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Decision support systems are a class of computer-based information systems or knowledge based systems that, in very different manner, support decision making activities.
Definitions Owing to the lack of a single generic model of decision-making and to the wide range of domains in which decisions are made, the concept of decision support system (DSS) is very broad. A DSS can take many different forms. Minimally we can say that a DSS is a system for making decisions. A decision is a choice between alternatives based on estimates of the values of those alternatives. Supporting a decision means supporting this choice by supporting the estimation, the evaluation and/or the comparison and choice. In practice references to DSS are usually references to computer applications that perform such a supporting role. The term decision support system has been used in many different ways (Alter 1980) and has received some very different definitions according to the author's point of view (Druzdzel and Flynn 1999). Finlay (1994) and others define a DSS rather broadly as "a computer-based system that aids the process of decision making." Turban (1995) defines it more specifically as "an interactive, flexible, and adaptable computer-based information system, especially developed for supporting the solution of a non-structured management problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows for the decision maker's own insights." Other definitions fall between these two extremes. For Keen and Scott Morton (1978), a DSS couples the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions ("DSS are computer-based support for management decision makers who are dealing with semi-structured problems"). For Sprague and Carlson (1982), DSS are "interactive computer-based systems that help decision makers utilize data and models to solve unstructured problems." In contrast, Keen (1980) claims that it is impossible to give a precise definition including all the facets of the DSS ("there can be no definition of decision support systems, only of decision support"). Nevertheless, according to Power (1997), the term decision support system remains a useful and inclusive term for many types of information systems that support decision making. He humorously adds that every time a computerized system is not an on-line transaction processing system (OLTP), someone will be tempted to call it a DSS. As you can see, there is no universally accepted definition of DSS. Recommended reading: Druzdzel and Flynn (1999), Power (2000), Sprague and Watson (1993), the first chapter of Power (2002), the first chapter of Makaras (1999), the first chapter of Silver (1991), the first two chapters of Sauter (1997), and Holsaple and Whinston (1996). A brief history In the absence of an all-inclusive definition, we focus on the history of DSS (see also Power, 2003). According to Keen and Scott Morton (1978), the concept of decision support has evolved from two main areas of research: the theoretical studies of organizational decision making done at the Carnegie Institute of Technology during the late 1950s and early 1960s, and the technical work on interactive computer systems, mainly carried out at the Massachusetts Institute of Technology in the 1960s. It is considered that the concept of 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. 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. It is clear that DSS belong to an environment with multidisciplinary foundations, including (but not exclusively) database research, artificial intelligence, human-computer interaction, simulation methods, software engineering, and telecommunications. 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 helping decision makers in particular. Taxonomies As with the definition, there is no universally accepted taxonomy of DSS either. Different authors propose different classifications. Using the relationship with the user as the criterion, Hättenschwiler (1999) 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 the decision maker (or its advisor) to modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation. The system again improves, completes, and refines the suggestions of the decision maker and sends them back to her for validation. The whole process then starts again, until a consolidated solution is generated. Using the mode of assistance as the criterion, Power (2002) differentiates communication-driven DSS, data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS. Using the scope as the criterion, Power (1997) 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 systems that runs on an individual manager's PC. Architectures Once again, different authors identify different components in a DSS. Sprague and Carlson (1982) identify three fundamental components of DSS: (a) the database management system (DBMS), (b) the model-base management system (MBMS), and (c) the dialog generation and management system (DGMS). Haag et al. (2000) describe these three components in more detail: the Data Management Component stores information (which can be further subdivided into that derived from an organization's traditional data repositories, from external sources such as the Internet, or from the personal insights and experiences of individual users); the Model Management Component handles representations of events, facts, or situations (using various kinds of models, two examples being optimization models and goal-seeking models); and the User Interface Management Component is of course the component that allows a user to interact with the system. According to Power (2002), academics and practitioners have discussed building DSS in terms of four major components: (a) the user interface, (b) the database, (c) the model and analytical tools, and (d) the DSS architecture and network. Hättenschwiler (1999) identifies five components of DSS: (a) users with different roles or functions in the decision making process (decision maker, advisors, domain experts, system experts, data collectors), (b) a specific and definable decision context, (c) a target system describing the majority of the preferences, (d) a knowledge base made of external data sources, knowledge databases, working databases, data warehouses and meta-databases, mathematical models and methods, procedures, inference and search engines, administrative programs, and reporting systems, and (e) a working environment for the preparation, analysis, and documentation of decision alternatives. Marakas (1999) proposes a generalized architecture made of five distinct parts: (a) the data management system, (b) the model management system, (c) the knowledge engine, (d) the user interface, and (e) the user(s). There are several ways to classify DSS applications. Not every DSS fits neatly into one category, but a mix of two or more architecture in one. Holsapple and Whinston (1996) 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 described by Holsapple and Whinston (1996). The support given by DSS can be separated into three distinct. interrelated categories (Hackathorn and Keen, 1981): Personal Support, Group Support and Organizational Support. Additionally, the build up of a DSS is also classified into a few characteristics. 1) inputs: this is used so the DSS can have factors, numbers, and characteristics to analyze. 2) user knowledge and expertise: This allows the system to decide how much it is relied on, and exactly what inputs must be analyzed with or without the user. 3) outputs: This is used so the user of the system can analyze the decisions that may be made and then potentially 4) make a decision: This decision making is made by the DSS, however, it is ultimately made by the user in order to decide on which criteria it should use. 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), e.g. see Gadomski et al.(2001). Applications As mentioned above, there are theoretical possibilities of building such systems in any knowledge domain. One of the examples is Clinical decision support system for medical diagnosis. 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 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, CN managed to decrease the incidence of derailments at the same time other companies were experiencing an increase. DSS has many applications that have already been spoken about. However, it can be used in any field where organization is necessary. Additionally, a DSS can be designed to help make decisions on the stock market, or deciding which area or segment to market a product toward. See also | ||||||||
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