A Decision Support System (DSS) is an umbrella term used to describe any computer application that enhances the user’s ability to make decisions. More specifically, the term is usually used to describe a computer-based system designed to help decision-makers use data, knowledge and communications technology to identify problems and make decisions to solve those problems.
Typical information that a decision support application might gather and present would be:
Comparative sales figures between one week and the next
Projected revenue figures based on new product sales assumptions
The consequences of different decision alternatives, given past experience in a context that is described
A decision support system may present information graphically and may include an expert system or artificial intelligence (AI). It may be aimed at business executives or some other group of knowledge workers.
Decision Support Systems Origins
In the 1960s, researchers began systematically studying the use of computerized quantitative models to assist in decision making and planning (Raymond, 1966; Turban, 1967; Urban, 1967, Holt and Huber, 1969). Ferguson and Jones (1969) reported the first experimental study using a computer aided decision system. They investigated a production scheduling application running on an IBM 7094. In retrospect, a major historical turning point was Michael S. Scott Morton’s (1967) dissertation field research at Harvard University.
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Scott Morton’s study involved building, implementing and then testing an interactive, model-driven management decision system. Fellow Harvard Ph.D. student Andrew McCosh asserts that the “concept of decision support systems was first articulated by Scott Morton in February 1964 in a basement office in Sherman Hall, Harvard Business School” (McCosh email, 2002) in a discussion they had about Scott Morton’s dissertation. During 1966, Scott Morton (1971) studied how computers and analytical models could help managers make a recurring key business planning decision. He conducted an experiment in which managers actually used a Management Decision System (MDS). Marketing and production managers used an MDS to coordinate production planning for laundry equipment. The MDS ran on an IDI 21 inch CRT with a light pen connected using a 2400 bps modem to a pair of Univac 494 systems.
The pioneering work of George Dantzig, Douglas Engelbart and Jay Forrester likely influenced the feasibility of building computerized decision support systems. In 1952, Dantzig became a research mathematician at the Rand Corporation, where he began implementing linear programming on its experimental computers. In the mid-1960s, Engelbart and colleagues developed the first hypermedia-groupware system called NLS (oNLine System). NLS facilitated the creation of digital libraries and the storage and retrieval of electronic documents using hypertext. NLS also provided for on-screen video teleconferencing and was a forerunner to group decision support systems. Forrester was involved in building the SAGE (Semi-Automatic Ground Environment) air defense system for North America completed in 1962. SAGE is probably the first computerized data-driven DSS. Also, Professor Forrester started the System Dynamics Group at the Massachusetts Institute of Technology Sloan School. His work on corporate modeling led to programming DYNAMO, a general simulation compiler.
In 1960, J.C.R. Licklider published his ideas about the future role of multiaccess interactive computing in a paper titled “Man-Computer Symbiosis.” He saw man-computer interaction as enhancing both the quality and efficiency of human problem solving and his paper provided a guide for decades of computer research to follow. Licklider was the architect of Project MAC at MIT that furthered the study of interactive computing.
By April 1964, the development of the IBM System 360 and other more powerful mainframe systems made it practical and cost-effective to develop Management Information Systems (MIS) for large companies (Davis, 1974). These early MIS focused on providing managers with structured, periodic reports and the information was primarily from accounting and transaction processing systems, but the systems did not provide interactive support to assist managers in decision making.
Around 1970 business journals started to publish articles on management decision systems, strategic planning systems and decision support systems (Sprague and Watson 1979).. For example, Scott Morton and colleagues McCosh and Stephens published decision support related articles in 1968. The first use of the term decision support system was in Gorry and Scott-Morton’s (1971) Sloan Management Review article. They argued that Management Information Systems primarily focused on structured decisions and suggested that the supporting information systems for semi-structured and unstructured decisions should be termed “Decision Support Systems”.
T.P. Gerrity, Jr. focused on Decision Support Systems design issues in his 1971 Sloan Management Review article titled “The Design of Man-Machine Decision Systems: An Application to Portfolio Management”. The article was based on his MIT Ph.D. dissertation. His system was designed to support investment managers in their daily administration of a clients’ stock portfolio.
