Capabilities Of Decision Support System Research Articles

Methodologies and Handling Techniques of Large-Scale Information in Decision Support Systems for Complex Missions

Designing integrating systems for support, real-time monitoring, and executing of complex missions is challenging, since they often fail due to high levels of complexity and overwhelming volume of input data. Past attempts have resorted to “ad hoc” solutions, which face issues of being non-updatable, non-upgradable, and not applicable to similar missions, necessitating a complete redesign and reconstruction of the system. In the national defense and security sector, the impact of this reconstruction requirement leads to significant costs and delays. This study presents advanced methodologies for organizing large-scale datasets and handling complex operational procedures systematically, enhancing the capabilities of Decision Support Systems (DSSs). By introducing Complex Mission Support Systems (CMSSs), a novel SS sub-component, improved accuracy and effectiveness are achieved. The CMSS includes mission conceptualization, analysis, real-time monitoring, control dynamics, execution strategies, and simulations. These methods significantly aid engineers in developing DSSs that are highly user-friendly and operational, thanks to human-reasoning-centered design, increasing performance and efficiency. In summary, the systematic development of data cores that support complex processes creates an adaptable and adjustable framework in a wide range of diverse missions. This approach significantly enhances the overall sustainability and robustness of an integrated system.

Artificial neural network-based decision support systems in manufacturing processes: A systematic literature review

The use of artificial neural network models to enrich the analytical and predictive capabilities of decision support systems in manufacturing has increased. The growing complexity and uncertainty in the manufacturing sector demand improved decision-making to ensure low operations costs, high productivity, and sustainable use of resources. Artificial neural networks have the inherent capacity to analyze the most uncertain and complex patterns in unstructured decision problems. This review aims to synthesize and provide a comprehensive summary of recent studies on artificial neural network-based decision support systems as applied in manufacturing processes. First, the specific processes in manufacturing where artificial neural network-based decision support systems are used are analyzed. A total of 99 multi-disciplinary publications on artificial neural network-based decision support systems published between 2011 and 2021 are retrieved and processed following a rigorous execution of the designated acceptance criteria and quality assessment. A review of the selected studies indicates a growing interest in applying artificial neural networks in decision support systems. Product and process design, performance evaluation, and predictive maintenance are the main application areas identified. A growing tendency to combine artificial neural network models with other intelligent tools, notably fuzzy logic, and genetic algorithm, is noted to overcome drawbacks such as slow convergence when training the algorithms. Further research should extend to other tools for enriching the performance of artificial neural networks in manufacturing processes.

Amplifying organizational performance from business intelligence: Business analytics implementation in the retail industry

PURPOSE: The concept of business analytics (BA) and business intelligence (BI) is just emerging in the Philippines. Since these are new concepts, it is important to investigate their impact on organizational performance and the performance metrics in business industry. The aim of this study is to examine the impact of business analytics generating business intelligence and how it affects organizational performance by developing a structural model. Consequently, the impact of organizational performance on other performance metrics was also established. METHODOLOGY: The partial least squares – structural equation modeling was utilized, which proposed a model that shows how business intelligence, generated by business BA, affects organizational performance, which consequently leads to improved marketing, financial, and business process performance. A survey was conducted on business analysts and executive managers of retail companies that have already been implementing BA for at least three years. FINDINGS: BA capabilities have a significant positive effect on the level of BI. BI has a significant positive effect on organizational performance. However, the result of the moderation analysis indicated that the level of readiness for BA implementation could not be considered a moderating factor on the relationship between BI and organizational performance. IMPLICATIONS: Out of the different BA capabilities, the decision support system and business process management were found to be the most beneficial functions in generating BI. BI amplifies organizational performance and consequently improves the marketing and business process performance of retail firms. However, the readiness for BA implementation does not significantly affect how BI improves organizational performance. Overall, it is recommended that in order to enhance marketing and business process performance, retail firms should focus on the BA capabilities of decision support system and business process management. ORIGINALITY AND VALUE: This would be the first empirical study in the Philippines which has assessed how business analytics and business intelligence impact organizational performance. This study is original in determining what BA capabilities generate BI, which translates to improved organizational performance. This study is also unique in defining what key performance metrics are much improved as a result of its implementation. This may serve as a viable reference for other researchers interested in business analytics and other technology about data management applied in business operations.

