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decision support system

(di′sizh·ən sə′pört ′sis·təm)

(computer science) A computer-based system that enables management to interrogate the computer system on an ad hoc basis for various kinds of information on the organization and to predict the effect of potential decisions beforehand. Abbreviated DSS.

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Dictionary: DSS

abbr.

Department of Social Services
digital satellite system

Sci-Tech Encyclopedia: Decision support system

A system that supports technological and managerial decision making by assisting in the organization of knowledge about ill-structured, semistructured, or unstructured issues. A structured issue has a framework comprising elements and relations between them that are known and understood. Structured issues are generally ones about which an individual has considerable experiential familiarity. A decision support system (DSS) is not intended to provide support to humans about structured issues since little cognitively based decision support is generally needed.

Emphasis in the use of a decision support system is upon provision of support to decision makers in terms of increasing the effectiveness of the decision-making effort. This support involves the systems engineering steps of formulation of alternatives, the analysis of their impacts, and interpretation and selection of appropriate options for implementations. See also Systems engineering.

Decisions may be described as structured or unstructured, depending upon whether or not the decision-making process can be explicitly described prior to its execution. Generally, operational performance decisions are more likely than strategic planning decisions to be prestructured. Thus, expert systems are usually more appropriate for operational performance and operational control decisions, while decision support systems are more appropriate for strategic planning and management control. See also Expert systems.

The primary components of a decision support system are a database management system (DBMS), a model-base management system (MBMS), and a dialog generation and management system (DGMS). An appropriate database management system must be able to work with both data that are internal to the organization and data that are external to it. Model-base management systems provide sophisticated analysis and interpretation capability. The dialog generation and management system is designed to satisfy knowledge representation, and control and interface requirements. See also Decision theory.

Accounting Dictionary: Decision Support System (DSS)

Branch of the broadly defined Management Information System (MIS) that provides answers to problems and that integrates the decision maker into the system as a component. The system utilizes such quantitative techniques as regression, linear programming, and financial planning modeling. DSS software furnishes support to the accountant in the decision-making process. It analyzes a specific situation and can be modified as the practitioner wishes. Models are constructed and decisions analyzed. Planning and forecasting are facilitated.

Small Business Encyclopedia: Decision Support Systems

Broadly speaking, decision support systems are a set of manual or computer-based tools that assist in some decision-making activity. In today's business environment, however, decision support systems (DSS) are commonly understood to be computerized management information systems designed to help business owners, executives, and managers resolve complicated business problems and/or questions. Good decision support systems can help business people perform a wide variety of functions, including cash flow analysis, concept ranking, multistage fore-casting, product performance improvement, and resource allocation analysis. Previously regarded as primarily a tool for big companies, DSS has in recent years come to be recognized as a potentially valuable tool for small business enterprises as well.

The Structure of Decisions

In order to discuss the support of decisions and what DSS tools can or should do, it is necessary to have a perspective on the nature of the decision process and the various requirements of supporting it. One way of looking at a decision is in terms of its key components. The first component is the data collected by a decision maker to be used in making the decision. The second component is the process selected by the decision maker to combine this data. Finally, there is an evaluation or learning component that compares decisions and examines them to see if there is a need to change either the data being used or the process that combines the data. These components of a decision interact with the characteristics of the decision that is being made.

STRUCTURED DECISIONS. Many analysts categorize decisions according to the degree of structure involved in the decision-making activity. Business analysts describe a structured decision as one in which all three components of a decision—the data, process, and evaluation—are determined. Since structured decisions are made on a regular basis in business environments, it makes sense to place a comparatively rigid framework around the decision and the people making it.

Structured decision support systems may simply use a checklist or form to ensure that all necessary data is collected and that the decision making process is not skewed by the absence of necessary data. If the choice is also to support the procedural or process component of the decision, then it is quite possible to develop a program either as part of the checklist or form. In fact, it is also possible and desirable to develop computer programs that collect and combine the data, thus giving the process a high degree of consistency or structure. When there is a desire to make a decision more structured, the support system for that decision is designed to ensure consistency. Many firms that hire individuals without a great deal of experience provide them with detailed guidelines on their decision making activities and support them by giving them little flexibility. One interesting consequence of making a decision more structured is that the liability for inappropriate decisions is shifted from individual decision makers to the larger company or organization.

UNSTRUCTURED DECISIONS. At the other end of the continuum are unstructured decisions. While these decisions have the same components as structured ones—data, process, and evaluation—there is little agreement on their nature. With unstructured decisions, for example, each decision maker may use different data and processes to reach a conclusion. In addition, because of the nature of the decision there may only a limited number of people within the organization that are even qualified to evaluate the decision.

