Management Information System 3

Amity School of Business

Jitendra Tomar
Amity School of Business,
Amity University, UP

09650512300
0120 4392867
jtomar@amity.edu

MIS - Orator


• Organizations, Management & Information.
• Information Technology Infrastructure.
• Managing and Organizing Support Systems for the Firm.
• Building Information Systems in the Digital Firm.
• Managing Information Systems in the Digital Firm.

MIS - Curriculum


Managers & their Prolific need.
• Strategic Management
– Typically, a board of directors and an executive
committee of the CEO and top executives.
– Develop overall organizational goals, strategies, policies,
and objectives as part of a strategic planning process.
– Monitor strategic performance of the organization and its
overall direction in the political, economic, and
competitive business environment.
– Decisions made at this level are highly structured.

Managers & Support Systems


• Tactical Management
– Business professionals in self directed teams as well as
business unit managers.
– Develop short and medium range plans, schedule and
budgets.
– Specify the policies, procedures, and business objectives
for the subunits of their company and allocate resources.
– Monitor the performance of their organizational subunits,
including departments, divisions, process teams and
workgroups.
– Decisions made at this level are semi-structured.



• Operational Management
– The members of self directed teams or operating
managers.
– Develop short-range plans such as weekly production
schedules.
– Direct the use of resources and the performance of tasks
according to procedures and within budgets.
– Establish the schedules for the teams and other
workgroups of the organization.
– Decisions made at this level are highly unstructured.



Managers & their Information Systems.
• Employees at different levels in an organization must make
decisions that vary in scope and type.
• In pyramidal view of an organization we have seen the types
of information systems needed for an organization’s different
operational and managerial levels.
• The information systems used at different levels of
management are:
– Transaction Processing System (Shop-floor Level),
– Decision Support System (Tactical & Strategic Level),
– Executive Information System (Strategic Level), &
– Expert System (Tactical & Strategic Level).
• Also, with growing need for information at different levels,
these traditional correlations can become blurred.



DSS - Defined.
• Decision Support Systems are computer based information
systems that provide interactive information support to
managers and business professionals during the decision-
making process.

• Decision Support Systems use
– Analytical Models,
– Specialized databases,
– A decision makers own insight and judgments, and
– An interactive, computer-based modeling process,
to support the making of business decisions.

Decision Support Systems


DSS – Phases in Decision Making.
A Decision Making is a three phase process.
• Intelligence Phase
– Collect data from inside the organization.
– Collect data from outside the organization.
– Collect information on all possible ways to solve the
problem.
• Design Phase
– Organize the data in a definite structure.
– Produce reasonable, potential courses of action. It could
be more than one.
• Choice Phase
– Select the course of action.



DSS – Problem types.
Depending upon the amount of data and the availability of
data analysis methods, the problems can be classified as:
• Structured Problem
– Optimal solution can be reached in a single set of steps.
– Solution with same data will always yield the same answer.
– Sequence of steps followed is called algorithm.
– Categories of data is known as parameters.
– It is referred as programmable problem.
• Unstructured Problem
– There is no standard algorithm for optimal solution because
• Either there is no enough information
• So many potential factors are there that no algorithm can be
formulated.
– Closely related to uncertainty.



DSS – Components.
Depending upon the amount of data and the availability of
data analysis methods, the problems can be classified as:
• The Data Management Module
– It is a database or data warehouse.
– Retrieves and manipulates relevant data.
• Model Management Module.
– Maintains alphanumeric and graphical models, formulas
and algorithms.
– Best Model is selected for typical decision making
problems.
• The Dialog Module
– It allows the user to access the database.
– Select data for decision process.



DSS – Analytical Models.
Using DSS involved four types of analytical models:
• What-If Analytical Model
– Observing how changes to selected variables affect other
variables.
– E.g.: What if we cut advertising by 10% ? What would
happen to sales?

• Sensitivity Analytical Model
– Observing how repeated changes to a single variable
affect other variables.
– E.g.: Let’s cut advertising by $100 repeatedly so we can
see its relationship to sales.



DSS – Analytical Models.
Using DSS involved four types of analytical models:
• Goal-Seek Analytical Model
– Making repeated changes to select variables until a
chosen variable reaches a target value.
– E.g.: Let's try increases in advertising until sales reach $1
Million

• Optimization Analytical Model
– Finding an optimum value for selected variables, given
certain constraints.
– E.g.: What’s the best amount of advertising to have, given
our budget and choice of media?



EIS - Defined.
• In 1980’s, the rapid development of microcomputer
processing power, application software packages, and
telecommunications networks gave birth to the phenomenon
of end-user computing.

