Types of Information System

1. TRANSACTION PROCESSING SYSTEM
“TPS are computerized systems that perform and record the
daily routine transactions necessary to conduct the business”

2. FEATURES
1. Organizations are required to process transactions in a way
that is required by its external stakeholders.
2. TPS are major producers of information for other types of
system
3. TPS handles details of data and these data show the results
of various activities usually on historical basis
4. Many TPS span the boundary of the organization and
connect the elements of its environment
5. TPS serve the need of operational level of the organization.
6. TPS are relevant to all the four functional areas of a business
organization

3. COMPONENTS OF TPS
1. INPUT
2. PROCESSING
3. STORAGE
4. OUTPUT

4. TPS CYCLE
Transaction
input
Input
validation
Transaction
document
Update
master file
Master
file
Process
transaction
correction Invalid input
Transaction
and control log
Control log
Valid
input

5. METHODS OF PROCESSING
TRANSACTIONS
1. On-line entry with immediate processing
2. On-line entry with subsequent processing
3. Batch processing

6. CONTROL FOR TRANSACTION
PROCESSING
• Audit Trail
• Ensuring Processing Of All Transactions

7. Office Automation System
• Virtual office
Office Automation Applications
• Word processing
• Desktop publishing
• Videotex
• document imaging
• Email
• Voice mail
• Audio conferencing
• Video conferencing

8. DECISION SUPPORT SYSTEM
• A decision support system can be defined simply as a system
that provides information for making semi-structured and
unstructured decisions.
• DSS represents a different approach to IS support for semi-structured
and unstructured decisions. They support a
variety of unstructured decision processes.
• DSS is a computer system at the management level of an
organization that combines data , sophisticated analytical
tools, and user friendly software to support semi-stuctured
and unstructured decision making

9. TYPES OF DSS
1. FILE DRAWER SYSTEMS
2. DATA ANALYSIS SYSTEM
3. ANALYSIS INFORMATION SYSTEM
4. ACCOUNTING MODELS
5. REPRESENTATIONAL MODELS
6. OPTIMISATION MODELS
7. SUGGESTION MODELS

10. COMPONENTS OF DSS
• DATABASE
• MODEL BASE: optimization models,
forecasting models, sensitivity analysis models
• DSS SOFTWARE

11. GROUP DECISION SUPPORT SYSTEM
• A GDSS is an interactive computer based system to facilitate
the solution of unstructured problems by a set of decision
makers working together as a group

12. Features of GDSS
• High level of interaction among decision makers who
work collectively on a problem
• Criticism free idea generation
• Priorities are set and decisions are made which
require finding ways to encompass the thinking of all
the members in making these decisions
• Access to relevant internal and external information
• Information about the problem on which a group is
working is stored so that those who fail to attend
meeting can work on the problem.

13. Components of GDSS
• Decision makers
• Database and model base
• Groupware: it can be classified into four categories:
1. Brainstorming
2. Alternative ranking and rating
3. Consensus building software
4. Group authoring and outlining software

14. Techniques of group decision making
• Brainstorming
• Nominal group technique
• Delphi technique
• Consensus mapping

15. Improved group discussion with GDSS
1. Idea generation
2. Enhanced participation
3. Improved idea evaluation
4. Preservation of organizational memory

16. EXECUTIVE INFORMATION SYSTEM
• EIS is a an information system at the strategic
level of an organization designed to address
unstructured decision making through
advanced graphics and communications.

17. features
• EIS are relevant to top management
• EIS cuts across the functional areas
• EIS combines both internal and external
information
• Information generated from EIS is in the form
of reports and graphics
• Executives are helped by EIS coaches and
chauffeurs

18. Components of EIS
• Executive
• Database: Data warehouse and Data mart
• EIS software:
– personal productivity software,
– Prewritten EIS software
– Customized EIS software
• Output

19. Developing EIS
1. Defining EIS requirements
2. Designing EIS database
3. Developing/ procuring EIS software
4. Designing EIS output

20. Critical success factors for EIS
• A committed and informed executive sponsor
• An operating sponsor
• Appropriate information service
• Appropriate information technology
• Data management
• Clear link to business objectives
• Management of organization resistance
• Management of the spread and the evolution of
the system

21. Artificial intelligence and expert
systems
• AI is the effort to develop computer-based
systems that can behave like humans, with the
ability to learn languages, accomplish physical
tasks, use a perceptual apparatus and emulate
human experience and decision making

22. features
• AI is a man made , unlike natural intelligence
which emerges from human brain
• It is a king of computer program that enables
the computer to think or behave the way
programmer wants

23. Business applications of AI
1. To capture and preserve expertise of experts who might not be
available in the organization due to their resignation, retirement
or death
2. To enhance the distribution of knowledge throughout the
organization
3. To create a mechanism that is not subject to human feelings like
fatigue and worry
4. To provide solutions to the problems that are too complex for
human beings like getting permutations, and combinations etc
5. To maintain or enhance strategic position of an organization in the
market place by devising suitable systems through AI that helps in
reducing cost of production or providing better product mix or
both.

24. EXPERT SYSTEMS
• It is a knowledge – intensive program that
solves a problem by capturing the expertise of
a human in limited domains of knowledge and
experience.

25. features
1. It performs some o the problem solving work of human by
going through logical reasoning which involves drawing
inferences about a problem step by step
2. It represents knowledge in the form of rules opr frames
3. It considers multiple hypothesis simultaneously in drawing
inferences
4. It is not generalized expert or problem solver

26. Components of an expert system
• User interface
• Knowledge base
• Inference engine:
– forward reasoning
– Backward reasoning
• Development engine

27. Developing an expert system
• Identification of problem
• Creation of development team
• Specification of rule set
• Development of prototype
• Testing and refining prototype