Decision Support System For Management Research Articles

IMPACT OF POTENTIALLY SOIL MINERALIZABLE NITROGEN (PMN) ON SOIL HEALTH AND CROP PRODUCTION

Potentially Soil Mineralizable Nitrogen (PMN) is critical in soil health and crop production. This review paper explores the impact of PMN on soil health, crop production, different soil types, and mitigation strategies. It highlights the importance of PMN in enhancing nutrient availability, crop yield, and quality. The paper discusses the role of PMN in promoting soil organic matter accumulation, supporting microbial activity, and improving soil physical properties. Case studies illustrate the influence of PMN on different soil types and associated crops. Mitigation and management strategies are discussed, such as soil organic matter management, precision nutrient management, and crop rotation/diversification. The review also identifies future research directions, including refining PMN measurement techniques, integrating into nutrient management decision support systems, and exploring PMN interactions with other soil properties and environmental factors. Overall, effective PMN management is crucial for sustainable agriculture, and further research and collaboration are needed to advance our understanding and develop practical strategies for its implementation.

Monitoring-based adaptive water level thresholds for bridge scour risk management

Riverbed scour is the leading cause of bridge failure worldwide. Recent developments in sensor technology for structures have resulted in more bridges being instrumented and monitored. However, alongside scour monitoring systems, there is a need of techniques to handle the data obtained and exploit them to inform the management of the bridge scour risk. This paper illustrates the development of a decision support system (DSS) for bridge scour risk management, which is based on a probabilistic framework for scour risk estimation, enhanced by real-time information from scour monitoring systems and in line with current risk procedures used by transport agencies. The proposed DSS provides bridge operators with adaptive measurement-informed water level thresholds triggering bridge closure to traffic under heavy floods. The application of the DSS is illustrated by considering a case study of three bridges at risk of scour managed by Transport Scotland. It is shown that information from scour sensors within the proposed DSS allows reducing the uncertainty in the scour estimates and yields adaptive water level thresholds triggering bridge closure to traffic that can differ significantly from those currently considered by transport agencies. This can ultimately result in a reduction of false alarms and unnecessary bridge closures.

APPLICATION OF DIGITAL TECHNOLOGIES IN THE DELIVERY OF CARGO IN INTERNATIONAL TRAFFIC

The article analyzes the current state of the theory and practice of solving issues related to the delivery of goods by road in international traffic. It is determined that increasing the efficiency of cargo transportation in international traffic is possible through the development and implementation of new information systems and the development of innovative software products to solve the problems of planning and operational management of the transport process. The purpose of this article is to develop an innovative integrated management decision support system for organizing and managing the delivery of goods in international traffic based on the combination of cloud technologies for centralized management of real-time information exchange between participants in the transportation process. The exchange of information between participants in the process of cargo delivery in international traffic using a software product allows to improve the provision of transportation services to the client, increase the level of information exchange, implement real-time management of the cargo transportation process, improve the efficiency of decision-making and immediately respond to any changes in the process. The article develops an integrated management decision support system for organizing and managing the delivery of goods in international traffic, based on the approach of combining cloud technologies for centralized management of real-time information exchange between participants in the transportation process. The application of the proposed system with the use of appropriate software will improve the quality of transportation services to the client, increase the level of information exchange, implement real-time management of the cargo delivery process and improve the efficiency of management decision-making. As criteria for assessing the feasibility of using the proposed system, it is customary to consider indicators that are critical to maintaining the required level of sustainability and service and most fully characterize the results of the work performed from the point of view of both the consumer and the carrier. Keywords: digital technologies, cargo delivery, international transportation, efficiency.