• End-users could now use their own computing resources to
support their job requirements instead of waiting for the
indirect support of centralized corporate information service
department.

• Gradually it became evident that most top corporate
executives, due to information overload, could not effectively
use either the generated reports of Information Systems or the
Analytical Modeling capabilities of DSS and hence the
concept of Executive Information System.

Executive Information System


EIS - Defined.
• EIS provides critical information from wide variety of internal
and external sources in easy-to-use displays to executives and
managers.
• It provides high ranking managers with the most essential
information.
• It is useful in parting down the information for executives who
always suffer from information overload.
• EIS do not contain analytical models.
• EIS consolidate and summarize internal and external data.
• It displays the data in a way so that exceptions are easily
spotted.

Executive Information System


Knowledge Management.
• Knowledge Base - A computer-accessible collection of
knowledge about a subject in a variety of forms, such as facts
and rules of inference, frames, and objectives.
• Knowledge Management – Managing the Knowledge Base
i.e. the process of organizing and sharing the diverse forms of
Business Knowledge and Knowledge Bases within an
organization. It includes project & enterprise libraries,
discussion DB, intranet DB, and similar knowledge bases.
• Knowledge Workers - People whose primary work activity
includes creating, using, and distributing information.
• Knowledge Engineers – The specialists who works with experts
to capture the knowledge they possess in order to develop a
knowledge base for expert systems and other knowledge-
based systems.
Knowledge Management


Knowledge Management & Research.
• There are two types of knowledge about a subject:
– Awareness: The explored and unexplored information about the
subject,
– Source: Where to locate the known and unknown facts about the
subject.
• Knowledge is accumulated through experience and kept at
places that are not readily available with the people:
– People’s Mind
– Discussion Transcript
– Paper Notes.
• Knowledge Management puts procedures & technologies to:
– Transfer individual knowledge into DBs or KBs.
– Recognize most relevant information / knowledge.
– Allow the people to share the information / knowledge.


Methods of Knowledge Representation.
• Case-Based Reasoning - Represent knowledge in the ES’s
knowledge base in the form of cases, i.e. examples of past
performance, occurrence, and experience
• Frame-Based Reasoning - Knowledge represented in the form
of a hierarchy or network of frames. A frame is a collection of
knowledge about an entity consisting of a complex package
of data values describing its attributes.
• Object-Based Reasoning - Knowledge represented as network
of objects. An object is a data element that includes both
data and the methods or processes that act on those data.
• Rule-Based Reasoning - Knowledge represented in the form of
rules and statements of fact. Rules are statements that
typically take the form of a premise and a conclusion such as:
If (Condition), Then (Conclusion).



Artificial Intelligence – What’s That?
• AI concept foresee machines having Human Intelligence.
• AI Technologies are used in variety of ways to improve
decision support provided to managers and business pros.
• AI-enabled applications are at work in information distribution
and retrieval, database mining, product design,
manufacturing, inspection, training, user support, surgical
planning, resource scheduling, and complex resource
management.
• For anyone who schedules, plans, allocate resources, design
products, use the Internet, develop the software, is an
investment professional, heads the IT, uses IT, or operates in
any other capacities and arenas, AI technologies already
may be in place and providing competitive advantage to
them.
Artificial Intelligence & Expert Systems


Artificial Intelligence – Early Encounters.
• In late 50’s and early 60’s, scientists tried to build a system that
could perform intelligent tasks.
• They tried to build a system that could mimic humans.
• These efforts failed because the programs needed for the task
would have to be unrealistically huge and very complex in
nature.
• They started with designing of programs to solve problems in
specific domain by utilizing expert knowledge and reasoning.
• The programs are referred as the Expert Systems, which are
based on the concept of Artificial Intelligence.



Artificial Intelligence – Matching the Human Intelligence.
Some of the attributes of Intelligent Human Behavior are given
below. AI is attempting to duplicate these capabilities in
computer-based systems.
• Think and Reason.
• Use reason to solve problems.
• Learn or understand from experience.
• Acquire and apply knowledge.
• Exhibit creativity and imagination.
• Deal with complex or perplexing situations.
• Respond quickly and successfully to new situations.
• Recognize the relative importance of elements in a situation.
• Handle ambiguous, incomplete, or erroneous information.



