Decision support system

(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.

1. Department of Social Services
2. digital satellite 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.

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.

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.

Further Reading:

Carlson, John R., Dawn S. Carlson, and Lori L. Wadsworth. "On the Relationship Between DSS Design Characteristics and Ethical Decision Making." Journal of Managerial Issues. Summer 1999.

Chaudhry, Sohail S., Linda Salchenberger, and Mahdi Beheshtian. "A Small Business Inventory DSS: Design, Development, and Implementation Issues." Computers & Operations Research. January 1996.

Gupta, Jatinder N.D., and Thomas M. Harris. "Decision Support Systems for Small Business." Journal of Systems Management. February 1989.

Kimball, Ralph, and Kevin Strahlo. "Why Decision Support Fails and How to Fix It." Datamation. June 1, 1994.

Kumar, Ram L. "Understanding DSS Value." Omega. June 1999.

Laudon, Kenneth C., and Jane Price Laudon. Management Information Systems: A Contemporary Perspective. New York: Macmillan, 1991.

Muller-Boling, Detlef, and Susanne Kirchhoff. "Expert Systems for Decision Support in Business Start-Ups." Journal of Small Business Management. April 1991.

Parkinson, Chris. "What If? Decision Shaping Systems." CMA—The Management Accounting Magazine. March 1995.

Raggad, Bel G. "Decision Support System: Use It or Skip It." Industrial Management and Data Systems. January 1997.

Raymond, Louis, and Francois Bergeron. "Personal DSS Success in Small Enterprises." Information and Management. May 1992.

Example of a Decision Support System for John Day Reservoir.

Decision support systems constitute a class of computer-based information systems including knowledge-based systems that support decision-making activities.

Contents 1 Overview 2 History 3 Taxonomies 4 Architecture 4.1 Development Frameworks 5 Classifying DSS 6 Applications 7 Benefits of DSS 8 See also 9 References 10 Further reading

Overview

A Decision Support System (DSS) is a class of information systems (including but not limited to computerized systems) that support business and organizational decision-making activities. 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, or business models to identify and solve problems and make decisions.

Typical information that a decision support application might gather and present are:

• an inventory of all of your current information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),
• comparative sales figures between one week and the next,
• projected revenue figures based on new product sales assumptions.

History

According to Keen (1978)^[1], 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.^[1] 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.

According to Sol (1987)^[2] 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". Late 1970s the the DSS movement started focussing 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 end 1980s DSS faced a new challenge towards the design of intelligent workstations.^[2]

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.^[3][4]

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.

The advent of better 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.

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 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^[5] 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.

Another taxonomy for DSS has been created by Daniel Power. Using the mode of assistance as the criterion, Power differentiates communication-driven DSS, data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS.^[6]

• A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or Groove^[7]
• 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.^[6]
• 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^[8].

Using scope as the criterion, Power^[9] 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.

Architecture

Design of a Drought Mitigation Decision Support System.

Three fundamental components of a DSS architecture are:^{[5][6][10][11][12]}

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

The users themselves are also important components of the architecture.^[5][12]

Development Frameworks

DSS systems are not entirely different from other systems and require a structured approach. Such a framework includes people, technology, and the development approach.^[10]

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. This level makes use of case tools or systems such as Crystal, AIMMS, and iThink.
3. 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 for the desired outcome.

Classifying DSS

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^[13] 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^[13].

The support given by DSS can be separated into three distinct, interrelated categories^[14]: 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 by the user
3. Outputs: Transformed data from which DSS "decisions" are generated
4. Decisions: Results generated by 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)^[15].

The nascent field of Decision engineering 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

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

One example is the 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^[16][17], 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^[18].

DSS are also prevalent in forest management where the long planning time frame demands specific requirements. All aspects of Forest management, from log transportation, harvest scheduling to sustainability and ecosystem protection have been addressed by modern DSSs. A comprehensive list and discussion of all available systems in forest management is being compiled under the COST action Forsys

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.