Assessing Relativeness in the Provision of Urban Ecosystem Services: Better Comparison Methods for Improved Well-Being

In this study, we evaluated alternative methods for comparing the provision of ecosystem services among urban areas, stressing how the choice of comparison method affects the ability to compare the ecosystem service outcomes, in order to improve the management actions in urban green areas, reduce environmental inequality, and ensure satisfactory levels of human well-being. For the analysis, ten spatial indicators were quantified to assess the provision of urban ecosystem services in Barcelona, Spain, and Santiago, Chile. Two comparison methods were applied in both cities to evaluate the differences in their provision scores. The analysis was performed using the Ecosystem Management Decision Support (EMDS) system, a spatially enabled decision support framework for environmental management. The results depicted changes in the values of the provision of ecosystem services depending on the methodological approach applied. When the data were analysed separately for each city, both cities registered a wide range of provision values across the city districts, varying from very low to very high values. However, when the analysis was based on the data for both cities, the provision scores in Santiago decreased, while they increased in Barcelona, showing relativeness and a discrepancy in their provisions, hindering an appropriate planning definition. Our results emphasise the importance of the choice of comparison approach in the analyses of urban ecosystem services and the need for further studies on these comparison methods.

A knowledge-based decision support system for incident traffic congestion management

A Knowledge-based Decision Support System (KB-DSS) based on a multi-stage Fuzzy Logic Controller (MS-FLC) is developed for traffic congestion management on expressways. The MS-FLC receives real-time traffic and incident data to analyse and anticipate the traffic conditions, to recommend alternative control measures in the form of natural languages for the human operator to select control decisions, and to calculate control settings to manage traffic congestion. In a case study, the KB-DSS is evaluated on a simulated network in comparison to ALINEA\Q, a popular ramp control method, across various traffic and incident situations. The results showed that: (i) the KB-DSS provides a systematic procedure in deriving control actions and a good capability to deliver linguistic expressions; (ii) the KB-DSS outperforms ALINEA\Q with respect to global objectives across many scenarios, attains significant improvements of mainline travel conditions and substantial reductions of ramp queues. These advantages make the KB-DSS a robust tool for traffic control for incident congestion management on expressways.

Physician Compliance With a Computerized Clinical Decision Support System for Anemia Management of Patients With End-stage Kidney Disease on Hemodialysis: Retrospective Electronic Health Record Observational Study.

Previous studies on clinical decision support systems (CDSSs) for the management of renal anemia in patients with end-stage kidney disease undergoing hemodialysis have previously focused solely on the effects of the CDSS. However, the role of physician compliance in the efficacy of the CDSS remains ill-defined. We aimed to investigate whether physician compliance was an intermediate variable between the CDSS and the management outcomes of renal anemia. We extracted the electronic health records of patients with end-stage kidney disease on hemodialysis at the Far Eastern Memorial Hospital Hemodialysis Center (FEMHHC) from 2016 to 2020. FEMHHC implemented a rule-based CDSS for the management of renal anemia in 2019. We compared the clinical outcomes of renal anemia between the pre- and post-CDSS periods using random intercept models. Hemoglobin levels of 10 to 12 g/dL were defined as the on-target range. Physician compliance was defined as the concordance of adjustments of the erythropoietin-stimulating agent (ESA) between the CDSS recommendations and the actual physician prescriptions. We included 717 eligible patients on hemodialysis (mean age 62.9, SD 11.6 years; male n=430, 59.9%) with a total of 36,091 hemoglobin measurements (average hemoglobin and on-target rate were 11.1, SD 1.4, g/dL and 59.9%, respectively). The on-target rate decreased from 61.3% (pre-CDSS) to 56.2% (post-CDSS) owing to a high hemoglobin percentage of >12 g/dL (pre: 21.5%; post: 29%). The failure rate (hemoglobin <10 g/dL) decreased from 17.2% (pre-CDSS) to 14.8% (post-CDSS). The average weekly ESA use of 5848 (SD 4211) units per week did not differ between phases. The overall concordance between CDSS recommendations and physician prescriptions was 62.3%. The CDSS concordance increased from 56.2% to 78.6%. In the adjusted random intercept model, the post-CDSS phase showed increased hemoglobin by 0.17 (95% CI 0.14-0.21) g/dL, weekly ESA by 264 (95% CI 158-371) units per week, and 3.4-fold (95% CI 3.1-3.6) increased concordance rate. However, the on-target rate (29%; odds ratio 0.71, 95% CI 0.66-0.75) and failure rate (16%; odds ratio 0.84, 95% CI 0.76-0.92) were reduced. After additional adjustments for concordance in the full models, increased hemoglobin and decreased on-target rate tended toward attenuation (from 0.17 to 0.13 g/dL and 0.71 to 0.73 g/dL, respectively). Increased ESA and decreased failure rate were completely mediated by physician compliance (from 264 to 50 units and 0.84 to 0.97, respectively). Our results confirmed that physician compliance was a complete intermediate factor accounting for the efficacy of the CDSS. The CDSS reduced failure rates of anemia management through physician compliance. Our study highlights the importance of optimizing physician compliance in the design and implementation of CDSSs to improve patient outcomes.

