Adaptive Decision Support Systems for Evaluating Risk and Uncertainty in Capital Investment Decisions: Opportunities for Future Research

1995 ◽  
Vol 21 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Wilton L. Accola ◽  
Surendra P. Agrawal ◽  
Clyde W. Holsapple
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Capital Investment ◽  
Support Systems ◽  
Investment Decisions ◽  
Future Research ◽  
Risk And Uncertainty

scholarly journals Scheduling and Rescheduling Operations Using Decision Support Systems: Insights From Emotional Influences on Decision-Making

2021 ◽  
Vol 2 ◽  
Author(s):  
Mélody Mailliez ◽  
Olga Battaïa ◽  
Raphaëlle N. Roy
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Mental State ◽  
Production Scheduling ◽  
Manufacturing Systems ◽  
Support Systems ◽  
Mental States ◽  
Future Research ◽  
Modern Manufacturing

For many years, manufacturers have focused on improving their productivity. Production scheduling operations are critical for this objective. However, in modern manufacturing systems, the original schedule must be regularly updated as it takes places in a dynamic and uncertain environment. The modern manufacturing environment is therefore very stressful for the managers in charge of the production process because they have to cope with many disruptions and uncertainties. To help them in their decision-making process, several decision support systems (DSSs) have been developed. A recent and enormous challenge is the implementation of DSSs to efficiently manage the aforementioned issues. Nowadays, these DSSs are assumed to reduce the users' stress and workload because they automatically (re)schedule the production by applying algorithms. However, to the best of our knowledge, the reciprocal influence of users' mental state (i.e., cognitive and affective states) and the use of these DSSs have received limited attention in the literature. Particularly, the influence of users' unrelated emotions has received even less attention. However, these influences are of particular interest because they can account for explaining the efficiency of DSSs, especially in modulating DSS feedback processing. As a result, we assumed that investigating the reciprocal influences of DSSs and users' mental states could provide useful avenues of investigation. The intention of this article is then to provide recommendations for future research on scheduling and rescheduling operations by suggesting the investigation of users' mental state and encouraging to conduct such research within the neuroergonomic approach.

scholarly journals Big data as a value generator in decision support systems: a literature review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gustavo Grander ◽  
Luciano Ferreira da Silva ◽  
Ernesto Del Rosário Santibañez Gonzalez
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support Systems ◽  
Support Systems ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Content Type ◽  
Theoretical Contribution

PurposeThis paper aims to analyze how decision support systems manage Big data to obtain value.Design/methodology/approachA systematic literature review was performed with screening and analysis of 72 articles published between 2012 and 2019.FindingsThe findings reveal that techniques of big data analytics, machine learning algorithms and technologies predominantly related to computer science and cloud computing are used on decision support systems. Another finding was that the main areas that these techniques and technologies are been applied are logistic, traffic, health, business and market. This article also allows authors to understand the relationship in which descriptive, predictive and prescriptive analyses are used according to an inverse relationship of complexity in data analysis and the need for human decision-making.Originality/valueAs it is an emerging theme, this study seeks to present an overview of the techniques and technologies that are being discussed in the literature to solve problems in their respective areas, as a form of theoretical contribution. The authors also understand that there is a practical contribution to the maturity of the discussion and with reflections even presented as suggestions for future research, such as the ethical discussion. This study’s descriptive classification can also serve as a guide for new researchers who seek to understand the research involving decision support systems and big data to gain value in our society.

Application of decision support systems in water management

2014 ◽  
Vol 22 (3) ◽  
pp. 189-205 ◽  
Author(s):  
Kejiang Zhang ◽  
Amin Zargar ◽  
Gopal Achari ◽  
M. Shafiqul Islam ◽  
Rehan Sadiq
Keyword(s):  
Water Management ◽  
Decision Support ◽  
Decision Support Systems ◽  
Asset Management ◽  
Water Resource Management ◽  
Distribution Systems ◽  
Water Distribution ◽  
Support Systems ◽  
Future Research ◽  
Water And Wastewater Treatment

This paper presents an overview of decision support systems (DSSs) as applied to water management. This includes the definition of DSSs as pertinent to water management, its basic components, various techniques employed in development of DSSs, and technological and conceptual trends in DSSs. The application of DSSs in various areas of water management such as water resource management, water and wastewater treatment operations, water distribution systems, and infrastructure asset management is discussed. Calibration and validation of DSSs and future research in the development of new generations of DSSs is presented.

