What is the difference between dss and eis

Design - several alternative solutions are developed. Choice - alternatives are compared to one another during the A choice stage.

Implementation - solution is implemented and tracked, in order to be improved upon. Each of the steps may require backing up to a preceding one, in order to redefine the problem or select a better solution. DSSs have several features to offer in the general information system environment of an organization.

Specifically, DSS can:. Support decision making in ill-structured situations when problems do not lend themselves to full computerization. Help to rapidly obtain quantitative results needed to reach a decision. Operate in the ad hoc mode to suit the current needs of the user. Give managers the opportunity to gain a better understanding of their business. Limitations of using spreadsheets as DSS models include:. They are limited in their data-handling capabilities and thus cannot work with large databases.

They do not allow for construction of more complex models. Modifications to spreadsheets are difficulty to keep updated when numerous people use them.

The three principal DSS subsystems and their principal capabilities are:. The Data Management Subsystem [Figure Data management subsystem of a DSS supplies data to which the models can be applied. It relies, in general, on a variety of internal and external databases. The power of a DSSs derives from their ability to provide easy access to data. The database extract procedures used by DSS is generally specified by a specialist, such as a database administrator, rather than by an end user.

The specialist needs to pay particular attention to data consistency across multiple decision support systems that extract data from the corporate databases. Data warehouses are used by many leading companies to support organizational DSS. Commercial data warehouses for decision support are emerging.

The Model Management Subsystem [Figure Model management subsystems maintain the libraries of models. A particular advantage of DSS is the decision maker's ability to use a model to explore the influence of various factors on outcomes a process known as sensitivity.

Two forms of such analysis are the what-if analysis and goal-seeking. The Dialog Management Subsystem [Figure Dialog management model supports the user in applying models to data. The notable feature is support of multiple forms of input and output. By combining various input and output capabilities of a DSS, users can engage in the individually selected dialogs that best support their decision-making styles.

The principal classes of DSS are those that provide:. These systems can provide user-friendly ad hoc access to the database. This capability is equivalent to what is offered by most DBMSs through a query language. However, such systems A open-up a database. These systems help analyze historical and current data, either on demand ad hoc or periodically. Data analysis systems are frequently oriented toward the consolidation aggregation of data, such as summarizing the performance of a firm's subunits and presenting the summaries in graphs.

Only very simple models are employed in data analysis systems. These systems generally assist in developing product plans, including market segment forecasts, sales forecasts, and analyses of competitive actions.

Their operation is based on access to a variety of internal and external marketing and product databases, including series of historical data. The systems in this category include only the simpler of the variety of marketing models, which show how existing trends in the marketplace will extend in the future if similar conditions prevail.

These models show the dependence between a controllable variable and an outcome. These are frequently simulation models which yield probabilistic results. Examples include representational models and risk analysis models. Optimization models are developed by management scientists to determine optimal allocation of resources or best possible schedules.

Systems with suggestion models suggest solutions within narrow domains of knowledge and sometimes combine a DSS with an expert system. It collects stores and distributes data as information, which helps to manage the organization properly. It also allows taking summarized reports. These reports help the management to monitor the organization, understand the current performance status and to take future business decisions.

It is designed to support the information needs for executive managers. Information is usually external, unstructured and uncertain.

Furthermore, this information is intelligence-based. Some examples of intelligent information are databases , patent records, financial reports, market reports, technical reports from consultants, government policies and confidential information about competitors. EIS provides multiple advantages. It is easier for upper-level executives to use and take decisions. It helps to monitor the company performance and examine the critical success factors.

It also allows analyzing trends and determines the competitiveness in the market. Moreover, it improves flexibility, strategic control, improves communication and provides time management. An Executive information system EIS , also known as an Executive support system ESS , is a type of management support system that facilitates and supports senior executive information and decision-making needs.

It provides easy access to internal and external information relevant to organizational goals. It does so by providing easy access to important data needed in an organization to achieve strategic goals.

An EIS usually has graphical displays on a user-friendly interface. Roderick Mason. Yet No Comments.