A decision support system (DSA) is a web-based information system that supports organizational or business decision-making processes. DSSs help individuals make informed decisions on issues that are rapidly changing and generally not easily defined in advance i.e. current affairs. Organizations rely on DSSs to manage a range of key decision indicators, such as company growth, budgets, financial performance, and supply chain issues. There are various types of DSS applications and technologies available today and we shall discuss some here.
An expert system usually collects, process and analyze data from many sources, such as surveys and internal documents. It provides insights on the most important issues affecting the organization and presents options for future actions based on the findings. An expert system is generally a web-based application. The key elements of a good expert system include:
A web-based decision support system has a number of advantages over other traditional approaches to problem-solving. Web applications enable quick dissemination of data and allows users from any location to access it. It can also be accessed from anywhere, even while on the go. Also, it provides immediate access to up-to-date information and removes the constraints that are usually placed on problem-solving. It is an ideal solution for fast identification of new problems within organizations and for rapid problem solving.
A software engineer builds a decision support system that helps the company make decisions (typically in the form of recommendations and actions). Decisions made by the system to help managers and/or employees make informed decisions about how to respond to change. It can help them predict possible outcomes from future events and make informed decisions in specific areas. This information system can help improve organizational decision-making processes and practices and make it more effective at mitigating risks.
A model-driven emphasizes access control. In a decision support system, the key decision makers are provided with all of the relevant information that they need to make informed decisions. It can provide information that is not known to the rest of the organization or to other decision makers. Model-driven concerns make it easy to provide decision makers with what they need by focusing the attention on the key stakeholders.
A typical information system offers expected sales figures, operating profit, and average inventory levels and provides projected revenue figures six months down the road. It is not feasible to project all of the figures associated with the business well in advance. A decision support system will allow the key decision makers to make informed decisions about operating costs, inventory levels, and profitability.
Another way to think about decision support systems is to think about problem-solving. Decision support systems help solve problems by providing critical analysis and information that allow decision makers to make informed decisions. For example, a manufacturing decision support system may analyze a problem and provide a suggested solution that could easily be implemented. This suggests that there are actual problems in the process rather than a lack of a quality product or an unworkable production process.
A critical component of decision support systems is a method for collecting contingency information that will help the system make better decisions. Contingency data is data that is needed before the system can make a decision and allows the system to adapt if a problem occurs. The data may include the anticipated cost of a new process, the loss of a process, the expected revenue loss resulting from a problem, the anticipated profit loss resulting from a problem, or a combination of these losses. Data contingency allows decision support software to quickly adapt to changing conditions.