The Role of Information in Decision Making

2011 ◽  
pp. 776-782
Author(s):  
Edward Shinnick ◽  
Geraldine Ryan
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Future Research ◽  
Managerial Decision ◽  
Nature Of Information ◽  
Effective Decision ◽  
Effective Decision Support ◽  
Effective Decision Making

The advent of the World Wide Web and other communication technologies has significantly changed how we access information, the amount of information available to us, and the cost of collecting that information. Individuals and businesses alike collect and interpret information in their decision-making activities and use this information for personal or economic gain. Underlying this description is the assumption that the information we need exists, is freely available, and easy to interpret. Yet in many instances this may not be the case at all. In some situations, information may be hidden, costly to assimilate, or difficult to interpret to ones own circumstances. In addition, two individuals who look at the same information can reach different conclusions as to its value. One person may see it as just a collection of numbers, another sees a market opportunity. In the latter case, information is used in an entrepreneurial way to create a business opportunity. Advances in technology have created opportunities to do this by creating information systems that can support business decision-making activities. Such decision support systems are playing an increasingly important role in determining not only the efficiency of businesses but also as business opportunities themselves through the design and implementation of such systems for other markets and businesses. However all is not easy as it may first seem. Quality decision making and effective decision support systems require high quality information. The implicit assumption in talking about decision support systems is that the required information is always available. It is somewhere “out there” and must just be collated to make use of it. However, very often this is not the case. Information that is scarce or inaccessible is often more valuable and can be the very reason for many firm’s existence. The importance for firms to process information to do with its business environment on issues such as, market trends, events, competitors, and technological innovations relevant to their success is prevalent in the management and IS literature.1 The theme of this article is to analyse the role information plays in managerial decision making at individual, group, and firm level from an economics perspective. We argue that access to information is essential for effective decision making and look at problems associated with insufficient information; the effects that such information deficits have in shaping and designing markets are then explored. We start by exploring the nature of information and the issue of asymmetric information. We examine the different solutions put forward to address information deficits, such as advertising, licensing, and regulation. Finally we conclude by outlining likely future research in markets with information deficits.

scholarly journals Intelligent Alarm System-Based Devices Designed for People with Disabilities, Caused by Various Chronic Diseases

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Roshanak Tirdad ◽  
Piruz Nami ◽  
Shayan Samieyan ◽  
Fakher Rahim
Keyword(s):  
Decision Support ◽  
Chronic Disease ◽  
Decision Support Systems ◽  
Chronic Diseases ◽  
Computer Network ◽  
Intelligent System ◽  
Support Systems ◽  
Future Research ◽  
Care Providers ◽  
Long Term Treatment

Context: Chronic diseases (CD) are defined as symptoms or disabilities, caused by diseases, genetic factors, and injury requiring long-term treatment. Intelligent alarm systems, which collect patient health data and transfer it to a medical server, help track and avoid future incidents. Method: The search terms were “computer network” OR “information systems” OR “wireless technology” OR “decision support systems” AND “chronic disease” OR “chronic disease” in major electronic databases, including Pubmed/Medline, Scopus, Embase, ISI Web of Science, and Cochrane Central. Results: The search resulted in 1275 articles with 11 specific to intelligence-based systems in chronic medical conditions until 08 June 2021. The creation of different access levels for care providers in the system and application customization according to CD conditions were the goals that can be achieved in future research. The human-computer interface (HCI) systems, smart home, and software, such as Fitbit using IoMT to monitor health metrics in people with different CDs, are introduced so far. Conclusions: These systems, if provided on the web and mobile platform, can be accessed at any time and place and are more efficient. Finally, the combination of clinical decision support systems with artificial intelligence has beneficial effects on physician's systems, increases the accuracy in CD diagnosis, and improves the pain management. This intelligent system demonstrates factors influencing back to work and allows identifying high-risk patients and their potential to handle activities of daily living.

scholarly journals Clinical Decision Support Systems for Drug Allergy Checking: Systematic Review (Preprint)

2017 ◽  
Author(s):  
Laura Légat ◽  
Sven Van Laere ◽  
Marc Nyssen ◽  
Stephane Steurbaut ◽  
Alain G Dupont ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Drug Allergy ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Outcome Evaluation ◽  
Future Research ◽  
Alert Fatigue

