DECISION SUPPORT SYSTEMS AND SOFTWARE: Definitions and Examples

DECISION SUPPORT SYSTEMS AND SOFTWARE
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A competent decision support system aids decision-makers in compiling various sorts of data they receive from numerous sources, including documents, raw data, management, business models, and employee first-hand experience. Multiple disciplines can benefit from using decision support system software. including engineering, agriculture, and rail project bid evaluation, as well as the verification of credit loan information, medical diagnosis, business management, and bid evaluation. Stay with me as I walk you through decision support systems software.

Decision Support Systems

An information system called a decision support system (DSS) helps a corporation make decisions that call for judgment, determination, and a series of actions. By analyzing massive amounts of unstructured data and gathering data that might aid in issue-solving and decision-making, the information system supports the mid and high-level management of a company.

Again, a DSS can be manually operated, automatically operated, or both. A decision support system uses analytical models to analyze summary data, exceptions, patterns, and trends. An aid to decision-making, a decision-support system does not always provide a decision. To identify problems, find solutions, and make judgments, the decision-makers assemble relevant information from raw data, papers, personal expertise, and/or business models.

Utilizing a DSS is mostly done to provide customers with information in an understandable fashion. A decision support system’s value lies in the fact that it may be set to produce different kinds of reports depending on what the user needs. For instance, the DSS can produce data and display that data graphically, such as a bar chart that shows predicted revenue, or in the form of a written report.

Data analysis is no longer restricted to big, heavy mainframe computers as technology advances. Since a DSS is essentially an application, you can install it on most desktop and laptop computers. A few decision support systems applications are also accessible on mobile platforms. Users who travel frequently benefit greatly from the decision support systems’ flexibility. They have the chance to stay informed at all times thanks to this, giving them the capacity to choose the best course of action for their business and clients when they’re on the go or even right away.

Characteristics of a Decision Support System 

  • Flexibility and capacity to change
  • a high degree of interaction
  • Easy of use
  • Effectiveness and efficiency
  • total command by the decision-makers
  • Effortless development
  • Extendibility
  • assistance with modeling and analysis
  • assistance with data access
  • independent, combined, and web-based

Components of a Decision Support System

A Decision support system framework consists of three key parts:

#1. Model Management System

Models that managers can use in their decision-making are stored in the model management system S=. Decisions about the organization’s financial well-being and the supply and demand for the company’s products and services are dependent on the models.

#2. User Interface 

The user interface of a DSS has tools that make it easier for the end user to utilize the system.

#3. Information Base

The knowledge base has information from both internal (information from a transaction processing system) and external (newspapers and online databases) sources.

Benefits of Decision Support System

  • A decision support system boosts the effectiveness and speed of decision-making processes. Because a DSS can gather and examine real-time data, it is feasible.
  • As it takes particular expertise to set up and run a decision support system in a business, it encourages training within the company.
  • With less time spent on repetitive chores, managers may have more mental bandwidth for strategic thinking.
  • Within the company, it enhances interpersonal communication.

 Disadvantages of a Decision Support System.

  • A decision support system is less affordable for smaller enterprises because of the high financial expenditure required to build and implement one.
  • As a DSS becomes part of a company’s regular decision-making process to make it faster and more efficient, the company may become dependent on it. However, managers frequently place an excessive amount of trust in the system, which eliminates the element of subjectivity in decision-making.
  • Due to the tendency of information systems to take into account all facets of a situation, a decision support system may result in information overload. End customers have a tough choice to make because there are so many options.
  • Lower-level employees may become fearful of and hostile toward the implementation of a decision support system. Many of them are hesitant to use new technology and worried about losing their employment to it.

Types of Decision Support Systems

Here are eight of the most common decision support system examples you might see at work:

#1. Communication-Driven:

Enables businesses to support tasks that call for the participation of multiple people. It contains integrated technologies like Google Docs and Microsoft SharePoint Workspace.

#2. Model-Driven:

Enables management and access to organizational, financial, and statistical models. By utilizing the data that users have provided, data is gathered and The parameters are established. To analyze scenarios, the data is turned into a decision-making model. Diodes, an open-source model-driven DSS, is an illustration of a model-driven decision support system.

#3. Knowledge-Driven:

Offers specific, factual responses to problems utilizing procedures, rules, or interactive decision-making tools like flowcharts.

#4. Document-Driven:

Controls information in different electronic formats that are not structured.

#5. Driven by Data:

Assists businesses in storing and analyzing both internal and external data.