John D.C. Little, also at Massachusetts Institute of Technology, was studying DSS for marketing. Little and Lodish (1969) reported research on MEDIAC, a media planning support system. Also, Little (1970) identified criteria for designing models and systems to support management decision-making. His four criteria included: robustness, ease of control, simplicity, and completeness of relevant detail. All four criteria remain relevant in evaluating modern Decision Support Systems. By 1975, Little was expanding the frontiers of computer-supported modeling. His DSS called Brandaid was designed to support product, promotion, pricing and advertising decisions. Little also helped develop the financial and marketing modeling language known as EXPRESS.
In 1974, Gordon Davis, a Professor at the University of Minnesota, published his influential text on Management Information Systems. He defined a Management Information System as “an integrated, man/machine system for providing information to support the operations, management, and decision-making functions in an organization. (p. 5).” Davis’s Chapter 12 was titled “Information System Support for Decision Making” and Chapter 13 was titled “Information System Support for Planning and Control”. Davis’s framework incorporated computerized decision support systems into the emerging field of management information systems.
Peter Keen and Charles Stabell claim the concept of decision support systems evolved from “the theoretical studies of organizational decisionmaking done at the Carnegie Institute of Technology during the late 1950s and early ’60s and the technical work on interactive computer systems, mainly carried out at the Massachusetts Institute of Technology in the 1960s. (Keen and Scott Morton, 1978)”. Herbert Simon’s books (1947, 1960) and articles provide a context for understanding and supporting decision making.
In 1995, Hans Klein and Leif Methlie noted “A study of the origin of DSS has still to be written. It seems that the first DSS papers were published by PhD students or professors in business schools, who had access to the first time-sharing computer system: Project MAC at the Sloan School, the Dartmouth Time Sharing Systems at the Tuck School. In France, HEC was the first French business school to have a time-sharing system (installed in 1967), and the first DSS papers were published by professors of the School in 1970. (p. 112).”
In s nutshell developments in DSS began with building model-driven DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems, spreadsheet DSS and Group DSS in the early and mid 80s. Data warehouses, Executive Information Systems, OLAP and Business Intelligence evolved in the late 1980s and early 1990s. Finally the latest one is knowledge-driven DSS and the implementation of Web-based DSS in the mid-1990s.
In the mid- to late 1970s, both practice and theory issues related to DSS were discussed at academic conferences including the American Institute for Decision Sciences meetings and the ACM SIGBDP Conference on Decision Support Systems in San Jose, CA in January 1977 (the proceeding were included in the journal Database). The first International Conference on Decision Support Systems was held in Atlanta, Georgiain 1981. Academic conferences provided forums for idea sharing, theory discussions and information exchange.
At about this same time, Keen and Scott Morton’s DSS textbook (1978) provided the first broad behavioral orientation to decision support system analysis, design, implementation, evaluation and development. This influential text provided a framework for teaching DSS in business schools. McCosh and Scott-Morton’s (1978) DSS book was more influential in Europe.
In 1980, Steven Alter published his MIT doctoral dissertation results in an influential book. Alter’s research and papers (1975; 1977) expanded the framework for thinking about business and management DSS. Also, his case studies provided a firm descriptive foundation of decision support system examples. A number of other MIT dissertations completed in the late 1970s also dealt with issues related to using models for decision support.
Alter concluded from his research (1980) that decision support systems could be categorized in terms of the generic operations that can be performed by such systems. These generic operations extend along a single dimension, ranging from extremely data-oriented to extremely model-oriented. Alter conducted a field study of 56 DSS that he categorized into seven distinct types of DSS. His seven types include:
File drawer systems that provide access to data items.
Data analysis systems that support the manipulation of data by computerized tools tailored to a specific task and setting or by more general tools and operators.
Analysis information systems that provide access to a series of decision-oriented databases and small models.
Accounting and financial models that calculate the consequences of possible actions.