BACKGROUND Worldwide, the burden of allergies—in particular, drug allergies—is growing. In the process of prescribing, dispensing, or administering a drug, a medication error may occur and can have adverse consequences; for example, a drug may be given to a patient with a documented allergy to that particular drug. Computerized physician order entry (CPOE) systems with built-in clinical decision support systems (CDSS) have the potential to prevent such medication errors and adverse events. OBJECTIVE The aim of this review is to provide a comprehensive overview regarding all aspects of CDSS for drug allergy, including documenting, coding, rule bases, alerts and alert fatigue, and outcome evaluation. METHODS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed as much as possible and searches were conducted in 5 databases using CPOE, CDSS, alerts, and allergic or allergy as keywords. Bias could not be evaluated according to PRISMA guidelines due to the heterogeneity of study types included in the review. RESULTS Of the 3160 articles considered, 60 met the inclusion criteria. A further 9 articles were added based on expert opinion, resulting in a total of 69 articles. An interrater agreement of 90.9% with a reliability Κ=.787 (95% CI 0.686-0.888) was reached. Large heterogeneity across study objectives, study designs, study populations, and reported results was found. Several key findings were identified. Evidence of the usefulness of clinical decision support for drug allergies has been documented. Nevertheless, there are some important problems associated with their use. Accurate and structured documenting of information on drug allergies in electronic health records (EHRs) is difficult, as it is often not clear to healthcare providers how and where to document drug allergies. Besides the underreporting of drug allergies, outdated or inaccurate drug allergy information in EHRs poses an important problem. Research on the use of coding terminologies for documenting drug allergies is sparse. There is no generally accepted standard terminology for structured documentation of allergy information. The final key finding is the consistently reported low specificity of drug allergy alerts. Current systems have high alert override rates of up to 90%, leading to alert fatigue. Important challenges remain for increasing the specificity of drug allergy alerts. We found only one study specifically reporting outcomes related to CDSS for drug allergies. It showed that adverse drug events resulting from overridden drug allergy alerts do not occur frequently. CONCLUSIONS Accurate and comprehensive recording of drug allergies is required for good use of CDSS for drug allergy screening. We found considerable variation in the way drug allergy are recorded in EHRs. It remains difficult to reduce drug allergy alert overload while maintaining patient safety as the highest priority. Future research should focus on improving alert specificity, thereby reducing override rates and alert fatigue. Also, the effect on patient outcomes and cost-effectiveness should be evaluated.

scholarly journals DECISION-MAKING WITH MODELING OF PROBLEM SITUATIONS USING THE ANALYTIC NETWORK HIERARCHY PROCESS

2011 ◽  
Vol 3 (1) ◽  
Author(s):  
Tatiana Kravchenko ◽  
Natalia Seredenko
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Risk And Uncertainty ◽  
Problem Situations ◽  
Network Hierarchy ◽  
Hierarchy Process

<p>The Modeling of problem situations is a very important issue in decision-making theory. Actually, there are no decision support systems which include decision making methods under risk and uncertainty. The main advantage of a proposed approach is the ability to process dependences and feedbacks which may exist between conditions, sub-conditions and their realizations.</p><p>http://dx.doi.org/10.13033/ijahp.v3i1.81</p>

scholarly journals Decision Support Systems in Construction: A Bibliometric Analysis

Buildings ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 108 ◽  
Author(s):  
Muhammad Rashid Minhas ◽  
Vidyasagar Potdar
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Bibliometric Analysis ◽  
Construction Projects ◽  
Support Systems ◽  
Research Output ◽  
Construction Materials ◽  
Sustainable Construction ◽  
Future Research ◽  
Future Research Directions

In recent years, the use of decision support systems for selecting sustainable construction materials in the building and commercial construction projects has received a great deal of attention. This paper reports an in-depth and systematic bibliometric analysis of the literature using Decision Support Systems (DSSs) for its construction, based on the papers published during the period from 2000 to 2016. The data were collected from two major databases, Web of Science (WoS) and Scopus, which included 2185 and 3233 peer reviewed articles, respectively. The analysis includes a general bibliometric analysis (publications output, country-wise research output, authorship, and collaboration patterns of these published articles). It also includes a citation analysis (keywords, most cited keywords, organizations, most cited articles, and average citations per article) and a network analysis (authors and countries). Overall, this study provides bibliometric insights and future research directions for researchers and practitioners who use DSSs.

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