Towards robust results in Multi-Criteria Decision Analysis: ranking reversal free methods case study

The practical capabilities of decision support systems are beneficial in many complex problems. They aim to equip the decision-maker with knowledge about the preferred choices from the set under consideration. Often, there are minor differences between the alternatives analyzed, making it necessary to determine whether changes in the parameters characterizing them can influence their final position. For this purpose, a sensitivity analysis of the solutions can be used to examine the resistance of the results to changes. In this paper, we address the problem of performing the sensitivity analysis using MCDA methods. Most studies focus on testing the sensitivity of rankings of popular methods. However, their main disadvantage is their susceptibility to the phenomenon of ranking reversal. It may distort the results obtained in a sensitivity analysis. Therefore, it is worth using methods resistant to the ranking reversal phenomenon for this purpose. The SPOTIS and COMET methods were used in a theoretical multi-criteria problem, where the calculated results were then subjected to sensitivity analysis. The study showed that the SPOTIS method tends to be more robust to the computed results.

Role and potential of decision support systems in forestry

Decision Support Systems (DSS) are essential tools for forest management practitioners to help take account of the many environmental, economic, administrative, legal and social aspects in forest management. This paper is concerned with the technique to develop DSS for forest management system to evaluate models and methods considering all the important factors to categorize the problem. The problem is based on temporal and spatial parameters, number of objectives, decision makers and goods and services. Some of these problem dimensions are inter-related, and we also found a significant relationship between various methods and problem dimensions, all of which have been analysed using contingency tables. The results showed that 63% of forest DSS use simulation modeling methods and these are particularly related to the spatial context and spatial scale and the number of people involved in taking a decision. The analysis showed how closely Multiple Criteria Decision Making is linked to problem types involving the consideration of the number of objectives, also with the goods and services. On the other hand, there was no significant relationship between optimization and statistical methods and problem dimensions, although they have been applied to approximately 60% and 16% of problems solved by DSS for forest management, respectively. Metaheuristics and spatial statistical methods are promising new approaches to deal with certain problem formulations and data sources. Nine out of ten DSS used an associated information system, but the availability and quality of data continue to be an important constraining issue, and one that could cause considerable difficulty in implementing DSS in practice. Very often DSS is used largely based to study market economy. The results suggest a strong need to improve the capabilities of DSS in this regard, developing and applying MCDM models and incorporating them in the design of DSS for forest management in coming years.

The Impact of Decision Support Systems on Organizational Innovation: Field Study in Industrial Companies in Dhofar Governorate: أثر نظم دعم القرارات على الإبداع التنظيمي: دراسة ميدانية في الشركات الصناعية بمحافظة ظفار

The aim of the research is to identify the impact of decision support systems on organizational creativity in industrial companies in Dhofar Governorate. The research relied on the questionnaire in collecting information. The research population consisted of employees in the top and middle management of three industrial companies in Dhofar Governorate (Raysut Cement, Dhofar Energy, and Salalah Methanol Company) and they (97) employees. Questionnaires were distributed to all members of the study community in the three companies, and the researcher retrieved (93) a valid questionnaire for statistical analysis. The researcher used the descriptive analytical method in collecting data, analyzing it, answering research questions and testing their hypotheses. The most important results of the research: the presence of a statistically significant effect to support senior management and the type of support systems used on organizational creativity at the level of statistical significance (0.05) Dhofar Governorate industrial companies. And the absence of a statistically significant effect of the available capabilities of decision support systems on organizational creativity at the level of statistical significance (0.05). Among the most important recommendations: Raising the levels of creativity of workers in industrial companies through holding workshops and developing them to the best practical practices in the field of benefiting from the outputs of decision support systems. And enhancing the inputs of decision support systems in industrial companies to make new and modern contributions to the decision-making process.

