A decision support system (DSS) is a computerized program used to support determinations, judgments, and courses of action in an organization or a business. A DSS sifts through and analyzes massive amounts of data, compiling comprehensive information that can be used to solve problems and in decision-making. Typical information used by a DSS includes target or projected revenue, sales figures or past ones from different time periods, and other inventory- or operations-related data.
A decision support system gathers and analyzes data, synthesizing it to produce comprehensive information reports. In this way, as an informational application, a DSS differs from an ordinary operations application, whose function is just to collect data. The DSS can either be completely computerized or powered by humans. In some cases, it may combine both. The ideal systems analyze information and actually make decisions for the user. At the very least, they allow human users to make more informed decisions at a quicker pace. The DSS can be employed by operations management and other planning departments in an organization to compile information and data and synthesize it into actionable intelligence. In fact, these systems are primarily used by mid- to upper-level management. For example, a DSS may be used to project a company's revenue over the upcoming six months based on new assumptions about product sales. Due to a large number of factors that surround projected revenue figures, this is not a straightforward calculation that can be done manually. However, a DSS can integrate all the multiple variables and generate an outcome and alternate outcomes, all based on the company's past product sales data and current variables.
A DSS can be tailored for any industry, profession, or domain including the medical field, government agencies, agricultural concerns, and corporate operations. The primary purpose of using a DSS is to present information to the customer in an easy-to-understand way. A DSS system is beneficial because it can be programmed to generate many types of reports, all based on user specifications. For example, the DSS can generate information and output its information graphically, as in a bar chart that represents projected revenue or as a written report. As technology continues to advance, data analysis is no longer limited to large, bulky mainframe computers. Since a DSS is essentially an application, it can be loaded on most computer systems, whether on desktops or laptops. Certain DSS applications are also available through mobile devices. The flexibility of the DSS is extremely beneficial for users who travel frequently. This gives them the opportunity to be well-informed at all times, providing them the ability to make the best decisions for their company and customers on the go or even on the spot. In organizations, a decision support system (DSS) analyzes and synthesizes vast amounts of data to assist in decision-making. With this information, it produces reports that may project revenue, sales, or manage inventory. Through the integration of multiple variables, a DSS can produce a number of different outcomes based on the company’s previous data and current inputs. Many different industries, from medicine to agriculture, use decision support systems. To help diagnose a patient, a medical clinician may use a computerized decision support system for diagnostics and prescriptions. Combining clinician inputs and previous electronic health records, a decision support system may assist a doctor in diagnosing a patient. Broadly speaking, decision support systems help in making more informed decisions. Often used by upper and mid-level management, decision support systems are used to make actionable decisions, or produce multiple possible outcomes based on current and historical company data. At the same time, decision support systems can be used to produce reports for customers that are easily digestible and can be adjusted based on user specifications. Example of a decision support system for John Day Reservoir.
A decision support system (DSS) is an information system that supports business or organizational decision-making activities. DSSs serve the management, operations and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance—i.e. unstructured and semi-structured decision problems. Decision support systems can be either fully computerized or human-powered, or a combination of both. While academics have perceived DSS as a tool to support decision making processes, DSS users see DSS as a tool to facilitate organizational processes.[1] Some authors have extended the definition of DSS to include any system that might support decision making and some DSS include a decision-making software component; Sprague (1980)[2] defines a properly termed DSS as follows:
DSSs include knowledge-based systems. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, and personal knowledge, or business models to identify and solve problems and make decisions. Typical information that a decision support application might gather and present includes:
HistoryThe concept of decision support has evolved mainly from the theoretical studies of organizational decision making done at the Carnegie Institute of Technology during the late 1950s and early 1960s, and the implementation work done in the 1960s.[3] DSS became an area of research of its own in the middle of the 1970s, before gaining in intensity during the 1980s. In the middle and late 1980s, executive information systems (EIS), group decision support systems (GDSS), and organizational decision support systems (ODSS) evolved from the single user and model-oriented DSS. According to Sol (1987),[4] the definition and scope of DSS have been migrating over the years: in the 1970s DSS was described as "a computer-based system to aid decision making"; in the late 1970s the DSS movement started focusing on "interactive computer-based systems which help decision-makers utilize data bases and models to solve ill-structured problems"; in the 1980s DSS should provide systems "using suitable and available technology to improve effectiveness of managerial and professional activities", and towards the end of 1980s DSS faced a new challenge towards the design of intelligent workstations.[4] In 1987, Texas Instruments completed development of the Gate Assignment Display System (GADS) for United Airlines. This decision support system is credited with significantly reducing travel delays by aiding the management of ground operations at various airports, beginning with O'Hare International Airport in Chicago and Stapleton Airport in Denver Colorado.