THE IMPACT OF MANAGEMENT ACCOUNTING PRACTICES ON OPERATIONAL PERFORMANCE IN SMES IN THE COLOMBO DISTRICT, SRI LANKA

"The objective of this study was to address the research problem of how the effect of management accounting practices influences the operation performance of SMEs in the manufacturing sector of the Colombo district. Different SMEs use financial knowledge in varying ways, which affects the performance of the businesses. This study sheds light on the Practices of management accounting used by SMEs in the manufacturing sector and how they affect Operational success. The Management accounting practices which are discussed in the study is, costing, performance evaluation, budgeting, strategic management accounting and decision support system. The data gathered was primary data, using a convenience sampling method in cross-sectional time horizon. And the deductive research methodology and the survey method were used in the research design. The Research was conducted using demographic analysis, correlation coefficient analysis, and regression analysis. The sample was taken from the small and medium-sized, 131 manufacturing enterprises in the Colombo district. The study found, the budgeting system, performance evaluation system, and strategic management accounting system to have a statistically significant impact on operational performance. Costing systems and Decision support systems were found to be not statistically significant in regression analysis. Therefore, 3 out of 5 hypotheses were accepted. As for concluding remarks, this study’s findings recommend that using management accounting practices can enhance operational performance. Further, study suggests that budgeting, performance evaluation, and strategic management accounting be used by management to improve the operational performance of Manufacturing SMEs. The study thereby, hopes to increase the knowledge and awareness of students and professionals who are interested in this field. Additionally, this will provide insight on the management accounting techniques now employed by SME's and their level of financial literacy. Consequently, this study’s valuable information can be used by government, organizations, and experts to advance the Small Medium Enterprises of Sri Lanka."

Optimization of Decision Support System in Investment Risk Management with Firefly Algorithm

Optimization of Decision Support System in Investment Risk Management with Firefly Algorithm

Refining Management of Apple Powdery Mildew in New York State with Weather-Based Fungicide Application Timing Programs.

In the absence of durable host resistance among commercial cultivars, chemical management continues to be an essential component of disease control in apple production. Apple powdery mildew, caused by the ascomycete Podosphaera leucotricha, is largely managed with regular fungicide applications from the host phenological stages of tight cluster to terminal bud growth set, with applications typically being made in a prophylactic manner irrespective of existing disease pressure. Here we evaluated two management programs that aligned fungicide applications to specific weather thresholds conducive to powdery mildew development using a rotation of single-site fungicides and sulfur. In three separate orchards among four cultivars, we compared powdery mildew disease progression over the growing season for each of the weather factor-based programs and a typical calendar-based application program. In each year of the trial, we found that management programs with weather-based fungicide applications provided levels of disease control similar to the calendar program but required 50 to 83.3% fewer mildew-specific fungicide applications throughout the growing season. Our results provide a framework with which to evaluate future weather-based management programs for apple powdery mildew management. This knowledge could be implemented in the creation of a powdery mildew disease management decision support system to better inform and aid fungicide application programs for continued sustainable apple production in the northeast United States.