An Approach Based on Mutually Informed Neural Networks to Optimize the Generalization Capabilities of Decision Support Systems Developed for Heart Failure Prediction

Available clinical methods for heart failure (HF) diagnosis are expensive and require a high-level of experts intervention. Recently, various machine learning models have been developed for the prediction of HF where most of them have an issue of over-fitting. Over-fitting occurs when machine learning based predictive models show better performance on the training data yet demonstrate a poor performance on the testing data and the other way around. Developing a machine learning model which is able to produce generalization capabilities (such that the model exhibits better performance on both the training and the testing data sets) could overall minimize the prediction errors. Hence, such prediction models could potentially be helpful to cardiologists for the effective diagnose of HF. This paper proposes a two-stage decision support system to overcome the over-fitting issue and to optimize the generalization factor. The first stage uses a mutual information based statistical model while the second stage uses a neural network. We applied our approach to the HF subset of publicly available Cleveland heart disease database. Our experimental results show that the proposed decision support system has optimized the generalization capabilities and has reduced the mean percent error (MPE) to 8.8% which is significantly less than the recently published studies. In addition, our model exhibits a 93.33% accuracy rate which is higher than twenty eight recently developed HF risk prediction models that achieved accuracy in the range of 57.85% to 92.31%. We can hope that our decision support system will be helpful to cardiologists if deployed in clinical setup.

Toward building a 3D Web-based spatial decision framework for apartment selection

In the last few decades, there has been a growing interest in effectively incorporating the analytic modeling capabilities of decision support systems and the spatial modeling capabilities of geospatial information systems to solve complex spatial decision-making problems in various fields. Spatial decision support systems assist decision makers in exploring, structuring, and generating solutions for complicated spatial decision problems such as apartment selection. The selection of an apartment is a decision which plays an important role in human life. The good location is the critical factor that affects the value and potential of a real estate. This emphasizes the significance of spatial factors in decision making in real estate business. The spatial accessibility value of each apartment to different service categories can be used while choosing the most suitable apartment. Hence, the study covers not only non-spatial aspects, for example, unit price, house size, and number of rooms, but also spatial aspects, such as spatial accessibility, of the apartment selection. To sum up, this study proposes a spatial decision framework, called EMEKLI, to facilitate the decision-making process for the selection of an apartment in the presence of different priorities and uncertainties among the decision criteria. Furthermore, the recommendations obtained from the decision-making process are shared with the decision makers in the 3D environment through a virtual globe.

Knowledge Acquisition for Electronic Health Records on cloud

Every Electronic Health Record (EHR) System has functionalities which protect patient’s data and drug related information prescribed at the time of medical diagnosis. Decision support system capabilities of the system provide necessary paperless handling support for the health information of a large number of patients. The extensive search on the practice of medical data indicates that there is a steep trend in the electronic health record usage. There is quite a number of EHR software available which are used as a Service globally, but many practitioners are not aware of selecting the right EHR SaaS product. Hence, there is a need to identify the best EHR SaaS from a pool of Software service products. To enhance the search of getting the best possible EHR SaaS product in this paper, researchers have adapted the novel way of using predefined attributes of these products and finding the relationship between them. Further, they are grouped into clusters depending on the rating, customer reviews, record of attested customers, eligible user professionals are guided in selecting a particular EHR SaaS.

Integration of Data Analytics and Mathematical Modellings

Decision support data management system requires the representation and manipulation of both data and mathematical models, when data modelling features are small ,it represents the mathematical relationships among the elements of the domain .Mathematical Techniques enhances the capabilities of decision support system, when data are larger in number, Mathematical modelling Techniques lack to many of the facilities, qualitative relationships among the covariates Data Analytics provide a way for the qualitative relationships among the covariates. In this paper, Data Analytics techniques are applied to Medissor data set, and it is shown that it convinces the qualitative representation of the Decision Support data Management system.

Managed Aquifer Recharge As a Key Element in Sonora River Basin Management, Mexico

AbstractArid regions frequently have competing demands for scarce water mainly for urban supply and irrigation. This paper presents a decision support system (DSS) as an instrument to find the optimal scheme for management of water resources from multiple supplies using managed aquifer recharge (MAR). The DSS capabilities are demonstrated through its application to the Sonora River basin. The watershed is located in Northern Mexico and has a wide variety of agricultural zones and many small towns along the river. The most important urban center is the city of Hermosillo with a concentrated water demand for urban and industrial uses. The DSS includes the hydrological modeling of the entire system, stream flow in rivers, the groundwater-flow model including MAR, and the characterization of the water quality from various sources. The result from this study establishes that it will be difficult to satisfy the increasing demand for water using the current supply-and-demand-allocation schemes. Alternative sourc...