CACI has begun integrating simulation and decision support systems. CACI defines three levels of simulation model maturity. “Level 1” models are traditional desktop simulation models that are executed within the native software package. These often require a simulation expert to implement modifications, run scenarios, and analyze results. “Level 2” models embed the modeling engine in a web application that allows the decision maker to make process and parameter changes without the assistance of an analyst. “Level 3” models are also embedded in a web-based application but are tied to real-time operational data. The execution of “level 3” models can be triggered automatically based on this real-time data and the corresponding results can be displayed on the manager’s desktop showing the prevailing trends and predictive analytics given the current processes and state of the system. The advantage of this approach is that “level 1” models developed for the FDA projects can migrate to “level 2 and 3” models in support of decision support, production/operations management, process/work flow management, and predictive analytics. This approach involves developing and maintaining reusable models that allow decision makers to easily define and extract business level information (e.g., process metrics). “Level 1” models are decomposed into their business objects and stored in a database. All process information is stored in the database, including activity, resource, and costing data. The database becomes a template library that users can access to build, change, and modify their own unique process flows and then use simulation to study their performance in an iterative manner.

Benefits of DSS

1. Improves personal efficiency
2. Expedites problem solving (speed up the progress of problems solving in an organization)
3. Facilitates interpersonal communication
4. Promotes learning or training
5. Increases organizational control
6. Generates new evidence in support of a decision
7. Creates a competitive advantage over competition
8. Encourages exploration and discovery on the part of the decision maker
9. Reveals new approaches to thinking about the problem space
10. Helps automate the managerial processes.

See also

• Decision theory
• Predictive analytics
• Land Allocation Decision Support System
• Online deliberation
• Self service software
• Spatial Decision Support System
• Morphological Analysis
• Clinical decision support system
• Decision engineering
• Model management

References

1. ^ ^{a b} Keen, P. G. W. (1978). Decision support systems: an organizational perspective. Reading, Mass., Addison-Wesley Pub. Co. ISBN 0-201-03667-3
2. ^ ^{a b} Henk G. Sol et al. (1987). Expert systems and artificial intelligence in decision support systems: proceedings of the Second Mini Euroconference, Lunteren, The Netherlands, 17-20 November, 1985. Springer, 1987. ISBN 9027724377. p.1-2.
3. ^ Efraim Turban, Jay E. Aronson, Ting-Peng Liang (2008). Decision Support Systems and Intelligent Systems. p. 574. 
4. ^ "Gate Delays at Airports Are Minimised for United by Texas Instruments' Explorer". Computer Business Review. 1987-11-26. http://www.cbronline.com/news/gate_delays_at_airports_are_minimised_for_united_by_texas_instruments_explorer. 
5. ^ ^{a b c} Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsunterstützung. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: 189-208.
6. ^ ^{a b c} Power, D. J. (2002). Decision support systems: concepts and resources for managers. Westport, Conn., Quorum Books.
7. ^ Stanhope, P. (2002). Get in the Groove: building tools and peer-to-peer solutions with the Groove platform. New York, Hungry Minds
8. ^ Gachet, A. (2004). Building Model-Driven Decision Support Systems with Dicodess. Zurich, VDF.
9. ^ Power, D. J. (1997). What is a DSS? The On-Line Executive Journal for Data-Intensive Decision Support 1(3).
10. ^ ^{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
11. ^ Haag, Cummings, McCubbrey, Pinsonneault, Donovan (2000). Management Information Systems: For The Information Age. McGraw-Hill Ryerson Limited: 136-140. ISBN 0-072-81947-2
12. ^ ^{a b} Marakas, G. M. (1999). Decision support systems in the twenty-first century. Upper Saddle River, N.J., Prentice Hall.
13. ^ ^{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
14. ^ 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.
15. ^ Gadomski A.M. et al. (1998). Integrated Parallel Bottom-up and Top-down Approach to the Development of Agent-based Intelligent DSSs for Emergency Management,TIEMS98, Washington, CiteSeerx - alfa:
16. ^ DSSAT4 (pdf)
17. ^ The Decision Support System for Agrotechnology Transfer
18. ^ 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.

Further reading

• Delic, K.A., Douillet,L. and Dayal, U. (2001) "Towards an architecture for real-time decision support systems:challenges and solutions.
• Gadomski, A.M. et 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; E-Book (2005-2008) 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 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.
• Matsatsinis, N.F. and Y. Siskos (2002), Intelligent support systems for marketing decisions, Kluwer Academic Publishers.
• 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.

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