Decision support system
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A decision support system (DSS) is an
While academics have perceived DSS as a tool to support
- DSS tends to be aimed at the less well structured, underspecified problem that upper level managers typically face;
- DSS attempts to combine the use of models or analytic techniques with traditional data access and retrieval functions;
- DSS specifically focuses on features which make them easy to use by non-computer-proficient people in an interactivemode; and
- DSS emphasizes decision makingapproach of the user.
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, personal knowledge, and/or business models to identify and solve problems and make decisions.
Typical information that a decision support application might gather and present includes:
- inventories of information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),
- comparative sales figures between one period and the next,
- projected revenue figures based on product sales assumptions.
History
The 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
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.
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 model-based.[6]
DSS is extensively used in business and management.
A growing area of DSS application, concepts, principles, and techniques is in
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
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
Components
Three fundamental components of a DSS architecture are:[17][18][19][20][21]
- the database (or knowledge base),
- the model (i.e., the decision context and user criteria)
- the user interface.
The
Taxonomies
Using 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]
- A communication-driven DSS enables cooperation, supporting more than one person working on a shared task; examples include integrated tools like Google Docs or Microsoft SharePoint Workspace.[23]
- A data-driven DSS (or data-oriented DSS) emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data.
- A document-driven DSS manages, retrieves, and manipulates unstructured informationin a variety of electronic formats.
- A knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules, procedures or in similar structures like interactive decision trees and flowcharts.[18]
- A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or open-source model-driven DSS generator.[24]
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 frameworks
Similarly 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:
- Intelligence – Searching for conditions that call for decision;
- Design – Developing and analyzing possible alternative actions of solution;
- Choice – Selecting 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:
- 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.
- Generator contains Hardware/software environment that allows people to easily develop specific DSS applications. This level makes use of case tools or systems such as Crystal, iThink.
- Tools include lower level hardware/software. DSS generators including special languages, function libraries and linking modules
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.
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[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:
- Inputs: Factors, numbers, and characteristics to analyze
- User knowledge and expertise: Inputs requiring manual analysis by the user
- Outputs: Transformed data from which DSS "decisions" are generated
- Decisions: Results generated by the DSS based on user criteria
DSSs which perform selected
The nascent field of
See also
- Argument map
- Cognitive assets (organizational)
- Decision theory
- Enterprise decision management
- Expert system
- Information assurance
- Integrative thinking
- Judge–advisor system
- Knapsack problem
- 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
- )
- ^ Sprague, R;(1980). "A Framework for the Development of Decision Support Systems." MIS Quarterly. Vol. 4, No. 4, pp. 1–25.
- ISBN 0-201-03667-3
- ^ ISBN 90-277-2437-7. pp. 1–2.
- ^ Efraim Turban; Jay E. Aronson; Ting-Peng Liang (2008). Decision Support Systems and Intelligent Systems. p. 574.
- PMID 18462999.
- S2CID 15362734.
- ISSN 0018-5345.
- ^ "DSSAT4 (pdf)" (PDF). Archived from the original (PDF) on 27 September 2007. Retrieved 29 December 2006.
- ^ "Official Home of the DSSAT Cropping Systems Model". DSSAT.net. Retrieved 19 August 2021.
- ^ Stephens, W. and Middleton, T. (2002). Why has the uptake of Decision Support Systems been so poor? In: Crop-soil simulation models in developing countries. 129-148 (Eds R.B. Matthews and William Stephens). Wallingford:CABI.
- ^ Community of Practice Forest Management Decision Support Systems, http://www.forestdss.org/
- . Retrieved 5 March 2014.
- ^ Masera, Alberto; et al. "Integrated approach to dam safety". Comitê Brasileiro de Barragens. Retrieved 16 December 2020.
- .
- S2CID 1746570.
- ^ a b c Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsunterstützung. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: 189-208.
- ^ a b c Power, D. J. (2002). Decision support systems: concepts and resources for managers. Westport, Conn., Quorum Books.
- ^ ISBN 0-13-086215-0
- ISBN 0-07-281947-2
- ^ a b Marakas, G. M. (1999). Decision support systems in the twenty-first century. Upper Saddle River, N.J., Prentice Hall.