[5] Beginning in about 1990, data warehousing and on-line analytical processing (OLAP) began broadening the realm of DSS. As the turn of the millennium approached, new Web-based analytical applications were introduced. DSS also have a weak connection to the user interface paradigm of hypertext. Both the University of Vermont PROMIS system (for medical decision making) and the Carnegie Mellon ZOG/KMS system (for military and business decision making) were decision support systems which also were major breakthroughs in user interface research. Furthermore, although hypertext researchers have generally been concerned with information overload, certain researchers, notably Douglas Engelbart, have been focused on decision makers in particular. The advent of more and better reporting technologies has seen DSS start to emerge as a critical component of management design. Examples of this can be seen in the intense amount of discussion of DSS in the education environment. ApplicationsDSS can theoretically be built in any knowledge domain. One example is the clinical decision support system for medical diagnosis. There are four stages in the evolution of clinical decision support system (CDSS): the primitive version is standalone and does not support integration; the second generation supports integration with other medical systems; the third is standard-based, and the fourth is service model-based.[6] DSS is extensively used in business and management. Executive dashboard and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources. Due to DSS, all the information from any organization is represented in the form of charts, graphs i.e. in a summarized way, which helps the management to take strategic decisions. For example, one of the DSS applications is the management and development of complex anti-terrorism systems.[7] Other examples include a bank loan officer verifying the credit of a loan applicant or an engineering firm that has bids on several projects and wants to know if they can be competitive with their costs. A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. Agricultural DSSes began to be developed and promoted in the 1990s.[8] For example, the DSSAT4 package,[9] The Decision Support System for Agrotechnology Transfer[10] developed through financial support of USAID during the 80s[citation needed] and 90s, has allowed rapid assessment of several agricultural production systems around the world to facilitate decision-making at the farm and policy levels. Precision agriculture seeks to tailor decisions to particular portions of farm fields. There are, however, many constraints to the successful adoption of DSS in agriculture.[11] DSS is also prevalent in forest management where the long planning horizon and the spatial dimension of planning problems demand specific requirements. All aspects of Forest management, from log transportation, harvest scheduling to sustainability and ecosystem protection have been addressed by modern DSSs. In this context, the consideration of single or multiple management objectives related to the provision of goods and services that are traded or non-traded and often subject to resource constraints and decision problems. The Community of Practice of Forest Management Decision Support Systems provides a large repository on knowledge about the construction and use of forest Decision Support Systems.[12] A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A problem faced by any railroad is worn-out or defective rails, which can result in hundreds of derailments per year. Under a DSS, the Canadian National Railway system managed to decrease the incidence of derailments at the same time other companies were experiencing an increase. DSS has been used for risk assessment to interpret monitoring data from large engineering structures such as dams, towers, cathedrals, or masonry buildings. For instance, Mistral is an expert system to monitor dam safety, developed in the 1990s by Ismes (Italy). It gets data from an automatic monitoring system and performs a diagnosis of the state of the dam. Its first copy, installed in 1992 on the Ridracoli Dam (Italy), is still operational 24/7/365.[13] It has been installed on several dams in Italy and abroad (e.g., Itaipu Dam in Brazil),[14] and on monuments under the name of Kaleidos.[15] Mistral is a registered trade mark of CESI. GIS has been successfully used since the ‘90s in conjunction with DSS, to show on a map real-time risk evaluations based on monitoring data gathered in the area of the Val Pola disaster (Italy). [16] ComponentsDesign of a drought mitigation decision support systemThree fundamental components of a DSS architecture are:[17][18][19][20][21]
The users themselves are also important components of the architecture.[17][21] TaxonomiesUsing the relationship with the user as the criterion, Haettenschwiler[17] differentiates passive, active, and cooperative DSS. A passive DSS is a system that aids the process of decision making, but that cannot bring out explicit decision suggestions or solutions. An active DSS can bring out such decision suggestions or solutions. A cooperative DSS allows for an iterative process between human and system towards the achievement of a consolidated solution: the decision maker (or its advisor) can modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation, and likewise the system again improves, completes, and refines the suggestions of the decision maker and sends them back to them for validation. Another taxonomy for DSS, according to the mode of assistance, has been created by D. Power:[22] he differentiates communication-driven DSS, data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS.[18]
Using scope as the criterion, Power[25] differentiates enterprise-wide DSS and desktop DSS. An enterprise-wide DSS is linked to large data warehouses and serves many managers in the company. A desktop, single-user DSS is a small system that runs on an individual manager's PC. Development frameworksSimilarly to other systems, DSS systems require a structured approach. Such a framework includes people, technology, and the development approach.[19] The Early Framework of Decision Support System consists of four phases:
DSS technology levels (of hardware and software) may include:
An iterative developmental approach allows for the DSS to be changed and redesigned at various intervals. Once the system is designed, it will need to be tested and revised where necessary for the desired outcome. ClassificationThere are several ways to classify DSS applications. Not every DSS fits neatly into one of the categories, but may be a mix of two or more architectures. Holsapple and Whinston[26] classify DSS into the following six frameworks: text-oriented DSS, database-oriented DSS, spreadsheet-oriented DSS, solver-oriented DSS, rule-oriented DSS, and compound DSS. A compound DSS is the most popular classification for a DSS; it is a hybrid system that includes two or more of the five basic structures.[26] The support given by DSS can be separated into three distinct, interrelated categories:[27] Personal Support, Group Support, and Organizational Support. DSS components may be classified as:
DSSs which perform selected cognitive decision-making functions and are based on artificial intelligence or intelligent agents technologies are called intelligent decision support systems (IDSS)[28] The nascent field of decision engineering treats the decision itself as an engineered object, and applies engineering principles such as design and quality assurance to an explicit representation of the elements that make up a decision. See also
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