According to the user relationship classification, there are three different classes of the decision support system. These three classes include; passive, active and cooperative ones. It is important to note that they apply to the concept of decision support systems in relation to: a specific decision context, a specifically targeted preference, a knowledge base, and an engine for analyzing and displaying decision options.
A passive DSS only provides assistance in the process of decision-making. However, it does not give any explicit recommendations or alternative solutions to the problem in question.
The passive DSS only maintains the firm at its current position. The reason is that it does not suggest ways in which the firm can improve its activities or operations. The active DSS offers both the recommendations and alternative solutions in a decision-making process. Managers come up with some ways of alleviating problems in the organization keeping in mind its objectives.
They identify patterns and alternatives and incorporate them into the active DSS. The latter one, in turn, determines whether such problems and gaps exist in relation to the current position of the company.
The cooperative system improves and completes the suggestions of an advisor. It sends them back to the latter one for validation. If no decision is met, the process starts all again until it is made. Decision support systems can be used in any knowledge domain to make the decision-making process easy. Many sectors of economy are embracing this technology in order to improve their overall performance.
The following ones are some of the ways that the DSS has been applied in various fields. The DSS is applied in medicine to make a medical diagnosis. Such decision support systems are called clinical decision support systems CDSS. If used properly, CDSS has the potential to change a perspective in medical training and practice Power, Evolution of CDSS usually takes place in four stages.
The primitive step is standalone and does not support integration. The second one allows for integration with other medical systems.
The third step is standard-based; and the final and fourth stages are model based. Business managers can take building the DSS according to their needs and objectives in order to achieve optimal decision-making. It can be applied in different areas of operations of business to improve decision-making. For example:. Inventory management: the DSS can be quite useful in evaluating and handling stock stored in the company or any other firm assets that can be optimized or moved around.
In simple terms, the DSS can help business managers in making decisions on the optimal supply chain to use. Sales optimization and projection: the DSS can be used as a tool to analyze sales data for making predictions and also monitoring the sales pattern.
With this information, managers make decisions about the best resource allocation, which will bring in positive results for the organization. The DSS can be used by realtors to monitor and manage a daily running of their businesses. The information from each property can be processed and used to make decisions not only on the routing basis of the company but also for a future planning for the organization Turban, With the help of DSS, a financial institution can verify the credit worthiness of a person before he or she applies for a loan.
Officers can also use the DSS to verify whether a certain firm bidding for different projects is competitive in terms of its costs. Forests are endangered across the world. Illegal human activities such as logging and charcoal burning are reducing forest at a high rate.
Therefore, there is a need to conserve them using proper technology. The DSS has come in handy in forest conservation and management.
It monitors important activities in the forest such as logging, protection, and also log transportation. Hence, it has helped a lot in conserving the forest across the world. The reason is that the forest departments can access information being initially difficult to get when making decisions. As a result, they have improved their services. Transport companies use the DSS to assess and test the transport equipment on a regular basis. It helps organizations avoid any delays and inconveniences that may be caused by undetected worn out transport equipment.
For example, the Canadian National Railway relies on the DSS to test the equipment and reduce incidences of derailments that may be brought about by defective railroads. The DSS can predict the demand for water in a particular region. It is possible through the use of geographical and historical information about the water consumption of the area to forecast and plan for future consumption of water.
In the situations where reaching a decision is difficult, the system assists by giving other alternatives. It improves the level of decision-making and is also faster compared to human performance.
Such benefit can be seen in cases where the active and collaborative DSS is used. It reduces the pressure on the decision maker to reach a decision.
This benefit helps managers to have a better control of the company. The reason is that with the help of the system they can make projections and decisions that will produce results.
The concept of decision support systems DSS is indeed very special with each theory having various definitions. These ones are based on the theory point of view.
Unstructured and Semi-Structured decision problems. Decision support systems can be either fully computerized, human-powered or a combination of both.
While academics have perceived DSS as a tool to support decision making process, DSS is also a tool to facilitate organizational processes. DSSs include knowledge-based systems. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, and personal knowledge, or business models to identify and solve problems and make decisions. DSSs are often contrasted with more automated decision-making systems known as Decision Management Systems.
There are possibilities and examples of building a DSS in any knowledge domain. For example, DSS is extensively used in business and management.
Executive dashboards and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources. Through a DSS, all the information from any organization is represented in the form of charts, graphs in a summarized way, which helps the management to take strategic decisions. For example, one of the DSS applications is the management and development of complex anti-terrorism systems.
In this way, it was possible to assess which of several alternatives offered the best business return. Decision support systems operate at many levels, and there are many examples in common day-to-day use. For example, GPS route planning determines the fastest and best route between two points by analyzing and comparing multiple possible options. Many GPS systems also include traffic avoidance capabilities that monitor traffic conditions in real time, allowing motorists to avoid congestion.
Farmers use crop-planning tools to determine the best time to plant, fertilize and reap. Medical diagnosis software that allows medical personnel to diagnose illnesses is another example. Most systems share a common attribute in that decisions are repetitive and based on known data. However, they aren't infallible and may make incorrect or irrational decisions, something many early GPS users discovered.
Historical data analysis, used in every facet of business and life, is well-developed and mature. Although such information is not always directly actionable, it's an important part of DSS because it reports past performance and highlights areas that need attention. Some examples include:. Numerous manual techniques exist that support decision-making. These include activities such as the SWOT analysis where teams determine their organization's strengths and weaknesses as well as identifying threats facing the organization and potential opportunities for further growth.
The outcomes of a SWOT analysis are actionable decisions for moving the organization forward. Other manual tools include decision matrixes, Pareto analyses and cost benefit analyses. Hybrid DSS solutions include the use of spreadsheet analyses that tap into the capability of Excel to compute, analyze, compare options and evaluate what-if scenarios. Although manual and hybrid DSS solutions are relatively slow and unwieldy, in the right hands, they are powerful decision support tools and many organizations rely on them.
While it's essential to understand what happened in the past, and why it happened, this knowledge is of limited use when trying to predict the future, except possibly in very stable and predictable environments. Typical information that a decision support application might gather and present would be, a Accessing all information assets, including legacy and relational data sources; b Comparative data figures; c Projected figures based on new data or assumptions; d Consequences of different decision alternatives, given past experience in a specific context.
There are a number of Decision Support Systems. These can be categorized into five types: Communication-driven DSS Most communications-driven DSSs are targetted at internal teams, including partners.
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