Introduction to Information Technology
Turban, Rainer and Potter
John Wiley & Sons, Inc.
Copyright 2005
Chapter10
Managerial Support Systems
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10.1 Managers and Decision Making
Management is a process by which
organizational goals are achieved through the
use of resource (people, money, energy,
materials, space, time) . These resources are
considered to be inputs; the attainment of the
goals is viewed as the output of the process.
The ratio between inputs and outputs is an
indication of the organization’s productivity.
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The Manager’s Job
Manager have three basic role (Mintzberg
1973) :
Interpersonal roles:: figurehead, leader, liaison
Informational roles: monitor, disseminator,
spokesperson
Decisional roles: entrepreneur, disturbance
handler, resource allocator, negotiator.
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Decision Making
A decision refers to a choice made between
two alternatives.
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The process and phases in decision
making
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Model ( in decision making )
Model is a simplified representation, or abstraction of reality
The benefits of modeling in decision making are:
The cost of virtual experimentation is much lower than the
cost of experimentation conducted with a real system.
Models allow for the simulated compression of time. Years of
operation can be simulated in seconds of computer time
Manipulating the model ( by changing variable ) is much
easier than manipulating the real system.
Modeling allows a manager to better deal with the
uncertainly by introducing many “ what- ifs” and calculating
the risks involved in specific actions.
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Why Manager Need IT Support
A key to good decision making is to explore and
compare many relevant alternatives. The more
alternatives that exist, the more computer-assisted
search and comparison are needed.
Typically, decisions must be made under time
pressure. Frequently it is not possible to manually
process the needed information fast enough to be
effective.
It is usually necessary to conduct a sophisticated
analysis in order to make a good decision. Such
analysis requires the use of modeling.
Decision makers can be in different locations and so
is the information. Bringing them all together quickly
and inexpensively may be a difficult task.
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Management Support Systems (MSSs)
Major IT technologies designed to support
managers; decision support systems,
executive support systems, groupware
technologies and intelligent system.
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Decision Support Framework.
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10.2 Decision Support Systems)DSS)
A computer-based information system that
combines models and data in an attempt to
solve semi-structured and some unstructured
problems with extensive user involvement.
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Characteristics and Capabilities of DSSs
Sensitivity analysis. The study of the impact
that changes in one (or more) parts of a
model have on other parts.
What-if analysis. The study of the impact of a
change in the assumptions (input data) on the
proposed solution.
Goal-seeking analysis. Study that attempts to
find the value of the inputs necessary to
achieve a desired level of output.
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Structure and Components of DSS.
Data management subsystem
Model management subsystem
User interface
Users
Knowledge- based subsystems
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The DSS and its Computing
Environment
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Emerging Types of DSS
Frontline decision making. The process by
which companies automate decision process
and push them down into the organization
and sometimes out to partners.
Real- Time Decision Support. The systems
that supports business decisions that must be
made at the right time and frequently under
time pressure.
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Group Decision Support Systems
Virtual group. A group whose members are in
different locations.
Group decision support system (GDSS). An
interactive computer-based system that
supports the process of finding solutions by a
group of decision makers.
Decision room. A face- to-face setting for a
group DSS, in which terminals are available
to the participants.
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10.3 Enterprise and Executive Decision Support
Systems
Organizational decision support system
(ODSS): A DSS that focuses on an
organizational task or activity involving a
sequence of operations and decision makers.
Executive information system (EIS): A
computer-based technology designed in
response to the specific needs of executive
support system (ESS).
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The Capabilities of an ESS
Capability
Description
Drill- down
Ability to go to details, at several levels; can be done by a series of
menus or by direct queries (Using intelligent and natural language
processing )
Critical success
factors (CSF)
The factors most critical for the success of business. These can be
organizational, industry departmental, etc.
Key performance
indicators (KPIs)
The specific measures of CSFs. Example are provided in online
file W10.6.
Status access
The latest data available on KPI or some other metric, ideally in
real time.
Trend analysis
Short, medium, and long–term trend of KPIs or metrics, which are
projected using forecasting methods.
Ad-hoc analysis
Analysis made any time. Upon demand and with any desired
factors and relationships.
Exception reporting
Report that highlight deviations larger than certain thresholds.
Reports may include only deviations. Based on the concept of
management by exception.
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10.4 Intelligent Support Systems: The
Basics
Intelligent support systems is a term that describes the
various commercial applications of artificial
intelligence (AI).


Artificial intelligence (AI). A subfield of computer
science concerned with studying the thought
processes of humans and representing those
processes via machines.
Turning test. A test for artificial intelligence, in which a
human interviewer, conversing with both an unseen
human being and an unseen computer, cannot
determine which is which; named for English
mathematician Alan Turing.