A data science-based marketing decision support system for brand management

To improve the marketing activity and brand management and justify the most effective marketing decisions, organizations should implement different information technologies, mathematical methods and models into the marketing decision support system (MDSS). The goal of this paper is to form an architecture of an MDSS, the model base of which is developed on Data Science tools, in particular regression analysis and machine learning methods. The proposed MDSS is a multi-agent information system comprising nine intellectual agents (market environment monitoring, data processing, marketing mix modeling, price policy support, portfolio management, strategic analysis, forecasting, customer segmentation, and customer classification). The functionality of these agents is realized through Data Science, which allows for the optimization of marketing activities (e.g., an effective brand management strategy and its elements (portfolio strategy, price policy, and media strategy) or solving the problems of attracting new and retaining current customers with the maximal return on marketing investments). The MDSS analyzes the marketing environment, media activity, and business indicators by constructing different models and forecasting various combinations of marketing factors to select the best one. The joint work of MDSS agents provides decision-makers with interactive reports. The research findings offer a scientific basis for making effective marketing decisions based on data, and the proposed MDSS can become part of an intelligent system for planning marketing activities.

Comparing Random Forest to Bayesian Networks as nitrogen management decision support systems

AbstractNitrogen (N) is notoriously difficult to manage and there are many approaches for fertilizer N rate recommendations. Existing fertilizer N rate recommendation systems can be improved by incorporating the effects of weather on sidedress economicoptimum N rates (EONR). In this study, we evaluated the performance of machine learning methods, a Bayesian Network (BN) and a Random Forest (RF) for estimating EONR for corn. BN draws relationships between variables based on assumptions about conditional independence, where the model is structured by an algorithm or, in this case, expert opinion. In contrast, RF determines model structure based on the input variables and model output. The models were trained and validated using a large database (n = 324) of corn yield response to N fertilizer collected across southern Ontario. Sixty‐six of the 324 site‐years were used for validation with success assessed by the frequency that N rate predictions that produced net returns were within CAN$25 ha−1 of the observed EONR. The success rate was 64% and 48% for the BN and RF, respectively. Both models incorporated weather from planting to sidedress and outperformed a benchmark provincial N recommendation system. We argue that BN has advantages when some input variables are unknown or uncertain and for improving model structure with stakeholder feedback. Moreover, RF is easy to implement but the model structure must use point estimates instead of probabilities for uncertain parameter values such as future weather. BN represents a more flexible modeling approach than RF for incorporating both modeling and stakeholder input.

Applications of Industry 4.0 Technologies in Warehouse Management: A Systematic Literature Review

Background: Recent literature indicates that warehouse management costs account for a significant portion of overall logistics costs in companies. Warehousing requires the classification, controlling and management of inventory as well as processing of related information. Therefore, adopting efficient and reasonable warehouse management measures to achieve effective management and control of materials is a key means to flexibly adjusting the supply and demand of storage materials and reduce operating costs. There remains a gap in the understanding of benefits and barriers to the full adoption of Industry 4.0 technologies and decision support systems (DSSs) in warehouse management. Methods: This work applies a systematic literature review methodology of recent implementation case studies to analyze documented barriers and benefits of Industry 4.0 technology adoption in warehouse management. For analysis, benefits and barriers are ranked in order of importance using Pareto analysis based on their frequency of occurrence. Results: Improved process efficiency, the availability of real-time data, added competitive advantage and the ability to integrate business activities digitally are the top four most important benefits of implementing Industry 4.0 technologies and decision support systems in warehouse management. The prominent barriers to implementation are high life cycle cost, challenging physical environment/layout, inadequate supporting resource constraints, increased security risk and high energy consumption. Conclusions: Barriers to implementing Industry 4.0 technologies are interrelated in nature and prevent businesses from realizing the full benefit of implemented Industry 4.0 technologies. Adequate financial support, new knowledge and skills are required to be able to ensure the successful implementation of Industry 4.0 in warehousing management.

