Application of Group Decision Support Systems in Marketing Education

Decision support and intelligent systems in the textile and apparel supply chain: An academic review of research articles

In the past several decades, Taiwanese companies have been successful in the world market. However, knowledge about how these firms use information technology is very limited. Investigates the application of decision support systems (DSS) and executive information systems (EIS) in Taiwan. Two mail surveys were conducted to explore who used the system, where the systems were applied, what decision models were used and reasons for not using these systems. The results indicate that: more than 20 per cent of the firms already used DSS or EIS and an additional 37 per cent had plans to adopt them shortly; 87 per cent of the firms considered DSS and EIS to be important to their competitiveness; middle- and lower-level managers were major DSS and EIS users; half of them used the system every day; the most troublesome problem in developing DSS/EIS was the difficulty in determining information requirements; and information aggregation and what-if analysis were major functions for decision support. Furthermore, quantitative models were employed only in large and old companies. The most popular models included linear programming, PERT/CPM, and regression analysis, whereas the most popular domains for application were finance and production management.

Background. The digital transformation of dentistry involves not only the digitization of patient records but also the integration of intelligent Decision Support Systems (DSS), which can enhance diagnostic accuracy, standardize treatment protocols, and optimize clinician – patient interaction. With the rapid development of artificial intelligence (АІ) technologies, DSS are gaining increasing importance in both clinical and educational contexts within dentistry. Purpose – to systematize existing approaches to the classification, design, and implementation of DSS in dentistry, with a focus on their functional capabilities, architectural models, and preconditions for successful clinical deployment. Materials and Methods. The study is based on the analysis of peer-reviewed scientific literature published between 2020 and 2024 from Scopus, PubMed, and IEEE databases, as well as technical documentation of existing DSS solutions. Methods of systems analysis, descriptive classification, comparative evaluation, and synthesis of preconditions for implementation were applied. Two summary tables were developed: one on the advantages and limitations of different DSS types, and another on implementation preconditions. Results. Two primary classification axes of DSS were identified: functional (diagnostic, prognostic, therapeutic planning, administrative, educational, AI-based) and architectural (rule-based, case-based, AI-based, fuzzy logic, hybrid systems). Rule-based systems remain the most widely used due to their transparency and alignment with clinical protocols, while AI-driven systems demonstrate potential but require further validation and explainability. A set of key preconditions for effective implementation was formulated, including structured clinical data, interoperability with electronic health records, appropriate infrastructure, and clinical staff readiness. Conclusions. DSS has demonstrated high potential for improving the quality, safety, and personalization of dental care. Promising directions for future development include hybrid DSS models that combine rule-based and AI approaches, as well as the creation of unified standards for integration and legal compliance.

Discusses the application of decision support systems (DSS) to assist in solving marketing decisions. As the marketing environment becomes more competitive the pressure to gain the “edge” over your competitors becomes more intense. Marketing decision support systems (MDSS) are valuable tools to assist in making marketing decisions to do just that! MDSS can be used to support, rather than replace, decision makers in the complex, semi or unstructured situations which are common in marketing problems. They incorporate the personal judgement and experience of the user to improve the effectiveness, rather than the efficiency of decision making. This means that managers will have more “quality” time to spend on analysis and planning for the future, rather than merely reflecting on the past. Discusses different types of DSS within an environment where there are discrepancies over definition, jargon and suppliers’ claims.

There are several spatial problems in the water resources sector such as flood control and management, which can be managed efficiently if there is a case-based decision-making process. These types of problems need human intervention and amicable management is possible by decision making based on various possible alternatives. The recent advances in computational facilities, quick and real time access to various types of data and development in graphical user interface (GUI), have led to the frequent application of decision support system (DSS) in complex water resources management problems like flood management. This review paper summarises past efforts employing DSS, risk-based DSS and future research for DSS used in flood management. The main purpose of this review is to assess the progress made in the development of DSS and to understand how efficient it is in solving flood management problems.

