Decision support system

decision support system ( DSS ) is a computer-based information system that supports business or organizational decision-making activities. DSSs serving 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-ie Unstructured and Semi-Structured decision problems. Decision support systems can be either fully computerized, human-powered or a combination of both.

While academics-have Perceived DSS as a tool to supporting decision making process , users see DSS 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:

  1. DSS tends to be at the very least structured, underspecified problem that upper level managers typically face;
  2. DSS attempts to combine the use of models or analytic techniques with traditional data access and retrieval functions;
  3. DSS, which is based on the use of non-computer-proficient people in an interactive mode; and
  4. DSS emphasizes flexibility and adaptability to Accommodate exchange in the environment and the decision making approach of the user.

DSSs include knowledge-based systems . A properly designed DSS is an interactive software-based system designed to help decision-makers compile 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 could gather and present includes:

  • Inventories of information assets (including legacy and relational data sources, cubes, data warehouses , and data marts )
  • Comparative sales figures between a period and the next,
  • Projected revenues based on product sales assumptions.

History

The Carnegie Institute of Technology during the late 1950s and early 1960s, and the implementation of the work carried out in the 1960s. [3] DSS became an area of ​​research of its own in the 1970s, before gaining in intensity during the 1980s. In the middle and late 1980s, executive decision-support systems (GDSS) , executive information systems (EIS), 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 has 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 for the effectiveness of management and professional activities, and towards the end of 1980s DSS to 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 is a leading provider of enterprise-class data management solutions . Examples of this can be seen in the DSS in the education environment.

DSS also has 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 qui aussi Were Major Breakthroughs in user interface research. Furthermore, ALTHOUGH hypertext Researchers-have beens Generally Concerned with information overload , some Researchers, notably Douglas Engelbart , we-have-been Focused decision makers In Particular.

Taxonomies

Using the relationship with the user as the criterion, Haettenschwiler [6] differentiates passive , active , and cooperative DSS . A passive DSS is a system that aids the process of decision making, but can not bring out explicit decision suggestions or solutions. An active DSS can bring out such decision suggestions or solutions. A co- operative DSS can be used to make a decision on the decision-making process of the decision maker (or its advisor). For validation, and likewise the system again improves, complete,

Another taxonomy for DSS, according to the mode of assistance, has been created by Daniel Power: he DSS , data-driven DSS , document-driven DSS , knowledge-driven DSS , and DSS model-driven . [7]

  • communication-driven DSS enables cooperation, supporting more than one person working on a shared task; Microsoft Docs or Microsoft Groove . [8]
  • data-driven DSS (or data-oriented DSS) emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data.
  • document-driven DSS manages, retrieves, and manipulates unstructured information in a variety of electronic formats.
  • knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules, procedures, or in similar structures. [7]
  • model-driven DSS based on a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation; They are not necessarily data-intensive. Dicodess is an example of an open source model-driven DSS generator. [9]

Using the scope of the criterion, Power [10] 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 PC.

Components

Design of a drought mitigation decision support system

Three fundamental components of a DSS architecture are: [6] [7] [11] [12] [13]

  1. The database (or knowledge base ),
  2. The model (ie, the decision context and user criteria)
  3. The user interface .

The users themselves are also important components of the architecture. [6] [13]

Development frameworks

Similarly to other systems, DSS systems require a structured approach. Such a framework includes people, technology, and the development approach. [11]

The Early Framework of Decision Support System consists of four phases:

  • Intelligence – Searching for conditions that call for decision;
  • Design – Developing and analyzing possible alternative actions of solution;
  • Choice – Select a course of action among those;
  • Implementation – Adopting the selected course of action in decision situation.

DSS technology levels (of hardware and software) may include:

  1. The actual application that will be used by the user. This is the part of the application that allows the decision maker to make decisions in a particular problem area. The user can act upon that particular problem.
  2. Generator contains Hardware / software environment that allows people to easily develop specific DSS applications. Crystal, Analytica and iThink .
  3. Tools include lower level hardware / software. DSS generators including special languages, function libraries and linking modules

An iterative developmental approach allows the DSS to be changed and redesigned at various intervals. Once the system is designed, it will be tested and revised as necessary for the desired outcome.

Classification

There 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 [14] classify DSS in the following six frameworks: text-oriented DSS, database-oriented DSS, DSS, spreadsheet-oriented DSS, solver-oriented 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. [14]

The support given by DSS can be separated into three separate, interrelated categories: [15] Personal Support, Group Support, and Organizational Support.

DSS components may be classified as:

  1. Inputs: Factors, numbers, and characteristics to analyze
  2. User knowledge and expertise: Inputs requiring manual analysis
  3. Outputs: Transformed data from which DSS “decisions” are generated
  4. Decisions: Results based on the DSS based on user criteria

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) [16]

The nascent field of decision engineer 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.

Applications

DSS 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-based model. [17]

DSS is extensively used in business and management. Executive dashboard and other business performance software. Due to DSS to all the information from any organization is represented in the form of charts, graphs ie in a summarized way, which helps the management to take strategic decision. For example, one of the DSS applications is the management and development of complex anti-terrorism systems. [18] Other examples include a bank loan officer and a banker.

