A decision support system (DSS) saves time and increases confidence during business decision-making. When you use a DSS, you benefit from its data analysis and ability to identify patterns. Different DSS options streamline your productivity and support your decision-making at work. In this article, we discuss eight of the most common DSS examples, how they perform and who typically uses them.
What is a DSS?
Decision support systems are types of information systems that analyze data to support executives, managers and staff in business decision-making. These systems assess large amounts of data, identify patterns and analyze the effects of business decisions. A DSS uses current and historical data to predict future trends, which reduces guessing and saves time. Businesses in all industries can benefit from DSS software, such as:
GPS: A GPS analyzes route information and traffic data to plan the best path between places.
Crop planning: Decision support systems help farmers know the best time to plant, fertilize and harvest crops.
Enterprise resource planning (ERP) dashboards: Decision-makers use ERP dashboards to oversee performance indicators.
Clinical decision-making: Medical professionals use clinical decision-support systems to help diagnose and plan treatment for patients.
A DSS relies on these main components to perform its functions:
User interface: The program an individual uses to interact with the DSS.
Knowledge base: The volume of data the DSS uses, which can be as small as a business’ private server or as large as the internet.
Model management system: The computer software responsible for storing and manipulating data models.
8 decision support system examples
Here are eight of the most common examples of decision support systems you might encounter in the workplace :
1. Data-driven DSS
A data-driven DSS gives users access to a large amount of internal and external data. This DSS will query a database using the web, an external server or a company’s mainframe. It relies on data mining to provide patterns and information about the data being assessed. Users rely on data-driven decision support systems to make decisions about businesses, inventories and products. Managers might find data-driven decision support systems most helpful when analyzing current and historical data to report on the conditions of a department or the business. CEOs, managers and staff might use a data-driven DSS.
Software examples of a data-driven DSS include:
Geographic Information Systems (GIS)
File drawer systems
Executive information systems
Computer-based databases with query systems
2. Model-driven DSS
A model-driven DSS allows a user to analyze and manipulate specific models of data, such as statistics, finances or scheduling. These decision support systems are specific to the type of model the user wants to interact with and typically offer less data than other DSS types. They analyze scenarios and data to allow the user to manipulate a model, such as creating a work schedule. They might use simple analysis tools or complex statistics, depending on the model’s purpose and the user’s needs. Managers, staff and third parties who interact with a business might use a model-driven DSS.
Software examples of a model-driven DSS include:
Decision analysis modeling
3. Knowledge-driven DSS
With a knowledge-driven DSS, a knowledge-management system monitors continually updated data about an organization to support decisions. The DSS uses diagnosis, prediction, interpretation and classification to recommend actions consistent with the business. A knowledge-driven DSS can be helpful to managers because it performs tasks faster than a human might. They can also help consumers decide which products and services to buy. This kind of DSS often relies on a data-mining component. Managers, staff and external users, such as customers, might use a knowledge-driven DSS.
Software examples of a knowledge-driven DSS include:
Software that identifies new or current customers who might be interested in products
Product selection software
4. Document-driven DSS
A document-driven DSS retrieves unstructured information from a variety of electronic sources. It searches webpages, documents in databases and other information based on a user’s search terms to gather relevant information. A document-driven DSS might be specific to a business’ private files or as broad as a common internet search engine. Anyone using a database’s search function or an internet search engine is using a document-driven DSS.
Software examples of a document-driven DSS include:
Database search software
Article databases with search functions
5. Communication-driven DSS
A communication-driven DSS uses tools to support communication and collaboration. Email is an example of a communication-driven DSS. This type of DSS includes shared tools that allow multiple people to work on a project at once and software that allows for digital communication between people. It improves a shared project’s efficiency and effectiveness and can help facilitate meetings and conversations. Internal team members, virtual business meeting hosts and online chat and video meeting software users can benefit from a communication-driven DSS.
A communication-driven DSS might also be called a group DSS. A communication-driven DSS focuses on communication and collaboration, while a group DSS helps groups streamline the decision-making process. A communication-driven DSS, for example, might help two people who work for the same company on different shifts share documents. It might also allow employees on opposite sides of the country to meet virtually to view a shared file. Software examples of a communication-driven DSS include:
Chat and instant messaging services
Collaboration software, such as document sharing and editing software
6. Intelligent DSS
Any DSS with artificial intelligence in its design is an example of an intelligent DSS (IDSS). Within an IDSS, AI does data mining and processing to filter through large datasets. An IDSS is designed to offer similar services to a human consultant. They’re programmed to identify patterns and trends to guide decision-making. They can also resolve problems and analyze solutions. AI components add advantages, such as fuzzy logic and machine learning, to a DSS. Managers, diagnosticians and other decision-makers might use an IDSS.
Software examples of an intelligent DSS include:
Smart manufacturing systems
Medical diagnostic systems
7. Manual DSS
A manual DSS relies on individuals instead of computers to support decision-making. A group of experts analyzes the strengths, weaknesses, opportunities and threats of their organization or project. A manual DSS is much slower than a computer-based DSS, but certain types of analysis still need a human eye at every step. Economists, executives and managers might use a manual DSS.
Examples of manual DSS include:
8. Hybrid DSS
A hybrid DSS combines parts of multiple DSS types to create a complex outcome. Large issues in industries such as finance and health care might require the tools of multiple decision support systems, such as a knowledge-driven DSS and a data-driven DSS. A hybrid DSS might use additional software to help these components work together. Sometimes a human analyzes and combines the results of each DSS. A hybrid DSS might also describe a system in which a human works with a DSS to extract and manipulate data. Medical professionals, financial decision-makers and researchers might use a hybrid DSS.
Software examples of a hybrid DSS include:
I hope you find this article helpful.