: Workshops in the late 1990's launched the commitment of the U.S. Geological Survey's Biological Resources Division (BRD) to develop and implement decision support systems (DSS) applications. One of the primary goals of this framework document is to provide sufficient background and information for Department of the Interior (DOI) bureau stakeholders and other clients to determine the potential for DSS development. Such an understanding can assist them in carrying out effective land planning and management practices. This document provides a definition of DSS and its characteristics and capabilities. It proceeds to describe issues related to meeting resource managers needs, such as the needs for specific applications, customer requirements, information and technology transfer, user support, and institutionalization. Using the decision process as a means to guide DSS development and determine users needs is also discussed. We conclude with information on methods to evaluate DSS development efforts and recommended procedures for verification and validation.

During the last few decades, research into sustainability aspects of ICT grew rapidly for most organisational and business processes. Evolution towards the development of infrastructure for Smart Cities, Green ICT with application of decision support systems (DSSs) created opportunities for solving complex problems, for example, sensor-enabled smart waste management. This paper focuses on supporting decisions for sustainable garbage collection route planning and optimisation. The objective is to create a practical solution for reducing CO2 emissions and reduce negative impact on the environment as a result. From the design science perspective, a DSS is proposed, researched and developed to increase the effectiveness of municipal waste management. The paper also evaluates this approach against the Green IS framework.

Decision support systems applications research: A bibliography (1971–1988)

This chapter describes an application of decision support systems to conflict modelling in information operations recognition. An information operation is considered as a complex weakly structured system. We suggest a model of conflict between two subjects, based on the second-order reflexive model. We describe a method for construction of the design pattern for knowledge bases of decision support systems. We also suggest a methodology allowing to use decision support systems for modelling of conflicts during information operations recognition. The methodology is based on the use of expert assessments and content monitoring data.KeywordsInformation operation recognitionDecision support systemKnowledge baseContent-monitoring systemReflexive modelConflict modelling

Today, not only scientists and researchers, but public services and governments as well use DSS (Decision Support System) methods to solve forest policy and management problems in order to meet the multiple needs of the public and to achieve the greatest possible effectiveness. This paper presents and analyses the current trends in emerging DSS applications for the sector of forest policy and management. The research relates to the scientific journal articles that propose a DSS since 2007 and until the present. The DSS are registered and classified as to their characteristics, such as their decision support topic, multiple-aim DSS, country of application, type of software, database use, GIS, mathematics, online presentation in the Internet etc. Finally the DSS applications are categorized as to the above characteristics and current trends are identified and discussed. GIS and Database technology seem to be the most popular software used by the decision makers, since Europe North America emphasize most in these kind of DSS applications.

In this paper, we try to optimize the best goats by using expert system. A farm to always be competitive in the face of market competition, is required to be able to provide effective and efficient services through the selection process and selection of livestock quickly but still do not rule out the accuracy of the best criteria of livestock to consumers. The purpose of this researcher is to build the best goat selection decision support system with topsis method as an option to determine some criteria with ideal solution. Technological developments can be utilized in the best selection of livestock, by building expert systems capable of handling livestock selection automatically and can save time and cost in selecting livestock. Based on the test results, the application of decision support system is useful to help and facilitate the selection of livestock, so that consumers can easily choice on the best livestock according to expectations based on predetermined criteria.

Weaning REX rabbits child is one of the keys to success in raising or cultivating rabbits. Unsuitable weaning can result in very high mortality or mortality risks, while weaning for too long results in decreased productivity and the possible marriage of children with rabbit parent. The research done is to design and build the application of decision support system (DSS) with Simple Additive Weighting method (SAW) with 5 criteria as supporting of DSS calculation that are body weight (gram), body length (cm), still breastfeeding or not, already independent for food or not and Gender. The webased DSS application created is tested by using the calculation system compared with research data. Based on the test of 36 rabbit children which is derived from previous research results obtained showed that the application of the DSS are built 100% the same result.