Generally, unstructured decisions are made in instances in which all elements of the business environment—customer expectations, competitor response, cost of securing raw materials, etc.—are not completely understood (new product and marketing strategy decisions commonly fit into this category). Unstructured decision systems typically focus on the individual or team that will make the decision. These decision makers are usually entrusted with decisions that are unstructured because of their experience or expertise, and therefore it is their individual ability that is of value. One approach to support systems in this area is to construct a program that simulates the process used by a particular individual. In essence, these systems—commonly referred to as "expert systems"—prompt the user with a series of questions regarding a decision situation. "Once the expert system has sufficient information about the decision scenario, it uses an inference engine which draws upon a data base of expertise in this decision area to provide the manager with the best possible alternative for the problem," explained Jatinder N.D. Gupta and Thomas M. Harris in the Journal of Systems Management. " The purported advantage of this decision aid is that it allows the manager the use of the collective knowledge of experts in this decision realm. Some of the current DSS applications have included long-range and strategic planning policy setting, new product planning, market planning, cash flow management, operational planning and budgeting, and portfolio management."

Another approach is to monitor and document the process that was used so that the decision maker(s) can readily review what has already been examined and concluded. An even more novel approach used to support these decisions is to provide environments that are specially designed to give these decision makers an atmosphere that is conducive to their particular tastes. The key to support of unstructured decisions is to understand the role that individuals experience or expertise plays in the decision and to allow for individual approaches.

SEMI-STRUCTURED DECISIONS. In the middle of the continuum are semi-structured decisions, and this is where most of what are considered to be true decision support systems are focused. Decisions of this type are characterized as having some agreement on the data, process, and/or evaluation to be used, but are also typified by efforts to retain some level of human judgement in the decision making process. An initial step in analyzing which support system is required is to understand where the limitations of the decision maker may be manifested (i.e., the data acquisition portion, the process component, or the evaluation of outcomes).

Grappling with the latter two types of decisions—unstructured and semi-structured—can be particularly problematic for small businesses, which often have limited technological or work force resources. As Gupta and Harris indicated, "many decision situations faced by executives in small business are one-of-a-kind, one-shot occurrences requiring specifically tailored solution approaches without the benefit of any previously available rules or procedures. This unstructured or semi-structured nature of these decisions situations aggravates the problem of limited resources and staff expertise available to a small business executive to analyze important decisions appropriately. Faced with this difficulty, an executive in a small business must seek tools and techniques that do not demand too much of his time and resources and are useful to make his life easier." Subsequently, small businesses have increasingly turned to DSS to provide them with assistance in business guidance and management.

Key Dss Functions

Gupta and Harris observed that DSS is predicated on the effective performance of three functions: information management, data quantification, and model manipulation: "Information management refers to the storage, retrieval, and reporting of information in a structured format convenient to the user. Data quantification is the process by which large amounts of information are condensed and analytically manipulated into a few core indicators that extract the essence of data. Model manipulation refers to the construction and resolution of various scenarios to answer 'what if' questions. It includes the processes of model formulation, alternatives generation and solution of the proposed models, often through the use of several operations research/management science approaches."

Entrepreneurs and owners of established enterprises are urged to make certain that their business needs a DSS before buying the various computer systems and software necessary to create one. Some small businesses, of course, have no need of a DSS. The owner of a car washing establishment, for instance, would be highly unlikely to make such an investment. But for those business owners who are guiding a complex operation, a decision support system can be a valuable tool. Another key consideration is whether the business's key personnel will ensure that the necessary time and effort is spent to incorporate DSS into the establishment's operations. After all, even the best decision support system is of little use if the business does not possess the training and knowledge necessary to use it effectively. If, after careful study of questions of DSS utility, the small business owner decides that DSS can help his or her company, the necessary investment can be made, and the key managers of the business can begin the process of developing their own DSS applications using available spreadsheet software.

Dss Uncertainties and Limitations

While decision support systems have been embraced by small business operators in a wide range of industries in recent years, entrepreneurs, programmers, and business consultants all agree that such systems are not perfect.

LEVEL OF "USER-FRIENDLINESS". Some observers contend that although decision support systems have become much more user-friendly in recent years, it remains an issue, especially for small business operations that do not have significant resources in terms of technological knowledge.

