the
is used to describe the
Expert System Configuration
Expert System Configuration
(Cont’d)
(Cont’d)
►
Rule
Rule
specifies what to do in a given
specifies what to do in a given
situation & consists of two parts:
situation & consists of two parts:
A c
A c
ondition
ondition
that may or may not be true, and
that may or may not be true, and
An
An
action
action
to be taken when the condition is true.
to be taken when the condition is true.
►
Inference engine is the portion of the expert
Inference engine is the portion of the expert
system that performs reasoning by using the
system that performs reasoning by using the
contents of the knowledge base in a
contents of the knowledge base in a
particular sequence.
particular sequence.
►
Goal variable
Goal variable
is assigning a value to the
is assigning a value to the
Expert System Configuration
Expert System Configuration
(Cont’d)
(Cont’d)
►
Expert system shell
Expert system shell
is a ready-made
is a ready-made
processor that can be tailored to a specific
processor that can be tailored to a specific
problem domain through the addition of the
problem domain through the addition of the
appropriate knowledge base.
appropriate knowledge base.
►
Case-based reasoning
Case-based reasoning
(
(
CBR
CBR
) uses
) uses
historical data as the basis for identifying
historical data as the basis for identifying
problems & recommending solutions.
problems & recommending solutions.
►
Decision tree
Decision tree
is a network-like structure
is a network-like structure
that enables the user to progress from the
that enables the user to progress from the
root through the network of branches by
root through the network of branches by
Figure 11.13 Expert System
Figure 11.13 Expert System
Group Decision Support
Group Decision Support
System
System
►
Group decision support system
Group decision support system
(
(
GDSS
GDSS
) is “a
) is “a
computer-based system that supports groups of
computer-based system that supports groups of
people engaged in a common task (or goal) & that
people engaged in a common task (or goal) & that
provides an interface to a shared environment”.
provides an interface to a shared environment”.
►
Aliases
Aliases
group support system
group support system
(
(
GSS
GSS
),
),
computer-
computer-supported cooperative work
supported cooperative work
(
(
CSCW
CSCW
),
),
computerized collaborative work
computerized collaborative work
support
support
, &
, &
electronic meeting system
electronic meeting system
(
(
EMS
EMS
).
).
►
Groupware
Groupware
the software used in these settings.
the software used in these settings.
►
Improved communications make possible improved
Improved communications make possible improved
GDSS Environmental Settings
GDSS Environmental Settings
►
Synchronous exchange
Synchronous exchange
when members meet at
when members meet at
the same time.
the same time.
►
Asynchronous exchange
Asynchronous exchange
when members meet at
when members meet at
different times.
different times.
►
Decision room
Decision room
is the setting for small groups of
is the setting for small groups of
people meeting face-to-face.
people meeting face-to-face.
►
Facilitator
Facilitator
is the person whose chief task is to
is the person whose chief task is to
keep the discussion on track.
keep the discussion on track.
►
Parallel communication
Parallel communication
is when all participants
is when all participants
enter comments at the same time,&
enter comments at the same time,&
►
Anonymity
Anonymity
is
is
when nobody is able to tell who
when nobody is able to tell who
entered a particular comment; participants say
entered a particular comment; participants say
what they REALLY think without fear
Figure 11.14 Group Size &
Figure 11.14 Group Size &
Location Determine DSS
Location Determine DSS
GDSS Environmental Settings
GDSS Environmental Settings
(Cont’d)
(Cont’d)
►
Local area decision network
Local area decision network
when it is impossible
when it is impossible
for small groups of people to meet face-to-face, the
for small groups of people to meet face-to-face, the
members can interact by means of a local area
members can interact by means of a local area
network, or LAN.
network, or LAN.
►
Legislative session
Legislative session
when the group is too large for a
when the group is too large for a
decision room.
decision room.
Imposes certain constraints on communications such as equal Imposes certain constraints on communications such as equal
participation by each member is removed or less time is
participation by each member is removed or less time is
available.
available.
►
Computer-mediated conference
Computer-mediated conference
several virtual
several virtual
office applications permit communication between
office applications permit communication between
large groups with geographically dispersed members.
large groups with geographically dispersed members.
Teleconferencing applications Teleconferencing applications include computer conferencing, include computer conferencing,
audio conferencing, & videoconferencing.
