Decision support systems classification in industry

Aqel, Musbah J.; Nakshabandi, Omar A.; Adeniyi, Ayodeji

doi:10.21533/pen.v7i2.550

Cited by 16 publications

(8 citation statements)

References 19 publications

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“…Aqel et al [8] synthesized a broad categorization of DSS by mode of assistance, orientation, user relationship, scope of use, focus area, type, and frequency of decision-making, showing how diverse and consolidated DSS are. According to Hasan et al [21], DSS is summarized in three categories when observing the focus area: model focus, data focus, and knowledge focus.…”

Section: A Decision Support Systemsmentioning

confidence: 99%

“…Among the chief limitations, when intelligent opaque-box models are employed in Decision Support Systems (DSS) to handle high-impact problems [8], for example, decisions involving human lives or high financial value, the lack of The associate editor coordinating the review of this manuscript and approving it for publication was Turgay Celik .…”

Section: Introductionmentioning

confidence: 99%

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Method to Produce More Reasonable Candidate Solutions With Explanations in Intelligent Decision Support Systems

Oliveira

Neto

2023

IEEE Access

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The integration of Artificial Intelligence techniques into Decision Support Systems yields effective solutions to decision problems, especially when complex scenarios are at hand. However, the use of intelligent black-box models can hinder the decision support system's potential to be fully adopted because opaque processes raise suspicions and doubts among careful decision makers. Moreover, appropriate and comprehensible explanations may foster trustworthiness and allow for reasonable adjustments or even corrections. This work proposed an approach that incorporates three reasonability aspects into Decision Systems: feasibility, rationality, and plausibility. Thus, by providing decision makers with reasonable candidate solutions for a complex problem, they are expected to perform their tasks more effectively (i.e. decide with more efficiency as well as efficacy). The new approach is accompanied by two proofs of concept in the health and public security areas. Comparative results using random and rational approaches, including the simulation of distinct user profiles, are presented. The proposed approach achieved superior metrics with regard to feasibility and plausibility, suggesting that this proposition can be applied to real-world applications.

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Section: A Decision Support Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Method to Produce More Reasonable Candidate Solutions With Explanations in Intelligent Decision Support Systems

Oliveira

Neto

2023

IEEE Access

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“…DSS are the area of the discipline of information systems (IS) that concentrates on supporting and improving managerial decision-making [16]. Given the research available on DSS, various definitions have been provided by scholars to present the differing perspectives on DSS [17]. According to Trianni et al [18], DSS involves computer systems that address issues that could be a combination of structured and unstructured components.…”

Section: Background 21 Decision Support Systems and Wildland Firesmentioning

confidence: 99%

Decision Support System Development of Wildland Fire: A Systematic Mapping

Vásquez

Cravero

Castro

et al. 2021

Forests

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Wildland fires have been a rising problem on the worldwide level, generating ecological and economic losses. Specifically, between wildland fire types, uncontrolled fires are critical due to the potential damage to the ecosystem and their effects on the soil, and, in the last decade, different technologies have been applied to fight them. Selecting a specific technology and Decision Support Systems (DSS) is fundamental, since the results and validity of this could drastically oscillate according to the different environmental and geographic factors of the terrain to be studied. Given the above, a systematic mapping was realized, with the purpose of recognizing the most-used DSS and context where they have been applied. One hundred and eighty-three studies were found that used different types of DSS to solve problems of detection, prediction, prevention, monitoring, simulation, administration, and access to routes. The concepts key to the type of solution are related to the use or development of systems or Information and Communication Technologies (ICT) in the computer science area. Although the use of BA and Big Data has increased in recent years, there are still many challenges to face, such as staff training, the friendly environment of DSS, and real-time decision-making.

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“…There is an urgent need to develop DSSs, which is an urgent scientific and social aim for the entire world community [31][32][33][34][35][36]. The level of development of digitalization in society also demonstrates the need to implement DSSs [37].…”

Section: Literature Sources Analysis and Purpose Of Study Formulationmentioning

confidence: 99%

Enterprise Compensation System Statistical Modeling for Decision Support System Development

et al. 2021

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This article raises the issue of decision support system (DSS) development in enterprises concerning the compensation system (CS). The topic is relevant as the CS is one of the main components in human resource management in business. A key element of such DSSs is CS models that provide predictive analytics. Such models are able to give information about how a particular CS affects output, product quality, employee satisfaction, and wage fund. Thus, the main goal of this article is to obtain a CS statistical model and its formulas for determining the probability densities of resultant indicators. To achieve this goal, the authors conducted several blocks of research. Firstly, mathematical formalization of CS functionality was described. Secondly, a statistical model of CS was built. Thirdly, calculations of CS result indicators were made. Reliable scientific methods were used: black box modeling and statistical modeling. This article proposes a statistical and analytical model. As an example, a piecework-bonus system statistical model is demonstrated. The discussion derives formulas of integral estimations showing the probability density of the resulting CS indicators and the related statistical characteristics. These results can be used to predict the behavior of the workforce. This constitutes the scientific novelty of the study, which will establish significant advances in the development of DSSs in the field of labor economics and HR management.

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Decision support systems classification in industry

Cited by 16 publications

References 19 publications

Method to Produce More Reasonable Candidate Solutions With Explanations in Intelligent Decision Support Systems

Method to Produce More Reasonable Candidate Solutions With Explanations in Intelligent Decision Support Systems

Decision Support System Development of Wildland Fire: A Systematic Mapping

Enterprise Compensation System Statistical Modeling for Decision Support System Development

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