HARD-TO-QUANTIFY FACTORS. Another limitation that decision makers confront has to do with combining or processing the information that they obtain. In many cases these limitations are due to the number of mathematical calculations required. For instance, a manufacturer pondering the introduction of a new product can not do so without first deciding on a price for the product. In order to make this decision, the effect of different variables (including price) on demand for the product and the subsequent profit must be evaluated. The manufacturer's perceptions of the demand for the product can be captured in a mathematical formula that portrays the relationship between profit, price, and other variables considered important. Once the relationships have been expressed, the decision maker may now want to change the values for different variables and see what the effect on profits would be. The ability to save mathematical relationships and then obtain results for different values is a feature of many decision support systems. This is called "what-if" analysis, and today's spreadsheet software packages are fully equipped to support this decision-making activity. Of course, additional factors must be taken into consideration as well when making business decisions. Hard-to-quantify factors such as future interest rates, new legislation, and hunches about product shelf life may all be considered. So even though the calculations may indicate that a certain demand for the product will be achieved at a certain price, the decision maker must use his or her judgment in making the final decision.

If the decision maker simply follows the output of a process model, then the decision is being moved toward the structured end of the continuum. In certain corporate environments, it may be easier for the decision maker to follow the prescriptions of the DSS; users of support systems are usually aware of the risks associated with certain choices. If decision makers feel that there is more risk associated with exercising judgment and opposing the suggestion of the DSS than there is in simply supporting the process, the DSS is moving the decision more toward the structured end of the spectrum. Therefore, the way in which a DSS will be used must be considered within the decision-making environment.

PROCESSING MODEL LIMITATIONS. Another problem with the use of support systems that perform calculations is that the user/decision maker may not be fully aware of the limitations or assumptions of the particular processing model. There may be instances in which the decision maker has an idea of the knowledge that is desired, but not necessarily the best way to get that knowledge. This problem may be seen in the use of statistical analysis to support a decision. Most statistical packages provide a variety of tests and will perform them on whatever data is presented, regardless of whether or not it is appropriate. This problem has been recognized by designers of support systems and has resulted in the development of DSS that support the choice of the type of analysis.

Definitions

Because there are many approaches to decision-making and because of 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. In general, we can say that a DSS is a computerized system for helping make decisions. A decision is a choice between alternatives based on estimates of the values of those alternatives. Supporting a decision means helping people working alone or in a group gather intelligence, generate alternatives and make choices. Supporting the choice making process involves supporting the estimation, the evaluation and/or the comparison of alternatives. In practice, references to DSS are usually references to computer applications that perform such a supporting role.^[1]

The term decision support system has been used in many different ways (Alter 1980, Power, 2002) and has been defined in various ways depending upon the author's point of view ^[2]. Finlay ^[3] and others define a DSS rather broadly as "a computer-based system that aids the process of decision making." Turban ^[4] 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 Little ^[5], a DSS is a "model-based set of procedures for processing data and judgments to assist a manager in his decision-making." For Keen and Scott Morton ^[6], 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"). Moore and Chang ^[7] define DSS as extendible systems capable of supporting ad hoc data analysis and decision modeling, oriented toward future planning, and used at irregular, unplanned intervals. For Sprague and Carlson ^[8], DSS are "interactive computer-based systems that help decision makers utilize data and models to solve unstructured problems." In contrast, Keen ^[9] 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 ^[10], 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. ^[11]

Recommended reading: Druzdzel and Flynn (1999), Power (2000), Sprague and Watson (1993), the first chapter of Power (2002), the first chapter of Marakas (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^[11]). According to Keen and Scott Morton ^[6], 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, Haettenschwiler ^[12] 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 ^[13] differentiates communication-driven DSS, data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS.

A model-driven DSS emphasizes access to and manipulation of 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 ^[14].
A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or Groove ^[15].
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 information in a variety of electronic formats.
A knowledge-driven DSS provides specialized problem solving expertise stored as facts, rules, procedures, or in similar structures.^[13]

Using scope as 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 systems that runs on an individual manager's PC.

Architectures

Once again, different authors identify different components in a DSS. Sprague and Carlson ^[8] 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. ^[16] 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 ^[13], 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.

DSS Architecture

Architecture

The Database The database contains information about internal data and external data that will contribute to the decision making process. This data is in most cases more extensive than traditional relational models

The Model Base This module contains a set of algorithms that makes decisions based on the information in the database. This information is then summarized and displayed as tables or graphs.

The Interface This is what the user will use to interface with the system. This is complimented with an interactive help and navigation screen.