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The Capabilities of an ESS
Capability
Natural Intelligence
Artificial Intelligence
Preservation of
knowledge
Perishable from an
organizational point of view
Permanent
Duplication and
dissemination of
knowledge
Difficult, expensive, takes times
Easy, fast and inexpensive once
knowledge is in computer
Total cost of
knowledge
Can be erratic and inconsistent,
incomplete at times
Consistent and thorough
Documentability of
process and
knowledge
Difficult, expensive
Fairly easy, inexpensive
Creativity
Can be very high
Low, uninspired
Use of sensory
experiences
Direct and rich in possibilities
Must be interpreted first; Limited
Recognizing patterns
and relationship
Fast, easy to explain
Machine learning still not as good as
people in most cases, but in some cases
can do better than people
Reasoning
Making use of wide context of
experiences
Good only in narrow, focused and stable
domains
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The Intelligent Systems
Name
Short Description
Expert System (ES)
Computerized advisory systems usually based on rules
Natural Language Processing
(NLP)
Enables computers to recognize and even understand human languages
Speech understanding
Enables computers to recognized words and understand short voice sentences.
Robotic and sensory systems
Programmable combination of mechanical and computer program. Recognize their
environments via sensors.
Computer vision and scene
recognition
Enable computers to interpret the content of pictures captured by cameras.
Machine learning
Enables computer to interpret the content of pictures captured by sensors ( see
next three items)
Handwriting recognition
Enables computers to recognized characters (letter, digits) written by hand.
Neural computing (networks)
Using massive parallel processing, able to reorganize patterns in large amount of
data.
Fuzzy logic
Enables computers to reason with partial information
Intelligent agents
Software programs that perform tasks for a human or machine master
Semantic web
An intelligent software program that “ understands” content of web pages.
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10.5 Expert Systems (ES)
A computer system that attempts to mimic
human experts by applying reasoning
methodologies or knowledge in a specific
domain.
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Expertise and knowledge
Expertise is the extensive, task-specific knowledge acquired from
training, reading and experience. The transfer of expertise from
an expert to computer and then to the user involves four
activities:
Knowledge acquisition: Knowledge is from experts or from
documented sources.
Knowledge representation: Acquired knowledge is organized
as rules or frames (objective-oriented) and stored
electronically in a knowledge base.
Knowledge inferencing: Given the necessary expertise
stored in the knowledge base, the computer is programmed
so that it can make inferences. The reasoning function is
performed in a component called the inference engine,
which is the brain of ES.
Knowledge transfer: The inferenced expertise is transferred
to the user in the form of a recommendation.
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Benefits of Expert Systems
Benefit
Description
Increased output and
productivity
ESs can configure for each custom order. Increasing production capabilities
Increased quality
ESs can provide consistent advise and reduce error rates.
Capture and dissemination of
scarce expertise
Expertise from anywhere in the world can be obtained and used.
Operation in hazardous
environments
Sensors can collect information that an ES interprets, enabling human workers to avoid
hot, humid, or toxic environments.
Accessibility to knowledge and
help desks
ESs can increase the productivity of help – desk employee, or even automate this
function.
Reliability
ESs do not become tired or bored, call in sick or go on strike. They consistently pay
attention to details.
Ability to work with incomplete or
uncertain information
Even with answer of ‘ don’t know ‘ an ES can produce an answer, though it may not be a
definite one.
Provision of training
The explanation facility of an ES can serve as a teaching device and knowledge base for
novices.
Enhancement of decisionmaking and problem-solving
capabilities
ESs allow the integration of expert judgment into analysis (e.g., diagnosis of machine
malfunction and even medical diagnosis).
Decreased decision-making time
ESs usually can make faster decision than humans working alone.
Reduce downtime
ESs can quickly diagnose faster decisions than humans and prescribe repairs.
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The Components of Expert Systems
Knowledge base
Inference engine
User interface
Blackboard
Explanation subsystem
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Structure and Process of an ES
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Ten Generic Categories of Expert Systems
Category
Problem Addressed
Interpretation
Inferring situation description from observations.
Prediction
Inferring likely consequence of given situation.
Diagnosis
Inferring system malfunctions from observations.
Design
Configuring objects under constraints.
Planning
Developing plans to achieve goals.
Monitoring
Comparing observations to plans, flagging exceptions.
Debugging
Prescribing remedies for malfunction.
Repair
Executing a plan to administer a prescribed remedy.
Instruction
Diagnosing , debugging, and correcting student performance
Control
Interpreting, predicting, repairing and monitoring system behavior
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10.6 Other Intelligent Systems
Natural language processing (NLP): Communicatng
with a computer in English or whatever language you
may speak.
Natural language understanding/speech (voice)
recognition: The ability of a computer to comprehend
instructions given in ordinary language, via the
keyboard or by voice.
Natural language generation/voice synthesis.
Technology that enables computers to produce
ordinary language, by “voice” or on the screen, so
that people can understand computers more easily.
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Other Intelligent Systems cont…
Artificial Neural Networks (ANNs): Computer
technology, modeled after concepts from biological
neural systems, that attempts to simulate massively
parallel processing of interconnected elements in a
network architecture.
Neural computing:. The application of artificial neural
network technology.
Pattern recognition:. The ability of a neural network to
establish patterns and characteristics in situation
where the logic or rules are not known, by analyzing
large quantities of data.
Fuzzy logic: . Computer reasoning that deal with
uncertainties by simulating the process of human
reasoning.
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programs or from the use of the information herein.
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