TRANSACTION PROCESSING SYSTEM

“TPS are computerized systems that perform and record the daily routine transactions necessary to conduct the business”

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

COMPONENTS OF TPS

1. INPUT

2. PROCESSING

3. STORAGE

4. OUTPUT

TPS CYCLE

Transaction input

Input validation

Transaction document

Master file

Update master file

Process transaction

Invalid inputcorrectionTransaction

and control logControl log

Valid input

METHODS OF PROCESSING TRANSACTIONS

1. On-line entry with immediate processing

2. On-line entry with subsequent processing

3. Batch processing

CONTROL FOR TRANSACTION PROCESSING

• Audit Trail

• Ensuring Processing Of All Transactions

Office Automation System

• Virtual office

Office Automation Applications• Word processing

• Desktop publishing

• Videotex

• document imaging

• Email

• Voice mail

• Audio conferencing

• Video conferencing

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-stucturedand unstructured decision making

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

COMPONENTS OF DSS

• DATABASE

• MODEL BASE: optimization models, forecasting models, sensitivity analysis models

• DSS SOFTWARE

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

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.

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

Techniques of group decision making

• Brainstorming

• Nominal group technique

• Delphi technique

• Consensus mapping

Improved group discussion with GDSS

1. Idea generation

2. Enhanced participation

3. Improved idea evaluation

4. Preservation of organizational memory

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.

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

Components of EIS

• Executive

• Database: Data warehouse and Data mart

• EIS software:

– personal productivity software,

– Prewritten EIS software

– Customized EIS software

• Output

Developing EIS

1. Defining EIS requirements

2. Designing EIS database

3. Developing/ procuring EIS software

4. Designing EIS output

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

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

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

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.

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.

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

Components of an expert system

• User interface

• Knowledge base

• Inference engine:

– forward reasoning

– Backward reasoning

• Development engine

Developing an expert system

• Identification of problem

• Creation of development team

• Specification of rule set

• Development of prototype

• Testing and refining prototype