- ^ "Decision Support Systems (DSS) Articles On-Line".
- ISBN 9780764548932. Retrieved 30 October 2019 – via ACM Digital Library.
- ^ Gachet, A. (2004). Building Model-Driven Decision Support Systems with Dicodess. Zurich, VDF.
- ^ Power, D. J. (1996). What is a DSS? The On-Line Executive Journal for Data-Intensive Decision Support 1(3).
- ^ ISBN 0-324-03578-0
- ^ Hackathorn, R. D., and P. G. W. Keen. (1981, September). "Organizational Strategies for Personal Computing in Decision Support Systems." MIS Quarterly, Vol. 5, No. 3.
- ^ F. Burstein; C. W. Holsapple (2008). Handbook on Decision Support Systems. Berlin: Springer Verlag.
Further reading
- Marius Cioca, Florin Filip (2015). Decision Support Systems – A Bibliography 1947-2007.
- Borges, J.G, Nordström, E.-M. Garcia Gonzalo, J. Hujala, T. Trasobares, A. (eds). (2014). " Computer-based tools for supporting forest management. The experience and the expertise world-wide. Dept of Forest Resource Management, Swedish University of Agricultural Sciences. Umeå. Sweden.
- Delic, K.A., Douillet, L. and Dayal, U. (2001) "Towards an architecture for real-time decision support systems:challenges and solutions.
- Diasio, S., Agell, N. (2009) "The evolution of expertise in decision support technologies: A challenge for organizations," cscwd, pp. 692–697, 13th International Conference on Computer Supported Cooperative Work in Design, 2009. https://web.archive.org/web/20121009235747/http://www.computer.org/portal/web/csdl/doi/10.1109/CSCWD.2009.4968139
- Gadomski, A.M. et al.(2001) "An Approach to the Intelligent Decision Advisor (IDA) for Emergency Managers Archived 5 March 2016 at the Wayback Machine", Int. J. Risk Assessment and Management, Vol. 2, Nos. 3/4.
- Gomes da Silva, Carlos; Clímaco, João; Figueira, José (2006). "A scatter search method for bi-criteria {0,1}-knapsack problems". European Journal of Operational Research. 169 (2). Elsevier BV: 373–391. ISSN 0377-2217.
- Ender, Gabriela; E-Book (2005–2011) about the OpenSpace-Online Real-Time Methodology: Knowledge-sharing, problem solving, results-oriented group dialogs about topics that matter with extensive conference documentation in real-time. Download https://web.archive.org/web/20070103022920/http://www.openspace-online.com/OpenSpace-Online_eBook_en.pdf
- Jiménez, Antonio; Ríos-Insua, Sixto; Mateos, Alfonso (2006). "A generic multi-attribute analysis system". Computers & Operations Research. 33 (4). Elsevier BV: 1081–1101. ISSN 0305-0548.
- Jintrawet, Attachai (1995). "A Decision Support System for Rapid Assessment of Lowland Rice-based Cropping Alternatives in Thailand". Agricultural Systems. 47 (2): 245–258. .
- Matsatsinis, N.F. and Y. Siskos (2002), Intelligent support systems for marketing decisions, Kluwer Academic Publishers.
- Omid A.Sianaki, O Hussain, T Dillon, AR Tabesh – ... Intelligence, Modelling and Simulation (CIMSiM), 2010, Intelligent decision support system for including consumers' preferences in residential energy consumption in smart grid
- Power, D. J. (2000). Web-based and model-driven decision support systems: concepts and issues. in proceedings of the Americas Conference on Information Systems, Long Beach, California.
- Reich, Yoram; Kapeliuk, Adi (2005). "A framework for organizing the space of decision problems with application to solving subjective, context-dependent problems". Decision Support Systems. 41 (1). Elsevier BV: 1–19. ISSN 0167-9236.
- ISBN 978-0471173359
- Silver, M. (1991). Systems that support decision makers: description and analysis. Chichester; New York, Wiley.
- Sprague, Ralph (1986). Decision support systems : putting theory into practice. Englewood Cliffs, N.J: Prentice-Hall. OCLC 13123699.