Decision Support Systems Concept

2010 ◽  
pp. 1-5
Author(s):  
Daniel J. Power
Keyword(s):  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Support Systems ◽  
Broad Class ◽  
Decision Makers ◽  
Management Decision ◽  
Making Sense ◽  
Other Information ◽  
New Applications

Since the late 1960s, researchers have been developing and implementing computerized systems to support management decision makers. A number of decision support system (DSS) typologies were proposed in the early 1980s (Alter, 1980; Sprague & Carlson, 1982), but technology developments and new applications led to an expanded DSS framework (Power, 2000a, 2000b, 2001). The expanded DSS framework that is explained in detail in Power (2002b) helps decision makers and DSS developers understand and categorize decision support projects as well as existing decision support systems. Many terms are used to describe decision support systems. For example, some vendors and managers use the terms business intelligence, collaborative systems, computationally oriented DSS, data warehousing, model-based DSS, and online analytical processing (OLAP) software to label decision support systems. Software vendors use these more specialized terms for both descriptive and marketing purposes. The terms used to describe decision support capabilities are important in making sense about what technologies have been deployed or are needed. Some DSSs are subsystems of other information systems and this integration adds to the complexity of categorizing and identifying DSSs. In general, decision support systems are a broad class of information systems used to assist people in decision- making activities (Power, 2004).

Decision Support Systems Concept

2011 ◽  
pp. 232-235 ◽  
Author(s):  
Daniel J. Power
Keyword(s):  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Support Systems ◽  
Broad Class ◽  
Decision Makers ◽  
Management Decision ◽  
Making Sense ◽  
Other Information ◽  
New Applications

Since the late 1960s, researchers have been developing and implementing computerized systems to support management decision makers. A number of decision support system (DSS) typologies were proposed in the early 1980s (Alter, 1980; Sprague & Carlson, 1982), but technology developments and new applications led to an expanded DSS framework (Power, 2000a, 2000b, 2001). The expanded DSS framework that is explained in detail in Power (2002b) helps decision makers and DSS developers understand and categorize decision support projects as well as existing decision support systems. Many terms are used to describe decision support systems. For example, some vendors and managers use the terms business intelligence, collaborative systems, computationally oriented DSS, data warehousing, model-based DSS, and online analytical processing (OLAP) software to label decision support systems. Software vendors use these more specialized terms for both descriptive and marketing purposes. The terms used to describe decision support capabilities are important in making sense about what technologies have been deployed or are needed. Some DSSs are subsystems of other information systems and this integration adds to the complexity of categorizing and identifying DSSs. In general, decision support systems are a broad class of information systems used to assist people in decision- making activities (Power, 2004).

Decision Support Systems Concept

2005 ◽  
pp. 750-753
Author(s):  
Daniel J. Power
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Decision Makers ◽  
Management Decision ◽  
New Applications

Since the late 1960s, researchers have been developing and implementing computerized systems to support management decision makers. A number of decision support systems (DSS) typologies were proposed in the early 1980s (cf., Alter, 1980; Sprague & Carlson, 1982), but technology developments and new applications led to an expanded DSS framework (cf., Power, 2000a, 2000b, 2001). The expanded DSS framework developed in detail in Power (2002a) helps decision makers and DSS developers explain and categorize potential decision support projects as well as existing DSS.

DSS and Multiple Perspectives of Complex Problems

2011 ◽  
pp. 286-295 ◽  
Author(s):  
David Paradice ◽  
Robert A. Davis
Keyword(s):  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Support Systems ◽  
Collaborative Work ◽  
Vantage Point ◽  
Decision Makers ◽  
Complex Problems ◽  
Knowledge Based ◽  
Spreadsheet Models

Decision support systems have always had a goal of supporting decision-makers. Over time, DSS have taken many forms, or many forms of computer-based support have been considered in the context of DSS, depending on one’s particular perspective. Regardless, there have been decision support systems (DSS), expert systems, executive information systems, group DSS (GDSS), group support systems (GSS), collaborative systems (or computer-supported collaborative work (CSCW) environments), knowledge-based systems, and inquiring systems, all of which are described elsewhere in this encyclopedia. The progression of decision support system types that have emerged follows to some degree the increasing complexity of the problems being addressed. Some of the early DSS involved single decision-makers utilizing spreadsheet models to solve problems. Such an approach would be inadequate in addressing complex problems because one aspect of problem complexity is that multiple stakeholders typically exist. Baldwin (1993) examined the need for supporting multiple views and provides the only attempt found in the information systems literature to operationalize the concept of a perspective. In his work, a view is defined as a set of beliefs that partially describe a general subject of discourse. He identified three major components of a view: the belief or notion to convey, a language to represent the notion, and a subject of discourse. He further described notions as comprising aspects and a vantage point. Aspects are the characteristics or attributes of a subject or situation that a particular notion emphasizes. A vantage point is described by the level of detail (i.e., overview or detailed analysis). Assuming the subject of discourse can be identified with the notion, Baldwin described how differences in views may occur via differences in the notion, the language, or both.