Artificial Intelligence – The Domains.
AI applications are grouped under three major areas:
Artificial
Intelligence

Cognitive Natural
Robotics
Science Interface
Applications
Applications Applications

•Learning Systems •Visual Perception •Natural Languages
•Fuzzy Logic •Tactility •Speech Recognition
•Genetic Algorithms •Dexterity •Multi-sensory
•Neural Networks •Locomotion Interfaces
•Intelligent Agents •Navigation •Virtual Reality



Artificial Intelligence – Commercial Applications.
Decision Support.
• AI is used to develop Intelligent work environment that will
help in capturing ‘why’ as well as ‘what’ of engineered design
and decision making.
• AI provides an Intelligent human-computer interface (HCI)
systems that can understand spoken language and gestures,
and facilitate problem solving by supporting organization
wide collaborations to solve particular problems.
• AI facilitates Situation Assessment and Resource Allocation
software for variety of uses that range from airlines & airports
to logistics centers.



Information Retrieval.
• AI-based Intra and Internet systems can be used to distill tidal
waves of information into simple presentations.
• AI based Natural Language Technology can be used to
retrieve any sort of online information, from text to pictures,
videos, maps, and audio clips, in response to a verbal query.
• AI helps in Database mining for purposes like marketing trend
analysis, financial forecasting, maintenance cost reduction,
and more.



Virtual Reality.
• AI provides X-ray like vision enabled by enhanced-reality
visualization. It enables Automated animation and haptic
interfaces that allow users to interact with virtual objects via
touch
• Brain surgeons to “see through” intervening tissue to operate,
monitor, and evaluate disease progression. Medical students
can “feel” what it’s like to suture severed aortas.
Robotics
• Machine Vision inspections systems for gauging, guiding,
identifying, and inspecting products and providing
competitive advantage in manufacturing are based on AI.
• AI leads to development of cutting-edge robotics systems
from micro robots and hands & legs to cognitive robotic and
trainable modular vision systems.


Expert System - Defined.
• An expert system is a knowledge-based information system
that uses its knowledge about a specific, complex application
area to act as an expert consultant to end users.
• Expert systems provide answers to questions in a very specific
problem area by making human like inferences about
knowledge contained in a specialized knowledge base.
• They also explain their reasoning process and conclusions to a
user.
• They provide decision support to the end users in form of an
advice from an expert consultant in a specific problem area.
• The components of Expert System include a knowledge base
and software modules that perform inferences on the
knowledge in the knowledge base and communicate
answers to a user’s questions.


Expert System - Benefits.
• An ES captures the expertise of an expert or group of experts
in a computer-based information system.
• It can out perform a single human expert in many ways.
– It is faster and more consistent.
– Can have knowledge of several experts.
– Does not get tired or distracted by over work or stress.
– They also help preserve and reproduce the knowledge of
experts. They allow an organization to preserve the
expertise of an expert before he/she leaves the
organization, which can later be shared by reproducing
the software and knowledge base of the expert system.



Expert System - Limitations.
• ESs can handle only narrow domains – Early attempts to
create general problem solvers failed miserably. Current ESs
performs well if the domain they handle is precisely defined.
• ESs do not possess common sense – With all the sophistication,
ESs can not recognize problems that require common sense.
The system will be able to solve only those problems it was
specifically programmed to solve.
• ESs have limited ability to learn – While neural network
technology made great strides in the area of machine
learning, the ability of computer-based programs to learn
remains limited. Knowledge Engineers must coach the systems
and provide continual feed back for the systems to learn. It
may take many years for scientists to produce an ES that can
quickly learn and apply self-learned knowledge.


Expert System – Suitability Criteria.
• Domain – The domain , or subject area, of the problem is
relatively small and limited to a well-defined problem area.
• Expertise – Solutions to the problem require the efforts of an
expert. That is, a body of knowledge, techniques, and
intuition is needed that only a few people possess.
• Complexity – Solution to a problem is a complex task that
requires logical inference processing, which would not be
handled as well by conventional information processing.
• Structure – The solution process must be able to cope with ill-
structured, uncertain, ambiguous, missing, and conflicting
data, and a problem situation that changes with the passage
of time.
• Availability – ES acts as an expert, who is articulate and
cooperative, and have the support of the management &
end users, is involved in the development of the proposed
system.


Expert System – In Action Globally.
• Telephone Network Maintenance – ES at Pacific Bell.
• Credit Evaluation – AmEx and FAST of American Express.
• Tax Planning – TaxAdvisor for Federal & State Tax laws.
• Insider Securities Trading – AMEX at American Stock
Exchange.
• Detection of common metals – ES at General Electric Corp.
• Mineral Exploration – PROSPECTOR
• Irrigation & Pest Management – EXNUT at U.S. Department of
Agriculture.
• Predicting failures of Diesel Engines – ES at Canadian Pacific
Railroad.
• Medical Diagnosis - ES named CADUCEUS, MYCIN and PUFF.
• Class Selection for Students – ES at California State University.

Management Information System 3

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