A Fuzzy Based Decision Support System for Evaluating Land Suitability and Selecting Crops

Problem statement: Evaluating land suitability and selecting crops in modern agriculture is of critical importance to every organization. This is because the narrower area of land, the more effectiveness in planting is required in accordance with the desires of the land. Process of evaluating land suitability class and selecting plants in accordance with decision marker's requirements is complex and unstructured. Approach: This study presented a fuzzy-based Decision Support System (DSS) for evaluating land suitability and selecting crops to be planted. A fuzzy rules was developed for evaluating land suitability and selecting the appropriate crops to be planted considering the decision maker's requirements in crops selection with the efficient use of the powerful reasoning and explanation capabilities of DSS. The idea of letting the problem to be solved determines the method to be used was incorporated into the DSS development. Results: As a result, effective decisions can be made for land suitability evaluation and crop selection problem. An example was presented to demonstrate the applicability of the proposed DSS for solving the problem of evaluating land suitability and selecting crops in real world situations. Conclusion: Fuzzy based model can represent and manipulate agriculture knowledge that is incomplete or vague and it can be used to determine land limitation rating. The rating value was used to determine limitation level of the land and used to determine what the most suitable crops to cultivate for the existing condition of the land.

Decision Making with a KGP Agent System

A human user is centrally involved in a Decision Support System (DSS) task, whereas for an autonomous agent system, a user’s role is a more abstract notion of oversight and approval. In this paper, we examine the potential integration of a multi-agent architecture into a DSS. The motivation in this work is to utilise autonomous agent problemsolvers within a suitable multi-agent framework in such a way as to realise and generalise the capabilities of a DSS, while maintaining the DSS characteristic of significant user involvement. The approach taken is to employ agents both as domain problem solvers as well as to manage the interaction with the user as part of the semi-autonomous design. A framework for agent-enhanced DSS is presented which is more general than those presented previously. We show how the architectural framework used for KGP agents can be specialised to realise enhanced DSS capabilities.

Evaluating the Impact of DSS, Cognitive Effort, and Incentives on Strategy Selection

Decision support system (DSS) researchers have long debated whether or not the provision of a DSS would lead to greater decision-making effectiveness, efficiency, or both. The work described in this paper examines how DSS designers can guide users towards employing more normative decision strategies. Working from notions of restrictiveness and decisional guidance (Silver 1990) supplemented by the cost-benefit framework of cognition, we explain how DSS capabilities influence decision behavior and performance through the manipulation of effort. The results of this work should assist DSS developers to devise directed or nondirected approaches to effect desired behaviors.

Asymmetric information A laboratory experimental analysis

Modern information systems technology, including expanded availability of local area networks, provides potentially fertile means for studying information related problems across functional business areas. Our work demonstrates the development and use of a networked system designed to investigate a traditionally controversial problem in financial markets, the impact of insider information. The research methodology used to tackle this issue incorporates the use of a networked system that we developed to conduct carefully controlled laboratory experiments. We emphasize this methodology as an alternative to techniques using recorded historical market data. The latter approaches are limited by an inability to accurately identify which traders possess and access inside information. Our system incorporates a communication and recording network among work stations with each station possessing stand alone decision support system capabilities. We demonstrate use of the system and present the results and accompanying analyses of a series of experiments relating to impacts of inside information.

XPLanner: A knowledge-based decision support system for facility management and planning

A case study is reported of the design, implementation, and evaluation of a knowledge-based decision support system, XPLanner. XPLanner integrates an expert system with optimization modeling technique, database management system, and interactive user interface to create a comprehensive decision aid for facility management and planning by the US Army. It is believed that integrating the expert system with the modeling and data management capabilities of decision support systems can create a computer system which effectively supports the combination of rational and heuristic paradigms which characterize the problem-solving approach to planning.