What is a DSS?

A decision support system (DSS), most simply, helps users make decisions. It’s a program that aids businesses in making judgements and determining courses of action. A DSS goes through large amounts of data, analyzing and organizing it into comprehensive reports that support decision making and problem solving. 

A DSS provides information that managers need to make informed decisions. These systems are usually geared toward specific managerial processes, though they are used to solve more complex issues. 

The types of information used by a DSS include: 

  • Target revenue
  • Projected revenue
  • Sales figures 
  • Inventory and operations-related data 

A DSS can be entirely automated or human-powered – or a combination of both. The best systems don’t just compile information into reports, they actually make decisions for the users. Essentially, they allow for informed decisions to be made more efficiently.

How Decision Support Systems Are Used

The main purpose of a DSS is to present information to users in an easy-to-read and straightforward manner. Operations management and planning departments employ the DSS to support decision making. In general, it’s mid-to-upper management that uses these systems. 

For problems or calculations that are not easy to do manually, like projected revenue, a DSS can integrate all the data and variables and produce an outcome. The system can project revenues according to whatever timeline is required, i.e., six months or the next year, based on past and current sales figures.

Decision support systems can be programmed to create a variety of report types including both written and graphic reports. The reports are based on the specifications the user inputs. 

Since a DSS is basically just an application, it can be utilized on most devices – laptops,  desktops and even DSS options for mobile devices. This flexibility means frequent travellers can be up-to-date at all times, allowing for fast decision making even when on the go. This is especially beneficial in today’s work climate in which there are more hybrid offices and remote workers.

A DSS can be customized for:

  • Specific industries 
  • Professions/fields 
  • Government agencies
  • Agricultural concerns
  • Corporate operations 

A Brief History of DSS

It was a retired telephone executive, Chester Barnard, who brought the term “decision making” from the public administration world to the business world. This phrase soon replaced others like “resource allocation” and “policy making”, and changed how managers operated.

Unlike policy making and resource allocation, which don’t necessarily result in a firm conclusion, “decision making” does involve ending deliberations and taking action.

And so, it was Barnard and other theorists that followed him that spurred the study of managerial decision making. The mission to make the most rational decisions continues, but it was recognized early on that psychological and contextual restraints interfere with the ability of humans to make perfectly rational decisions.  

Other constraints, like time limitations, complex circumstances, emotions, and human computational power, affect our ability to make decisions. It was clear that humans – whether individually or in groups – required some kind of support in decision making. That support would come in the form of electronic computing.

Decision support, as a concept, was developed mainly from the studies of organizational decision making at the Carnegie Institute of Technology during the 1950s-1960s. Stemming from a deep interest in organizational behavior and the human brain, scientists laid the groundwork for computerized tools to assist with decision making. By the 1970s, DSS became a research area of its own.

Over the next two decades, EIS – Executive Information Systems -, and GDSS – Group Decision Support Systems -, and ODSS – Organizational Decision Support Systems -, emerged, having evolved from the single user and model-oriented DSS. 

The scope and definition of DSS also progressed over the years. It started as a computer-based aid to decision-making, but it came to involve users accessing databases and models to solve unstructured problems. Now, it’s seen as a critical tool for managerial tasks.

In the late 80s, a DSS was designed for United Airlines. It directly resulted in the reduction of travel delays as it assisted ground operations management. The realm of DSS expanded in the 1990s with data warehousing and on-line analytical processing (OLAP). Toward the turn of the millennium, new web-based applications were discovered. 

Components of a DSS Framework

A DSS consists of three main components: 

  • Database or Knowledge Base – The database contains information from a variety of sources, both internal and external. Internal information is collected through different applications while external info is mined on the internet (newspapers, online databases, etc.). 
  • User Interface – There are tools that help the end-user of a DSS navigate the system, making the interaction easier. It’s a graphic interface that displays the information in various forms from text to tables and charts. 
  • Model – The model, or software system, includes the mathematical and analytical methods that are used to sift through and analyze the data. There can be multiple models within one system, each performing a specific function. The models used depend on the user requirements and the DSS purpose. 

Types of Decision Support Systems

There is more than one type of DSS and they can be categorized into 5 types: 

  • Communication-driven – This type supports companies who have more than one person working on a shared task. 
  • Model-driven – Not totally data-centered, a model-driven DSS helps decision makers assess situations based on specific parameters and data. 
  • Data-driven – Allows for access to and manipulation of internal and external data.
  • Knowledge-driven – This DSS provides specialized expertise for problem-solving in the form of rules, facts, and structured resources like flowcharts and decision trees. 
  • Document-driven – Collects unstructured information, manages it, and manipulates it in a variety of formats. 

Applications

For example, in a bank, a DSS can be used to establish yearly targets based on deposit and loan trends, as well as verify the credit of loan applicants.

In the clinical field, DS systems support medical diagnoses. DSS’s are also increasingly being used in agriculture to facilitate policy creation and implementation, as well as forest management – specifically for spatial planning and harvest scheduling. 

One specific example that demonstrates the significance of a DSS is the Canadian National Railway system. Using a DSS, the Canadian National Railway system was able to reduce the number of derailments at a time when other railways were seeing an increase.

Pros & Cons of a DSS

The Pros 

How can a DSS benefit an organization? 

  • Makes decision-making faster and more efficient, particularly because a DSS can gather and analyze real-time data.
  • Allows for the automation of managerial processes, freeing up time for managers to spend on making decisions.
  • Promotes skills-development as employees must be trained and acquire specific skills to implement and operate a DSS.
  • Improves interpersonal communication within a company as it requires cooperation and the exchange of information. 

The Cons 

While a DSS is generally an asset for a business, it has its downsides: 

  • Developing and implementing a DSS, as well as training, is a substantial investment, which means it’s not accessible to all organizations. 
  • Companies can become so reliant on a decision support system that the subjectivity of making a decision is lost. 
  • Sometimes too much information results in multiple options for end-users, creating more of a problem than it solves. 
  • A DSS might cause resistance within an organization as employees fear or mistrust new technology and its implications. 

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