#6. Intelligent DSS:

A decision support system is intelligent if it incorporates artificial intelligence into its architecture (IDSS). In an intelligent decision support system, AI is used to filter large sets of data by mining and processing the data. The goal of an intelligent decision support system is to make it work like a human advisor.

#7. A Manual DSS:

It uses people rather than technology to aid in decision-making. A team of professionals evaluates the advantages, disadvantages, opportunities, and dangers facing their project or business.

#8. A Hybrid DSS:

It combines elements from many DSS types to produce a complex result. Large-scale problems in sectors like banking and healthcare may call for the use of tools from several decision support systems, including knowledge-driven and data-driven DSSs.

Examples of Decision Support System

There are many different kinds of industries that use decision support systems. Examples include:

  • GPS route preparation: By evaluating the various possibilities, a DSS has the ability to plan the quickest and best routes between two sites. These systems frequently have the capability to monitor traffic in real-time to avoid congestion.
  • Crop preparation: Farmers use DSS to figure out when is the best time to plant, fertilize, and harvest crops. For instance, in order to do “what-if” analyses at its corn manufacturing sites, Bayer Crop Science created “virtual factories,” in which it has applied analytics and decision-support to every aspect of its operations.
  • Scientific DSS: These tools support physicians in making patient diagnoses. A clinical DSS developed by Penn Medicine enables it to take ICU patients off ventilators more quickly.
  • Dashboards for ERP: Managers that use these systems can keep an eye on performance metrics. Clearlink, a digital marketing and services company, uses a DSS system to help its managers identify which of its workers need additional support.

Decision Support Systems Software

A decision support system software, broadly speaking, is analytics software that collects and analyzes data to assist in decision-making.

There are many different types of decision support. They range from model-based DSS systems that use predefined criteria to perform automated calculations and deliver best-case decisions to modern business intelligence systems that use AI and machine learning to suggest insights and analyses for humans to perform. DSS is utilized for many types of problems to solve them quickly and expedite operations, planning, and business management.

The Well-Known Decision Support System Software Includes:

#1. WebFOCUS

For Information Builders, enterprise, and mid-market businesses that must integrate and embed data across applications, this data and analytics platform is designed. It offers cloud, multi-cloud, on-prem, and hybrid alternatives.

#2. QlikView

Built on the Associative Engine, QlikView is the company’s traditional analytics offering. It uses a customizable interface to assist users with their daily duties.

#3. BusinessObjects SAP

BusinessObjects has applications for reporting and analysis to help users understand trends and the reasons behind them.

#4. Spotfire by TIBCO

Users can build dashboards using this data visualization and analytics software, which also powers real-time analytics apps and predictive applications.

#5. Cloud Salesforce Analytics

Built on the Salesforce.com platform, this analytics tool uses artificial intelligence to help businesses identify possibilities and forecast outcomes.

#6. Powernoodle

A cloud-based tool for choice engagement called Powernoodle makes use of cognitive, behavioral, and decision science. It provides support for modeling the workflows of various stakeholder groups and pre-built templates that address typical decision types.

#7. Decision Making using 1000minds.

A set of tools and procedures for decision-making, prioritization, and conjoint analysis are available online under the name 1000minds. It was generated from studies on patient prioritization for surgery conducted at the University of Otago in the 1990s.

#8. Briq

For general contractors and subcontractors in the construction industry, Briq is a platform for predictive analytics and automation. To enable AI for predictive and prescriptive analytics, it uses data from systems such as CRM, project management, and accounting.

What Are the Three Components of Dss?

Decision support systems, according to Management Study HQ, have three important components: the database, the software system, and the user interface.

Conclusion

Decision support systems support decision-making processes with large amounts of data and give management options. Managerial decisions are crucial, yet DSS does not make decisions. These systems are used to carry out sophisticated statistical and mathematical models, data analyses, and decision support.

In this essay, we talk about how decision support systems are different from other information processing systems. It was found that managers used decision support systems to assist with single, semi-structured decisions, quick changes, and easy access to information. A decision support system includes tools that, when combined, can assist managers in making decisions in a variety of real-world situations.

There are both software and hardware parts to each of these tools. A decision support system should assist decision makers at all management levels, both individually and in groups, and in semi-structured decisions. It should also provide tools for simulating and analyzing data so that decision-makers can relate it to a general data station and have enough flexibility to coordinate various management techniques.

Decision Support Systems and Software FAQs

What is a Decision Support Systems Software?

A decision support systems software, broadly speaking, is analytics software that collects and analyzes data to assist in decision-making.

How Many Types of Dss Are There?

Decision support systems are divided into two categories: model-driven DSS and data-driven DSS.

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