Framework DSS systems are not entirely different to other systems and require a structured approach. A framework was provided by Sprague and Watson (1993). The framework has three main levels. 1. Technology levels 2. People involved 3. The developmental approach

1. Technology Levels Sprague has suggested that there are three levels of hardware and software that has been proposed for DSS. a) Level 1 – Specific DSS This is the actual application that will be used to by the user. This is the part of the application that allows the decision maker to make decisions in a particular problem area. b) Level 2 – DSS Generator This level contains Hardware/software environment that allows people to easily develop specific DSS applications. This level makes use of case tools or systems like Crystal c) Level 3 – DSS Tools

Contains lower level hardware/software. DSS generators including special languages, function libraries and linking modules

2. People Involved Sprague suggests there are 5 roles involved in a typical DSS development cycle. A) The end user. B) An intermediary. C) DSS developer D) Technical supporter E) Systems Expert

3. Developmental The developmental approach for a DSS system should be strongly iterative. This will allow for the application to be changed and redesigned at various intervals. The initial problem is used to design the system on and then tested and revised to ensure the desired outcome is achieved.

NCC Education Limited - Management Support Systems

Hättenschwiler ^[12] 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 ^[17] 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 ^[18] 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 ^[18].

The support given by DSS can be separated into three distinct, interrelated categories ^[19]: 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).

Applications

As mentioned above, there are theoretical possibilities of building such systems in any knowledge domain.

Some 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.

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.

A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. For example, the DSSAT4 package^[20]^[21], developed through financial support of USAID during the 80's and 90's, has allowed rapid assessment of several agricultural production systems around the world to facilitate decision-making at the farm and policy levels. There are, however, many constraints to the successful adoption on DSS in agriculture^[22].

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.

DSS Architecture

Architecture

The Model Base This module contains a set of algorithms that makes decisions based on the information in the database. This information is then summarized and displayed as tables or graphs.

The Interface This is what the user will use to interface with the system. This is complimented with an interactive help and navigation screen.

Contains lower level hardware/software. DSS generators including special languages, function libraries and linking modules

2. People Involved Sprague suggests there are 5 roles involved in a typical DSS development cycle. A) The end user. B) An intermediary. C) DSS developer D) Technical supporter E) Systems Expert

NCC Education Limited - Management Support Systems

Characteristics and Capabilities of DSS

Because there is no exact definition of DSS, there is obviously no agreement on the standard characteristics and capabilities of DSS. Turban, E.,Aronson, J.E., and Liang, T.P. ^[23] constitute an ideal set of characteristics and capabilities of DSS. The key DSS characteristics and capabilities are as follows:

Support for decision makers in semistructured and unstructured problems.
Support managers at all levels.
Support individuals and groups.
Support for interdependent or sequential decisions.
Support intelligence, design, choice, and implementation.
Support variety of decision processes and styles.
DSS should be adaptable and flexible.
DSS should be interactive and provide ease of use.
Effectiveness balanced with efficiency (benefit must exceed cost).
Complete control by decision-makers.
Ease of development by (modification to suit needs and changing environment) end users.
Support modeling and analysis.
Data access.
Standalone, integration and Web-based.

References

^ Alter, S. L. (1980). Decision support systems: current practice and continuing challenges. Reading, Mass., Addison-Wesley Pub.
^ Druzdzel, M. J. and R. R. Flynn (1999). Decision Support Systems. Encyclopedia of Library and Information Science. A. Kent, Marcel Dekker, Inc.
^ Finlay, P. N. (1994). Introducing decision support systems. Oxford, UK Cambridge, Mass., NCC Blackwell; Blackwell Publishers.
^ Turban, E. (1995). Decision support and expert systems: management support systems. Englewood Cliffs, N.J., Prentice Hall. ISBN 0-024-21702-6
^ Little, J.D.C.(1970, April). "Models and Managers:The Concept of a Decision Calculus." Management Science, Vol.16,NO.8
^ ^a ^b Keen, P. G. W. (1978). Decision support systems: an organizational perspective. Reading, Mass., Addison-Wesley Pub. Co. ISBN 0-201-03667-3
^ Moore, J.H.,and M.G.Chang.(1980,Fall)."Design of Decision Support Systems." Data Base,Vol.12, Nos.1 and 2.
^ ^a ^b Sprague, R. H. and E. D. Carlson (1982). Building effective decision support systems. Englewood Cliffs, N.J., Prentice-Hall. ISBN 0-130-86215-0
^ Keen, P. G. W. (1980). Decision support systems: a research perspective. Decision support systems : issues and challenges. G. Fick and R. H. Sprague. Oxford ; New York, Pergamon Press.
^ ^a ^b Power, D. J. (1997). What is a DSS? The On-Line Executive Journal for Data-Intensive Decision Support 1(3).
^ ^a ^b Power, D.J. A Brief History of Decision Support Systems DSSResources.COM, World Wide Web, version 2.8, May 31, 2003.
^ ^a ^b 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.
^ Gachet, A. (2004). Building Model-Driven Decision Support Systems with Dicodess. Zurich, VDF.
^ Stanhope, P. (2002). Get in the Groove: building tools and peer-to-peer solutions with the Groove platform. New York, Hungry Minds
^ Haag, Cummings, McCubbrey, Pinsonneault, Donovan (2000). Management Information Systems: For The Information Age. McGraw-Hill Ryerson Limited: 136-140. ISBN 0-072-81947-2
^ Marakas, G. M. (1999). Decision support systems in the twenty-first century. Upper Saddle River, N.J., Prentice Hall.
^ ^a ^b Holsapple, C.W., and A. B. Whinston. (1996). Decision Support Systems: A Knowledge-Based Approach. St. Paul: West Publishing. 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.
^ DSSAT4 (pdf)
^ The Decision Support System for Agrotechnology Transfer
^ 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.
^ Turban, E.,Aronson, J.E., and Liang, T.P.(2005). Decision Support Systems and Intelligent Systems. New Jersey, Pearson Education, Inc.