Representational models that estimate the consequences of actions on the basis of simulation models.
Optimization models that provide guidelines for action by generating an optimal solution consistent with a series of constraints.
Suggestion models that perform the logical processing leading to a specific suggested decision for a fairly structured or well-understood task.
Types Of DSS
1 Model-driven DSS
Model-driven support systems incorporate the ability to manipulate data to generate statistical and financial reports, as well as simulation models, to aid decision-makers. Model-based decision support systems can be extremely useful in forecasting the effects of changes in business processes, as they can use past data to answer complex ‘what-if’ questions for decision makers.
A model-driven DSS emphasizes access to and manipulation of financial, optimization and/or simulation models. Simple quantitative models provide the most elementary level of functionality. Model-driven DSS use limited data and parameters provided by decision makers to aid decision makers in analyzing a situation, but in general large data bases are not needed for model-driven DSS.
Scott-Morton’s (1971) production planning management decision system was the first widely discussed model-driven DSS, but Ferguson and Jones’ (1969) production scheduling application was also a model-driven DSS.
2 Data-driven DSS
Data-driven DSS are a form of support system that focuses on the provision of internal (and sometimes external) data to aid decision making. Most often this will come in the form of a data warehouse – a database designed to store data in such a way as to allow for its querying and analysis by users.
One of the first data-driven DSS was built using an APL-based software package called AAIMS, An Analytical Information Management System. It was developed from 1970-1974 by Richard Klaas and Charles Weiss at American Airlines
In general, a data-driven DSS emphasizes access to and manipulation of a time-series of internal company data and sometimes external and real-time data. Simple file systems accessed by query and retrieval tools provide the most elementary level of functionality. Data warehouse systems that allow the manipulation of data by computerized tools tailored to a specific task and setting or by more general tools and operators provide additional functionality.
Another example of a data-driven DSS would be a Geographic Information System(GIS), which can be used to visually represent geographically dependant data using maps.
3 Communications-driven DSS
Communication-Driven DSS is a type of DSS that enhances decision-making by enabling communication and sharing of information between groups of people. At its most basic level a C-D DSS could be a simple threaded e-mail. At its most complex it could be a web-conferencing application or interactive video.
Communications-driven DSS use network and communications technologies to facilitate decision-relevant collaboration and communication. In these systems, communication technologies are the dominant architectural component.
In general, groupware, bulletin boards, audio and videoconferencing are the primary technologies for communications-driven decision support. In the past few years, voice and video delivered using the Internet protocol have greatly expanded the possibilities for synchronous communications-driven DSS.
4 Document-driven DSS
A document-driven DSS uses computer storage and processing technologies to provide document retrieval and analysis. Large document databases may include scanned documents, hypertext documents, images, sounds and video. Examples of documents that might be accessed by a document-driven DSS are policies and procedures, product specifications, catalogs, and corporate historical documents, including minutes of meetings and correspondence.
Document-driven DSS are support systems designed to convert documents into valuable business data. While data-driven DSS rely on data that is already in a standardised format that lends itself to database storage and analysis, document-driven DSS makes use of data that cannot easily be standardised and stored.
The three primary forms of data used in document driven DSS are:
None of these formats lend themselves easily to standardised database storage and analysis, so managers require DSS tools to convert them into data that can be valuable in the decision making process.
Document-driven DSS is the newest field of study in Decision Support Systems. Examples of document-driven tools can be found in Internet search engines, designed to sift through vast volumes of unsorted data through the use of keyword searches.
5 Knowledge-driven DSS
Knowledge-driven DSS can suggest or recommend actions to managers. These DSS are person-computer systems with specialized problem-solving expertise. The “expertise” consists of knowledge about a particular domain, understanding of problems within that domain, and “skill” at solving some of these problems.
In short a Web-based decision support system as a computerized system that delivers decision support information or decision support tools to a manager or business analyst using a “thin-client” Web browser like Netscape Navigator or Internet Explorer. The computer server that is hosting the DSS application is linked to the user’s computer by a network with the TCP/IP protocol.
