Historical Overview of Decision Support Systems (DSS)

Author(s):  
Udo Richard Averweg

During the late 1970s the term “decision support systems” was first coined by P. G. W. Keen, a British Academic then working in the United States of America. In 1978, Keen and Scott Morton published a book entitled, Decision Support Systems: An Organizational Perspective (Keen & Scott Morton, 1978), wherein they defined the subject title as computer systems having an impact on decisions where computer and analytical aids can be of value but where the manager’s judgment is essential. Information systems (IS) researchers and technologists have developed and investigated decision support systems (DSS) for more than 35 years (Power, 2003b). The structure of this article is as follows: The background to DSS will be given. Some DSS definitions, a discussion of DSS evolution, development of the DSS field and frameworks are then presented. Some future trends for DSS are then suggested.

2011 ◽  
Vol 26 (4) ◽  
pp. 259-267 ◽  
Author(s):  
Helga Drummond

Dysfunctional MIS is an important topic but one that has received comparatively little attention in the literature. This discussion paper attaches new literature to the subject. The new literature centres upon the epistemological status of certain forms of MIS. More specifically, it is argued that MIS, based upon metonymy (part for whole substitution), can seriously mislead managers because the representation gets mistaken for the reality. The demonstration is based on high level risk registers. Risk registers were selected for analysis because they are ubiquitous and important decision support systems. In theory, diligent use of risk registers should virtually eliminate unpleasant surprises. In practice, the result may be an illusion of control. Analysis draws upon sociology and psychology to explain why this may be so.


2017 ◽  
Vol 20 (2) ◽  
pp. 263-280
Author(s):  
Stuart F. Sheffield ◽  
Jonathan L. Goodall ◽  
Mohamed M. Morsy ◽  
Alexander B. Chen

Abstract Web services providing machine-accessible interfaces to environmental data are now commonplace. Building on this, a current trend is to expand these web services to provide on-demand access to model and analysis services. This progression suggests the future possibility of cloud-based decision support systems (DSSs) integrating distributed data and analysis services delivered through a host of providers. Such distributed environmental DSSs have many potential benefits, but would require highly scalable and responsive web services. The objective of this study is to assess the current feasibility of building distributed environmental DSSs from existing web services in the United States. Results show that, of the many available web services providing information about soils, river network topology, watersheds, streamflow, etc., response times are often only a few seconds for a small project area, but can grow exponentially as the project area increases. On-demand watershed delineation remains a slow-to-respond service relative to the other services tested. Also, the results suggest the need to better co-locate servers near client applications to speed up response times. Collectively, these results provide specific areas where future research is needed in order to achieve the vision of on-demand distributed environmental DSSs.


Tecnura ◽  
2020 ◽  
Vol 24 (66) ◽  
pp. 95-108
Author(s):  
Juan Manuel Sánchez Céspedes ◽  
Juan Pablo Rodríguez Miranda ◽  
Olga Lucia Ramos Sandoval

Context: The process of formulating agricultural public policies is very complex due to the large number of variables involved in the process. That is why the development of decision support systems (DSS) help to improve this process. The article reviews the developments that have been made regarding the subject. Method: The method was to conduct a bibliographic review in several scientific databases, looking for developments of DSS systems applied to the process of formulating agricultural policies. When determining which DSS systems have been developed, a qualitative and descriptive analysis of the systems was carried out.


2003 ◽  
Vol 13 (3) ◽  
pp. 562-568
Author(s):  
Jonathan M. Lehrer ◽  
Mark H. Brand

Web sites such as the University of Connecticut (UConn) Plant Database allow large volumes of information and images to be stored, published and accessed by users for the purpose of informed decision-making. Sorting information on the World Wide Web (Web) can be difficult, especially for novice users and those interested in quick results. The advent of Internet search and retrieval software fosters the creation of interactive decision support systems. The Plant Selector was designed to complement the UConn Plant Database plant encyclopedia by allowing Web site users to generate lists of woody ornamental plants that match specific criteria. On completion of an HTML-based search form by users, a Web-enabled database is searched and lists of matching plants are presented for review. To facilitate analysis of the Plant Selector's efficacy, an online questionnaire was implemented to solicit user feedback. Survey data from 426 responses to the online evaluation tool were analyzed both to understand user demographics and gauge satisfaction with the Plant Selector module. Survey data revealed that most Plant Selector users are between 40 to 65 years of age and homeowners with minimal horticultural experience. A large percentage of Web site visitors (68%) is located across the United States beyond Connecticut and the New England region. The great majority of survey respondents (65%) use this tool to select plants for the home landscape. Most (77%) either agree or strongly agree that the Plant Selector is easy to use and delivers results that are useful (66%), while 70% agree or strongly agree that the categories used by the Plant Selector are sufficient. The survey results in general suggest that Web-based decision support systems may serve useful roles in the field of horticulture education.


Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 483
Author(s):  
Melanie Colavito

Decision support systems (DSSs) are increasingly common in forest and wildfire planning and management in the United States. Recent policy direction and frameworks call for collaborative assessment of wildfire risk to inform fuels treatment prioritization using the best available science. There are numerous DSSs applicable to forest and wildfire planning, which can support timely and relevant information for decision making, but the use and adoption of these systems is inconsistent. There is a need to elucidate the use of DSSs, specifically those that support pre-wildfire, spatial planning, such as wildfire risk assessment and forest fuels treatment prioritization. It is important to understand what DSSs are in use, barriers and facilitators to their use, and recommendations for improving their use. Semi-structured interviews with key informants were used to assess these questions. Respondents identified numerous barriers, as well as recommendations for improving DSS development and integration, specifically with respect to capacity, communication, implementation, question identification, testing, education and training, and policy, guidance, and authorities. These recommendations can inform DSS use for wildfire risk assessment and treatment prioritization to meet the goals of national policies and frameworks. Lastly, a framework for organizing spatial, pre-wildfire planning DSSs to support end-user understanding and use is provided.


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