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.