ADVANTAGES OF DSS
Time savings. For all categories of decision support systems, research has demonstrated and substantiated reduced decision cycle time, increased employee productivity and more timely information for decision making. The time savings that have been documented from using computerized decision support are often substantial.
Enhance effectiveness. A second category of advantage that has been widely discussed and examined is improved decision making effectiveness and better decisions. Decision quality and decision making effectiveness are however hard to document and measure.
Improve interpersonal communication. DSS can improve communication and collaboration among decision makers. In appropriate circumstances, communications-driven and group DSS have had this impact. Model-driven DSS provide a means for sharing facts and assumptions. Data-driven DSS make “one version of the truth” about company operations available to managers and hence can encourage fact-based decision making. Improved data accessibility is often a major motivation for building a data-driven DSS. This advantage has not been adequately demonstrated for most types of DSS.
Competitive advantage. Vendors frequently cite this advantage for business intelligence systems, performance management systems, and web-based DSS. Although it is possible to gain a competitive advantage from computerized decision support, this is not a likely outcome.
Cost reduction. Some research and especially case studies have documented DSS cost saving from labor savings in making decisions and from lower infrastructure or technology costs. This is not always a goal of building DSS.
Increase decision maker satisfaction. The novelty of using computers has and may continue to confound analysis of this outcome. DSS may reduce frustrations of decision makers, create perceptions that better information is being used and/or create perceptions that the individual is a “better” decision maker.
Promote learning. Learning can occur as a by-product of initial and ongoing use of a DSS. Two types of learning seem to occur: learning of new concepts and the development of a better factual understanding of the business and decision making environment.
Increase organizational control. Data-driven DSS often make business transaction data available for performance monitoring and ad hoc querying. Such systems can enhance management understanding of business operations and managers perceive that this is useful.
DISADVANTAGES OF DSS
Overemphasize decision making. Clearly the focus of those of us interested in computerized decision support is on decisions and decision making. Implementing DSS may reinforce the rational perspective and overemphasize decision processes and decision making. It is important to educate managers about the broader context of decision making and the social, political and emotional factors that impact organizational success. It is especially important to continue examining when and under what circumstances DSS should be built and used. We must continue to ask if the decision situation is appropriate for using any type of DSS and if a specific DSS is or remains appropriate to use for making or informing a specific decision.
Assumption of relevance. According to Winograd and Flores (1986), “Once a computer system has been installed it is difficult to avoid the assumption that the things it can deal with are the most relevant things for the manager’s concern.” The danger is that once DSS become common in organizations, that managers will use them inappropriately.
Unanticipated effects. Implementing decision support technologies may have unanticipated consequences. It is conceivable and it has been demonstrated that some DSS reduce the skill needed to perform a decision task. Some DSS overload decision makers with information and actually reduce decision making effectiveness. I’m sure other such unintended consequences have been documented. Nevertheless, most of the examples seem correctable, avoidable or subject to remedy if and when they occur.
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Obscuring responsibility. The computer doesn’t make a “bad” decision, people do. Unfortunately some people may deflect personal responsibility to a DSS. Managers need to be continually reminded that the computerized decision support system is an intermediary between the people who built the system and the people who use the system. The entire responsibility associated with making a decision using a DSS resides with people who built and use the system.
False belief in objectivity. Managers who use DSS may or may not be more objective in their decision making. Computer software can encourage more rational action, but managers can also use decision support technologies to rationalize their actions. It is an overstatement to suggest that people using a DSS are more objective and rational than managers who are not using computerized decision support.
Status reduction. Some managers argue using a DSS will diminish their status and force them to do clerical work. This perceptual problem can be a disadvantage of implementing a DSS. Managers and IS staff who advocate building and using computerized decision support need to deal with any status issues that may arise.
Information overload. Too much information is a major problem for people and many DSS increase the information load. Although this can be a problem, DSS can help managers organize and use information. DSS can actually reduce and manage the information load of a user. DSS developers need to try to measure the information load created by the system and DSS users need to monitor their perceptions of how much information they are receiving.
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