A decision support system is a computer application that helps users analyze problems
and make business decisions more confidently. It uses data routinely collected in organizations
and special analysis tools to provide information support to complex decisions. For example, a
firm’s sales department may be interested in analyzing various sales decision options. The
decision support application might gather data, present the data graphically and help in
evaluating various options. It may use past sales figures, project sales based on sales
assumptions for each alternative considered and display information graphically. It may also use
artificial intelligence to enhance its decision support capability.
Decision Support System assists managers in making unstructured decisions. The system
enables them to interact with the database, model base and other software. It enables the users to
generate the information they need rather than depend on some reports produced according to
some anticipated information needs. DSS is more suited to handling unique and non-routine
decision problems. In many situations the problem itself may not be easily identified. Similarly,
identifying alternatives, identifying outcomes of each alternative considered, evaluation of
alternatives etc. pose problems to the decision maker. Each problem might require a different
approach to problem definition, analysis and resolution. Not only that it is difficult to solve such
problems, it is also possible that the decision process and solution vary with the decision maker.
DSS is designed to support managerial decision-making, usually, at middle and top levels
of management. Decisions made at the top level are mostly futuristic and non-repetitive in
nature. Such decision situations are highly uncertain and even specification of information
requirements for decisions are difficult. They are classified as non-programmable or
unstructured decision situations. The impact of such decisions will be seen throughout the
organization and cost of a wrong decision is usually very high, for example a decision to sell off
a line of business. This is in sharp contrast to programmable or structured decisions where the
decision procedure can be well defined and every information requirement can be pre-specified.
Most of the decisions taken at lower levels of management fall into this category. For example,
a decision to replenish stock of an inventory item is a highly structured decision taken at the
operational level. DSS is intended to help managers making unstructured decisions. ‘the system
includes a database, various models (Mathematical models for optimization etc.) and an interface
for the manager (usually a terminal) to interact with the system. The manager takes data from
the database, selects appropriate model or models and analyses the data using these models to
know the probable results of various actions.
DSS is thus an interactive computer system with many user-friendly features aimed at
helping non-computer specialist managers in making plans and decisions on their own. With the
recent advances in computing technology, particularly the powerful microcomputer and
interactive devices, the uses of DSS is expanding rapidly as these managers find it easy to access
databases and model base for retrieving and analyzing data.
DSS contains a database, models and data manipulation tools to help decision makers. It
is useful where decisions are semi-structured or unstructured. The decision rule for a structured
decision can be pre-specified. Hence, it is possible to automate such problem solving.
Intelligence activities are targeted at discovering problems of organizations. The
information reporting system can handle most of these information requirements. In the design
phase, alternative solutions to the problem identified are generated. This stage requires more
focused information and more intelligence based systems like DSS and Knowledge based
systems. Choice phase involves selecting the right alternative. This requires thorough
evaluation of the consequences of all the alternatives under consideration in terms of risk and
return, and its impact on problem area.
Decision implementation is a critical phase. Managers are anxious about the results of
decision implementation right from day one of implementation. In this phase, managers call for
information on implementation of decision such as stage of implementation, time and cost
involved, implementation constraints, and impact of implementation.
DSS can support repetitive or non-repetitive decision-making. It provides capabilities
for repetitive decision-making by defining procedures and formats. For example, an insurance
agent may use a DSS package to help clients in choosing insurance schemes. With the
privatization of insurance in the country, innovative insurance products are being introduced. An
investor will find it difficult to properly identify an insurance product matching his or her
requirements. The agent can carry a laptop with a DSS for insurance products to his clients. The
DSS can be used by the sales agent to demonstrate to the clients the details of each scheme in
terms of risk covered, bonus, maturity value, premiums etc. and help the clients arrive at their
decisions to purchase insurance policy.
DSS can also help non-routine decision-making. In fact its utility is high when nonrepetitive
decisions are made. For solving a non-repetitive problem, the DSS provides data,
models and interface methods to the user to select and analyze data. For example, a marketing
manager might want to analyze the potential demand for new products that the company is
planning to introduce. The marketing manager can use a DSS to forecast the demand using
relevant data about the market obtained form some database service firms like Centre for
Monitoring Indian Economy. The analysis will provide new insights into the market behaviour
and product performance that will help the manager in introducing new products into the market.
and make business decisions more confidently. It uses data routinely collected in organizations
and special analysis tools to provide information support to complex decisions. For example, a
firm’s sales department may be interested in analyzing various sales decision options. The
decision support application might gather data, present the data graphically and help in
evaluating various options. It may use past sales figures, project sales based on sales
assumptions for each alternative considered and display information graphically. It may also use
artificial intelligence to enhance its decision support capability.