The forest management decision support system project

1991 ◽  
Vol 67 (6) ◽  
pp. 622-628 ◽  
Author(s):  
Dan Bulger ◽  
Harold Hunt
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Forest Management ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Support Systems ◽  
Decision Makers ◽  
Management Decision ◽  
Management Decision Support

The focus of a decision support system is much different from Management Information Systems (MIS) and data-based "decision support systems". Decision support systems, as defined by the authors, focus on decisions and decision makers, and on information. Technology is treated as a tool and data as the raw material. In many traditional systems the focus is on the technology, and the data is the "information", while decision makers are, to some extent, externalized.The purpose of the Forest Management Decision Support System (FMDSS) project is to develop a set of software tools for creating forest management decision support systems. This set of tools will be used to implement a prototype forest management decision support system for the Plonski forest, near Kirkland Lake, Ontario.There are three critical ingredients in building the FMDSS, these are: (1) knowledge of the decision making process, (2) knowledge of the forest, and (3) the functionality of underlying support technology. The growing maturity of the underlying technology provides a tremendous opportunity to develop decision support tools. However, a significant obstacle to building FMDSS has been the diffuse nature of knowledge about forest management decision making processes, and about the forest ecosystem itself. Often this knowledge is spread widely among foresters, technicians, policy makers, and scientists, or is in a form that is not easily amenable to the decision support process. This has created a heavy burden on the project team to gather and collate the knowledge so that it could be incorporated into the function and design of the system. It will be difficult to gauge the success of this exercise until users obtain the software and begin to experiment with its use.

Review of classification studies for machine learning in the development of intelligent management decision support systems

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Classification Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Management ◽  
Management Decision Support

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.

Decision Support Systems in Crop Protection

1994 ◽  
Vol 23 (4) ◽  
pp. 281-285 ◽  
Author(s):  
Jonathan D. Knight ◽  
John D. Mumford
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Crop Protection ◽  
Expert Advice ◽  
Management Decision ◽  
Sufficient Information ◽  
Record Keeping ◽  
Pests And Diseases ◽  
The Right

All farmers and growers have at some time faced the decision of whether to control a pest in their crop. In order to make the correct decision the farmer needs access to, and an understanding of, sufficient information relevant to such pest problems. Decision support systems are able to help farmers make these difficult decisions by providing information in an easily understandable and quickly accessed form. The increasing use of computers by farmers for record-keeping and business management is putting the hardware necessary for the implementation of these systems onto more and more farms. The scarcity of expert advice, increasingly complex decisions and reduced economic margins all increase the importance of making the right pest management decision at the right time. It is against this background that decision support systems have an important role to play in the fight against losses caused by pests and diseases.

scholarly journals The Effect of Perceived Factors of Decision Support Systems on Actual Usage: Behavioral Intention of Using Decision Support Systems as a Mediating Variable: “An Empirical Study of Jordanian Higher Education Institutions”

2016 ◽  
Vol 12 (1) ◽  
pp. 201
Author(s):  
Bilal Mohammed Salem Al-Momani
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Behavioral Intention ◽  
Support Systems ◽  
Higher Education Institutions ◽  
Quality Of Information

Decision support systems (DSS) are interactive computer-based systems that provide information, modeling, and manipulation of data. DSS are clearly knowledge-based information systems to capture, Processing and analysis of information affecting or aims to influence the decision making process, performed by people in scope professional job appointed by a user. Hence, this study describes briefly the key concepts of decision support systems such as perceived factors with a focus on quality  of information systems and quality of information variables, behavioral intention of using DSS, and actual DSS use by adopting and extending the technology acceptance model (TAM) of Davis (1989); and Davis, Bagozzi and Warshaw (1989).There are two main goals, which stimulate the study. The first goal is to combine Perceived DSS factors and behavioral intention to use DSS from both the social perspective and a technology perspective with regard to actual DSS usage, and an experimental test of relations provide strategic locations to organizations and providing indicators that should help them manage their DSS effectiveness. Managers face the dilemma in choosing and focusing on most important factors which contributing to the positive behavioral intention of use DSS by the decision makers, which, in turn, could contribute positively in the actual DSS usage by them and other users to effectively solve organizational problems. Hence, this study presents a model which should provide the useful tool for top management in the higher education institutions- in particular-to understand the factors that determine using behaviors for designing proactive interventions and to motivate the acceptance of TAM in order to use the DSS in a way that contributes to the higher education decision-making plan and IT policy.To accomplish or attain the above mentioned objectives, the researcher developed a research instrument (questionnaire) and distributed it amongst the higher education institutions in Jordan to collect data in order to empirically study hypothesis testing (related to the objectives of study). 341 questionnaires were returned from the study respondents. Data were analyzed by utilizing both SPSS (conducted descriptive analysis) and AMOS (conducting structural equation modelling).Findings of the study indicate that some hypotheses were supported while the others were not. Contributions of the study were presented. In addition, the researcher presented some recommendations. Finally, this study has identified opportunities for further study which has progressed greatly advanced understanding constantly of DSS usage, that can help formulate powerful strategies Involving differentiation between DSS perceived factors.

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