A decision support system (DSS) is a class of computerized information system that collects, organizes and analyzes data to facilitate decision-making activities in an organization. Decision support systems can either be fully computerized, human-powered or can be a combination of both. Decision support systems usually serve mostly mid and upper ranking level of management, operational and planning staff in an organization, letting them sift through massive amounts of data in order to compile information that can be used to make decisions concerning problems, some of which may or may not be specified in advanced i.e unstructured decision problems.
The History of Decision support systems
The advent of decision support systems began in the mid-1960s. Researchers began to systematically study how the use of computerized quantitative models could assist decision making and planning processes. Around 1970, various business journals started to publish articles on management decision systems, strategic planning systems, and decision support systems. The term “Decision Support System” was first used in 1971 by Gorry and Scott-Morton in their article published in the Sloan management review. They argued that Management Information Systems primarily focused on structured decisions and suggested that the supporting information system for semi-structured and unstructured decisions be termed Decision Support Systems.
The launch of the Worldwide Web and global Internet in 1995 provided a technological platform that further expanded the deployment of computerized decision support. Today, there are various models of Decision support systems which could be complex such as Executive Business Systems, Clinical Decision Support Systems, or basic ones such as Excel.
Classifications of Decision Support Systems
There are different ways of classifying decision support systems, but the most common categorization divides Decision support systems into 4 classes which are;
- Data-driven Decision support systems
- Document-driven Decision support systems
- Model-based Decision support systems
- Knowledge-driven Decision support systems
- Data-driven Decision support systems which emphasize access to and manipulation of data from internal and external sources with the aim of generating reports that would inform strategic decisions.
- Document-driven Decision support systems are used to search databases or web pages to find or retrieve documents related to specific keywords imputed. They are mostly web-based.
- Model-based Decision support systems are complex systems that rely explicitly on data and parameters provided by the user. This data is then manipulated or simulated by the decision maker to analyze specific situations.
- Knowledge-driven Decision support systems use rule-based systems, procedures or facts that have been stored in the database to provide specialized capabilities for decision support.
Components of Decision Support Systems
The components of a Decision Support System can be broadly categorized into five types;
- Hardware: his could be executive workstations, connected through networks to other computers, or personal computers connected through a network to larger computer systems.
- Software: the software packages are otherwise known as Decision support systems generators contain models of databases… These models provide the ability to create, maintain and manipulate the mathematical models in the model using capabilities provided by the packages.
- Data: this comprises information needed for the specific type of decision, extracted by the manager or personnel from the database of the organization.
- Model: the model base includes mathematical formulas and analytical techniques stored in a variety of program modules and files. The model-based management software is tailored to support the specific decision required by the user.
- People: many may argue that this isn’t an actual component of a Decision support systems, but without the users to input, analyze and interpret the data, the Decision support systems are just an application. Users of the Decision support systems may range from data analysts, application developers to the managers who rely on the system for decision making.
Applications of Decision Support Systems
Decision support systems can be applied in a variety of industries including, but not limited to Finance, Agriculture, Healthcare, Engineering. The basic concept is the same i.e decision making, but the model would be designed to suit the specific result desired.Below are some examples of industries where decision support systems are used and how they are applied;
Decision support systems are used extensively in the financial world to get an overall view of events that determine a company’s progress. It can be used to analyze sales data, project a company’s revenue for a given period based on past sales and new assumptions or factors such as new products or emerging competitors. Decision support systems can also be used to identify negative trends and help in the allocation of better resources. The information can be presented in the form of graphs, charts or another simplified way, but the goal is that it be easily understood and makes for easy interpretation.
Decision support systems used in healthcare are known as Clinical Decision Support System (decision support systems). The system is designed to help health professionals and physicians make clinical decisions, they can use it to analyze and reach a diagnosis based on patient data, disease state and treatment trends. A specific example would be in cancer therapy; a Decision support systems can use previous data to determine the appropriate amount of radiotherapy beams a cancer patient may require for treatment, the oncologist would then review the suggested plan to determine it’s viability.
Decision support systems in agriculture can help simulate patterns of crop growth, evaluate farming method, assess possible impacts of climate change on the harvest. The parameters imputed into the software could include soil condition, weather, irrigation, fertilizer type. The system then uses these data to predict the expected crop outcome. Of course, the prediction is not always entirely accurate as there are other natural or man-made factors that may come into play, but it gives the farmer an idea of what to expect.
Although the use of Decision support systems in government has not fully been adopted, various country governments and governmental organizations have adopted the use of Decision support systems to make decisions and effective policies that have impacted it’s population and economy positively. An example of Decision support systems use in government is in the Iraqi government’s compensation of Kuwait for economic loss suffered as a result of it’s an invasion of Kuwait in 1990. The Kuwaiti government used a problem specific Decision support systems to estimate the economic impact of the war by imputing economic performance from before the war, and simulating expected economic performance if there had been no war. Thus they were able to get an estimate of the magnitude of financial loss the economy suffered.