What are decision support systems?

Decision support systems are interactive systems that support businesses in making decisions by analysing and evaluating large volumes of data. The systems are used in various industries and are primarily used for unstructured problems in operational functions.

What do decision support systems do?

Decision support systems (DSS) are computer-aided planning and information systems for improving the decision-making capabilities of businesses. The interactive systems help management, operations and planning to structure highly complex problems and make well-founded decisions. Both operational and strategic tasks are supported. The key functions of decision support systems are:

• Sorting, filtering and displaying data
• Evaluation options like comparisons, totaling and averaging
• Model calculations
• Linking data with optimisation algorithms

DSS analyse large amounts of data in order to deliver relevant information in the form of tables, graphics and simulations. They draw on knowledge and data from various areas, including raw data, documents and personal knowledge. As a result, decision support systems offer higher quality information than conventional reports. They primarily get their data from relational databases, data warehouses and cubes (data storage within models). Sometimes they also draw on other sources, like turnover and sales forecasts and electronic health records.

How do decision support systems work?

Decision support systems usually combine three components:

• Knowledge base: The database of knowledge works like a library of information and is a central component of DSS. It includes information that’s internal to the business as well as external sources from the internet. DSS databases can be implemented as a standalone system or a data warehouse.
• Software system: The foundation of the software system is a model, i.e. a simulation of a real system. It uses statistical models that establish relationships between events and variables, sensitivity analysis models (‘what if’ analyses) and various prediction models, such as time series analyses and regression models.
• User interface: Dashboards enable users to look at results and make it easier to process saved data. DSS user interfaces consist of a simple window, command lines and menu-driven interfaces.

What are the types of decision support systems?

There are various types of decision support systems, which can be divided into the following categories based on their primary source of information:

• Data-driven decision support systems are based on data from internal or external databases. They usually use data mining techniques to recognise patterns and derive predictions. Companies often use data-driven DSS to optimise business processes. In public administration, data-driven DSS are used for things like fighting crime.
• Model-driven decision support systems focus on the use of mathematical and simulation-based models that are adapted to specific user requirements. Model-driven DSS usually aren’t too data heavy and are especially useful in situations where it is difficult to make decisions based on historical data alone.
• Communication-driven and group decision support systems support communication, coordination and collaboration. They also help groups involved in decision making to analyse problem situations. These DSS use communications tools like instant messaging.
• Knowledge-driven DSS provide specialised expertise for solving problems, which is stored in a knowledge database that is continuously updated. Knowledge-driven DSS are primarily used for tasks that require human expertise.
• Document-driven DSS integrate special technologies to retrieve and analyse documents. One example is search engines, which allow users to search databases for specific terms.

What are the most important uses for decision support systems?

DSS can be adapted to changing issues and technical circumstances, making them very flexible. However, keep in mind that they merely support human judgement and can’t replace it. That means that humans are still responsible for interpreting the information they provide and making the final decision. Decision support systems simply provide the most relevant information and evaluate the effects of potential decisions.

Decision support systems are primarily useful for dealing with unstructured problems. That includes situations with highly dispersed data and enormous volumes of data (big data) and for cases in which you can’t recognise a logical connection between pieces of information. Some fields DSS are used in include:

• Route planning with GPS: Decision support systems can determine the ideal route between two points. Modern systems can even monitor traffic live, which can help to avoid jams.
• Agriculture: Farmers use DSS to determine the optimal time for sowing, fertilising and harvesting.
• Medicine: Clinical DSS are used to interpret test results, diagnose illnesses and create treatment plans. For example, a clinical DSS by Penn Medicine is designed to wean patients off ventilators more quickly.
• ERP dashboards: ERP dashboards provide a snapshot of key business metrics. Decision support systems can be used to visualise business and production processes and monitor performance targets to identify areas for improvement.

How are DSS and AI related?

Decision support systems typically offer the option to integrate artificial intelligence. Intelligent decision support systems (IDSS) can process very large volumes of data from different sources, which can derive recommendations for better decisions. They use AI technologies like machine learning to recognise patterns and correlations.

Intelligent DSS behave similar to human consultants, but can process and analyse information more efficiently than humans. They are used in, for example, flexible manufacturing, marketing and medical diagnostics.

What are the pros and cons of decision support systems?

DSS offer many benefits that help businesses make decisions more efficiently. They can be seamlessly integrated into existing information systems and extended based on individual needs if necessary. They allow for intuitive use, which is especially important for human-machine interactions. Even though the final decision rests with a human, decision support systems significantly improve planning processes, which in turn often results in cost savings. Another advantage is that it’s possible to trace any data back to its origins.

However, DSS also come with disadvantages. For one, implementing and maintaining a DSS is often expensive. And the quality of their recommendations depends heavily on the data they’re based on. Finally, there is the risk that decision makers may rely too much on DSS and ignore their own judgement.