A growing area of ​​DSS application, concepts, principles, and techniques is in agricultural production , marketing for sustainable development . For example, the DSSAT4 package, [19] [20] developed through financial support of the USAID during the 80s and 90s, has allowed rapid assessment of various agricultural production systems around the world to facilitate decision-making at the farm and policy levels. Precision farming and farming . There are, however, many constraints to the successful adoption on DSS in agriculture. [21]

DSS are also prevalent in forest management where the long planning horizon and the spatial dimension of planning problems demands 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, Decision Support Systems provides a comprehensive repository of knowledge on the construction and use of forest. [22]

A Canadian National Railway System, which tests the 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 was managed to reduce the incidence of derailments at the same time.

See also

Wikimedia Commons has media related to Decision support systems .
  • Argument map
  • Cognitive assets (organizational)
  • Decision theory
  • Enterprise decision management
  • Expert system
  • Judge-advisor system
  • Land allocation decision support system
  • List of concept and mind-mapping software
  • Morphological analysis (problem-solving)
  • Online deliberation
  • Participation (decision making)
  • Predictive analytics
  • Project management software
  • Self service software
  • Spatial decision support system
  • Strategic planning software

References

  1. Jump up^ Keen, Peter; (1980), “Decision support systems: a research perspective.” Cambridge, Mass. : Center for Information Systems Research, Alfred P. Sloan School of Management. http://hdl.handle.net/1721.1/47172
  2. Jump up^ Sprague, R (1980). “A Framework for the Development of Decision Support Systems.” MIS Quarterly. Flight. 4, No. 4, pp.1-25.
  3. Jump up^ Keen, PGW (1978). Decision support systems: an organizational perspective. Reading, Mass., Addison-Wesley Pub. Co.ISBN 0-201-03667-3
  4. ^ Jump up to:a b Henk G. Sol et al. (1987). Expertise and Artificial Intelligence in Decision Support Systems: Proceedings of the Second Euroconference, Lunteren, The Netherlands, 17-20 November 1985 . Springer, 1987. ISBN 90-277-2437-7 . p.1-2.
  5. Jump up^ Efraim Turban; Jay E. Aronson; Ting-Peng Liang (2008). Decision Support Systems and Intelligent Systems . p. 574.
  6. ^ Jump up to:a b c Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsunterstützung. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: 189-208.
  7. ^ Jump up to:a b c Power, DJ (2002). Decision support systems: concepts and resources for managers. Westport, Conn., Quorum Books.
  8. Jump up^ Stanhope, P. (2002). Get in the groove: building tools and peer-to-peer solutions with the Groove platform. New York, Hungry Minds
  9. Jump up^ Gachet, A. (2004). Building Model-Driven Decision Support Systems with Dicodess. Zurich, VDF.
  10. Jump up^ Power, DJ (1996). What is a DSS? The On-Line Executive Journal for Data-Intensive Decision Support 1 (3).
  11. ^ Jump up to:a b Sprague, RH and ED Carlson (1982). Building effective decision support systems. Englewood C ㄴ liffs, NJ, Prentice-Hall. ISBN 0-13-086215-0
  12. Jump up^ Haag, Cummings, McCubbrey, Pinsonneault, Donovan (2000). Management Information Systems: For The Information Age. McGraw-Hill Ryerson Limited: 136-140. ISBN 0-07-281947-2
  13. ^ Jump up to:a b Marakas, GM (1999). Decision support systems in the twenty-first century. Upper Saddle River, NJ, Prentice Hall.
  14. ^ Jump up to:a b Holsapple, CW, and AB Whinston. (1996). Decision Support Systems: A Knowledge-Based Approach. St. Paul: West Publishing. ISBN 0-324-03578-0
  15. Jump up^ Hackathorn, RD, and PGW Keen. (1981, September). “Organizational Strategies for Personal Computing in Decision Support Systems.” MIS Quarterly, Vol. 5, No. 3.
  16. Jump up^ F. Burstein; CW Holsapple (2008). Handbook on Decision Support Systems. Berlin: Springer Verlag .
  17. Jump up^ Wright, A; Sittig, D (2008). “A framework and model for evaluating clinical decision support architectures”. Journal of Biomedical Informatics. 41 : 982-990. Doi : 10.1016 / j.jbi.2008.03.009 .
  18. Jump up^ Zhang, SX; Babovic, V. (2011). “An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions” . Decision Support Systems . 51 (1): 119-129.
  19. Jump up^ DSSAT4 (pdf)
  20. Jump up^ The Decision Support System for Agrotechnology Transfer
  21. Jump up^ Stephens, W. and Middleton, T. (2002). Why have the uptake of Decision Support Systems been so poor? In: Crop-soil simulation models in developing countries. 129-148 (Eds RB Matthews and William Stephens). Wallingford: CABI.
  22. Jump up^ Community of Practice Forest Management Decision Support Systems,http://www.forestdss.org/

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