In order to come up with sound critical business decisions, the business leader must take many things into consideration. For one, it is important to know the possible scenarios that might play out if they go with one decision over another. In some instances, they would need to test ideas or simulate situations in order to make a more informed decision.
And even with the data already in place, the next challenge would be how to interpret this data in order to come up with something coherent and comprehensive that decision-makers will be able to understand and draw information from in their decisions. This is where the Decision Support System (DSS) comes in.
Definition
A Decision Support System is an interactive information system that collects, organizes, and analyzes large volumes of data. It leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. Such data could include target or projected revenue, historical and present sales figures, and other inventory- or operations-related data, among others.
As a tool that helps facilitate the decision-making process, DSS supports an organization’s management, operations, and planning levels, especially in matters such as assessing the significance of uncertainties and the tradeoffs involved in making one decision over another.
How it relates to Business Intelligence
Given how DSS is somewhat similar in nature to Business Intelligence (BI), there has been some confusion as to whether they are one and the same or if their similarities are overblown. Truth is, the Decision Support System is considered an element of Business Intelligence, alongside data warehousing and data mining for instance. BI is a broad category of applications, services, and technologies for gathering, storing, analyzing, and accessing data, DSS is more purpose-built for supporting specific decisions.
Types of Decision Support Systems
DSS comes in different forms, with each business having its own DSS setup that best fits its processes. But generally, there have been five types of Decision Support Systems that have been identified. These are:
Data-driven DSS - These include file drawer and management reporting systems, executive information systems, and geographic information systems (GIS). They emphasize access to and manipulation of large databases of structured data, often a time series of internal company data and sometimes external data.
Model-driven DSS - Systems that use accounting and financial models, representational models, and optimization models fall under this type. They emphasize access to and manipulation of a model by leveraging simple statistical and analytical tools.
Knowledge-driven DSS – Also known at times as advisory systems, consultation systems, or suggestion systems, these systems suggest or recommend actions to managers, providing specialized problem-solving expertise based on a particular domain. They are typically used for tasks including classification, configuration, diagnosis, interpretation, planning, and prediction.
Document-driven DSS - These systems integrate storage and processing technologies for document retrieval and analysis, such as search engines.
Communication-driven and group DSS – These systems have two key components: the communication-driven part which focuses on communication, collaboration, and coordination to help people working on a shared task, and the group component (also known as GDSS) supports groups of decision-makers in analyzing problem situations and in group decision-making tasks.
Components of a DSS
The efficiency of Decision Support Systems lies in its three key components. These are:
Database - It gathers data from different sources, including data internal to the organization, data generated by applications, and external data acquired from third parties or mined from the Internet. The database size will vary depending on what the organization will need.
Software system - The software system is built on a model that is designed or chosen based on the purpose of the DSS. Commonly used models include:
Statistical - used to establish relationships between events and factors related to that event.
Sensitivity analysis - used for “what if” analysis.
Optimization analysis - used to find the optimum value for a target variable in relation to other variables.
Forecasting - used to analyze business conditions and formulate plans.
Backward analysis sensitivity - sometimes called goal-seeking analysis, sets a target value for a particular variable and then determines the values other variables need to hit to meet that target value.
User interface - Dashboards and other user interfaces that allow users to interact with and view results.
Application in different industries
With a DSS in place, businesses are able to formulate plans that are more strategic and more accurate, thus resulting in improved operational efficiency and resource allocation. Here are some examples of how DSS benefits various industries and some real-life examples as well.
GPS route planning - DSS can be used to plan the most optimal routes as it is capable to monitor traffic in real-time to route around congestion.
Crop planning – DSS is actually being used by farmers to help them identify the best time to plant, fertilize, and reap specific crops. One particular company, Bayer Crop Science, has used DSS capabilities, in particularly applied analytics and decision support, to every element of its business, including the creation of “virtual factories” to perform “what-if” analyses at its corn manufacturing sites.
Clinical diagnosis - DSS has served as a key component for doctors to diagnose the conditions of their patients Penn Medicine has created a clinical DSS that helps it get ICU patients off ventilators faster.
ERP – DSS can help monitor performance indicators within the business. Digital marketing and services firm Clearlink uses a DSS system to help its managers pinpoint which agents need extra help.
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