Decision Support System assists managers in making unstructured decisions. The system
enables them to interact with the database, model base and other software. It enables the users to
generate the information they need rather than depend on some reports produced according to
some anticipated information needs. DSS is more suited to handling unique and non-routine
decision problems. In many situations the problem itself may not be easily identified. Similarly,
identifying alternatives, identifying outcomes of each alternative considered, evaluation of
alternatives etc. pose problems to the decision maker. Each problem might require a different
approach to problem definition, analysis and resolution. Not only that it is difficult to solve such
problems, it is also possible that the decision process and solution vary with the decision maker.
DSS is designed to support managerial decision-making, usually, at middle and top levels
of management. Decisions made at the top level are mostly futuristic and non-repetitive in
nature. Such decision situations are highly uncertain and even specification of information
requirements for decisions are difficult. They are classified as non-programmable or
unstructured decision situations. The impact of such decisions will be seen throughout the
organization and cost of a wrong decision is usually very high, for example a decision to sell off
a line of business. This is in sharp contrast to programmable or structured decisions where the
decision procedure can be well defined and every information requirement can be pre-specified.
Most of the decisions taken at lower levels of management fall into this category. For example,
a decision to replenish stock of an inventory item is a highly structured decision taken at the
operational level. DSS is intended to help managers making unstructured decisions. ‘the system
includes a database, various models (Mathematical models for optimization etc.) and an interface
for the manager (usually a terminal) to interact with the system. The manager takes data from
the database, selects appropriate model or models and analyses the data using these models to
know the probable results of various actions.
DSS is thus an interactive computer system with many user-friendly features aimed at
helping non-computer specialist managers in making plans and decisions on their own. With the
recent advances in computing technology, particularly the powerful microcomputer and
interactive devices, the uses of DSS is expanding rapidly as these managers find it easy to access
databases and model base for retrieving and analyzing data.
DSS contains a database, models and data manipulation tools to help decision makers. It
is useful where decisions are semi-structured or unstructured. The decision rule for a structured
decision can be pre-specified. Hence, it is possible to automate such problem solving.
Intelligence activities are targeted at discovering problems of organizations. The
information reporting system can handle most of these information requirements. In the design
phase, alternative solutions to the problem identified are generated. This stage requires more
focused information and more intelligence based systems like DSS and Knowledge based
systems. Choice phase involves selecting the right alternative. This requires thorough
evaluation of the consequences of all the alternatives under consideration in terms of risk and
return, and its impact on problem area.
decision implementation right from day one of implementation. In this phase, managers call for
information on implementation of decision such as stage of implementation, time and cost
involved, implementation constraints, and impact of implementation.
DSS can support repetitive or non-repetitive decision-making. It provides capabilities
for repetitive decision-making by defining procedures and formats. For example, an insurance
agent may use a DSS package to help clients in choosing insurance schemes. With the
privatization of insurance in the country, innovative insurance products are being introduced. An
investor will find it difficult to properly identify an insurance product matching his or her
requirements. The agent can carry a laptop with a DSS for insurance products to his clients. The
DSS can be used by the sales agent to demonstrate to the clients the details of each scheme in
terms of risk covered, bonus, maturity value, premiums etc. and help the clients arrive at their
decisions to purchase insurance policy.
DSS can also help non-routine decision-making. In fact its utility is high when nonrepetitive
decisions are made. For solving a non-repetitive problem, the DSS provides data,
models and interface methods to the user to select and analyze data. For example, a marketing
manager might want to analyze the potential demand for new products that the company is
planning to introduce. The marketing manager can use a DSS to forecast the demand using
relevant data about the market obtained form some database service firms like Centre for
Monitoring Indian Economy. The analysis will provide new insights into the market behaviour
and product performance that will help the manager in introducing new products into the market.
No comments:
Post a Comment