References not yet tagged in text

Delic, K.A., Douillet,L. and Dayal, U. (2001) "Towards an architecture for real-time decision support systems:challenges and solutions.
Gadomski, A.M. at al.(2001) "An Approach to the Intelligent Decision Advisor (IDA) for Emergency Managers.Int. J. Risk Assessment and Management, Vol. 2, Nos. 3/4.
Gomes da Silva, Carlos; Clímaco, João; Figueira, José. European Journal of Operational Research.
Ender, Gabriela (2005-2007) E-Book about the OpenSpace-Online® Real-Time Methodology: Knowledge-sharing, problem solving and results-oriented group dialogs in real-time about topics that matter. Download http://www.openspace-online.com/OpenSpace-Online_eBook_en.pdf
Jiménez, Antonio; Ríos-Insua, Sixto; Mateos, Alfonso. Computers & Operations Research.
Jintrawet, Attachai (1995). A Decision Support System for Rapid Assessment of Lowland Rice-based Cropping Alternatives in Thailand. Agricultural Systems 47: 245-258.
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. Decision Support Systems., Nov2005, Vol. 41 Issue 1, p1-19, 19p.
Sauter, V. L. (1997). Decision support systems: an applied managerial approach. New York, John Wiley.
Silver, M. (1991). Systems that support decision makers: description and analysis. Chichester ; New York, Wiley.
Sprague, R. H. and H. J. Watson (1993). Decision support systems: putting theory into practice. Englewood Clifts, N.J., Prentice Hall.

External links

Department of Social and Decision Sciences - Carnegie Mellon University
Elsevier DSS Publications
DSSAT4 - the University of Hawaii

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Meaning	Category
Data Storage System	Computing->General
Decision Support System	Governmental->Military Business->Accounting Governmental->Transportation
Defense Security Service	Governmental->US Government
Defense Supply Service	Governmental->Military
Department Of Social Services	Governmental->State & Local
Department for Safety and Security	Governmental->United Nations
Department of Social Security	Governmental->US Government Governmental->Police
Desk Synchronization Stand	Business->Products
Deskpack Software System	Computing->Software
Destructive, Snoopy, And Superfluous	Computing->Security Miscellaneous->Funnies
Digital Satellite Services	Regional->Time Zones
Digital Satellite System	Community->Media Computing->Drivers
Digital Signature Standard	Governmental->Military Computing->Networking
Digital Speech Standard	Academic & Science->Electronics
Digital Subscriber Signalling	Computing->Telecom
Diplomatic Security Service	Academic & Science->Electronics
Direct Situational Substitution	Governmental->Military
Direct Supply Support	Governmental->Military
Disability Status Scale	Governmental->US Government
Discotheque Sound System	Community
Dismounted Soldier System	Governmental->Military
Display stocker status	Academic & Science->Electronics
Distributed Systems And Simulation	Computing->General
Distribution Standard System	Governmental->Military
Dive Safety System	Academic & Science->Ocean Science
Dividend Settlement Service	Business->General
Docusate Sodium	Medical->Physiology
Don't Study Sociology	Academic & Science->Universities
Dual Slalom Specific	Community->Sports
Dubai Summer Surprises	Business->Firms Community
Dynamic Splash Screen	Computing->General
Quantum Corporation ( Data Storage Systems)	Business->NYSE Symbols
Screensaver file (DCC)	Computing->File Extensions
Sound (Digital Soup)	Computing->File Extensions

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