This paper aims to introduce the Decision Support System (DSS), which is an area informatics approach to area studies, and the research network complex based on DSS's application in Thailand. In particular, the paper presents and discusses current activities of the DSS research network at the national level in Thailand rather than being a theoretical and analytical study of the DSS mechanism or its cases. DSS has wide applications in Thailand, extending to various fields such as agricultural and natural resource management, economic and social management, historical and cultural preservation, and so on. DSS generally refers to a support system embedded into a computer system for providing intellectual resources with the appropriate numerical model necessary for decision making in production activities. Agricultural and natural resources have been the foundation of social and economic development in Thailand since the country's first national plan in the1960s. Decision making to maintain agricultural productivity as well as to protect natural resources requires well-integrated data sets. The Thailand Research Fund(TRF) established a DSS research and development network (TRF-DSS) in 2002 to help various research teams in Thailand develop DSS tools and components for addressing agricultural and natural resource management issues. Using a "systems"approach, the DSS framework allows researchers and users to identify and integrate key components as well as to define databases and model-base management systems. This paper focuses on the TRF-DSS research and development network, which consists of 12 universities, two line agencies in Thailand, and a line agency in Cambodia. During 2002–10, a total of 59 research projects were granted a budget allocation of 140.1 million baht, and a budget of 15.4 million baht was allocated to support activities of the network. In addition, 10 projects were funded to carry out post-project activities, with a total budget of 1.5 million baht. More than 20 DSS tools were developed and implemented by various users, ranging from policy makers to provincial and local government agencies engaged in short- and long-term planning and management. Most DSS tools were designed to allow the integration of biophysical and socioeconomic data as well as the decision support modules for alternatives evaluation and analysis. These approaches support the choices of dynamic simulation models as well as multi-criteria analyses for modelbase software development. They also allow users to evaluate various alternatives in agricultural and natural resource management. Networking is a powerful platform for DSS research and development and maybe applied to other types of research and development agenda in Thailand, such as area study projects. DSS tools contribute to an understanding of sustainability of production systems against a background of climate change, poverty reduction, and food security.

This study presents the development and application of a prototype decision support system (DSS) for tree selection specifically for Punjab, India, a region facing challenges of low forest cover and an increasing demand for sustainable land use practices. The DSS developed using the R Shiny framework integrates ecological, social, and agro-commercial criteria to facilitate scientific knowledge decision making in tree plantation. The modules in this DSS include a tree selection tool based on comprehensive species attributes, a GIS-based tree suitability map module utilizing an Analytical Hierarchical Process (AHP), and a silviculture practice information module sourced from authoritative databases. Combining sophisticated statistical and spatial analysis, such as NMDS and AHP-GIS, this DSS mitigates data redundancy in SDM while incorporating extensive bibliographic research in dataset processing. The study highlights the necessity of fundamental niche-based suitability in comparison to realized niche suitability. It emphasizes on the importance of addressing ecosystem services, agro-commercial aspects, and enhancing silvicultural knowledge. Additionally, the study underscores the significance of local stakeholder engagement in tree selection, particularly involving farmers and other growers, to ensure community involvement and support. The DSS supports agroforestry initiatives and finds applications in urban tree management and governmental programs, emphasizing the use of scientific literature at each step, in contrast to relying solely on local knowledge.

In this paper we consider the basic ideas, the implementation, and the application of an object-oriented decision support system (DSS), especially for multiple criteria decision making (MCDM). One main idea of the DSS is to utilize the features of different MCDM methods by integration. We discuss several approaches of integration esp. the combination of methods, the use of neural networks, and a generalization of network-like structures. Information acquisition is an essential part for any DSS. Beside aspects of interactive data entry, we discuss the utilization of historical data (especially from former decisions) for learning. By doing so the parameters of a method, some methods, or a network of methods can be tuned and we can answer questions like: Which method should be used? How can the parameters be adjusted? Is it useful to apply different methods and to aggregate their results to build a compromise solution? And how should this be done?