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era.

Smart agriculture is gaining a lot of attention recently, owing to technological advancement and promotion of sustainable habits. Unmanned aerial vehicles (UAVs) play a crucial role in smart agriculture by aiding in different phases of agriculture. The contribution of UAVs to sustainable and precision agriculture is a critical and challenging issue to be taken into account, particularly for smallholder farmers in order to save time and money, and improve their agricultural skills. Thence, this study targets to propose an integrated group decision-making framework to determine the best agricultural UAV. Previous studies on UAV evaluation, (i) could not model uncertainty effectively, (ii) weights of experts are not methodically determined; (iii) importance of experts and criteria types are not considered during criteria weight calculation, and (iv) personalized ranking of UAVs is lacking along with consideration to dual weight entities. Herein, nine critical selection criteria are identified, drawing upon the relevant literature and experts' opinions, and five extant UAVs are considered for evaluation. To circumvent the gaps, in this work, a new integrated framework is developed considering q-rung orthopair fuzzy numbers (q-ROFNs) for apt UAV selection. Specifically, methodical estimation of experts' weights is achieved by presenting the regret measure. Further, weighted logarithmic percentage change-driven objective weighting (LOPCOW) technique is formulated for criteria weight calculation, and an algorithm for personalized ranking of UAVs is presented with visekriterijumska optimizacija i kompromisno resenje (VIKOR) approach combined with Copeland strategy. The findings show that the foremost criteria in agricultural UAV selection are "camera," "power system," and "radar system," respectively. Further, it is inferred that the most promising UAV is the DJ AGRAS T30. Since the applicability of UAV in agriculture will get inevitable, the developed framework can be an effective decision support system for farmers, managers, policymakers, and other stakeholders.

ПРИМЕНЕНИЕ ПРАВИЛ НЕЧЕТКОЙ ЛОГИКИ ДЛЯ АНАЛИЗА ДАННЫХ И ПРИНЯТИЯ РЕШЕНИЙ ПРИ УПРАВЛЕНИИ ГРУЗОПЕРЕВОЗКАМИ В УСЛОВИЯХ НЕОПРЕДЕЛЕННОСТИ

Today, the transport services market and the transportation market play a leading role among the system-forming basic components of the transport services market. The transformation of a number of production processes, caused by the digitalization of the economy, has not left aside the management of logic. The article deals with the automation of the formation of alternative routes through the use of the developed algorithm (the mathematical support of which is based on the application of the rules of odd logic), which implements the optimization of the choice based on the customer's requests in the organization of cargo transportation. This approach will allow firms not only to manage their work processes in conditions of risk and uncertainty caused by increasing competition and external factors that are difficult to predict, including economic and geopolitical ones, but also to customize orders, while reducing risks using methods of their distribution. The article substantiates the application of this algorithm and provides a description of its implementation within one of the modules of the proposed intelligent decision support system for cargo traffic management. The purpose of the study is to describe and test the mathematical apparatus of the theory of fuzzy sets adapted to the search for cargo transportation routes, which allows automating a number of poorly formalized processes for a decision-maker in the logistics sphere. Materials and methods. Based on the analysis of scientific ideas and methodological approaches in logistics of domestic and foreign authors, as well as mathematical methods and models, the choice of tools for improving the efficiency of cargo transportation organization through automation of management processes was made. Results. The fuzzy logic apparatus has been adapted to solve logistical problems: a selection of criteria is presented, alternative routes are formulated, computational experiments implemented through the Yandex DataLens environment are described. The model was adjusted taking into account the weight coefficients of the criteria based on the customer's wishes. The analysis and synthesis of the obtained results are presented, as well as the advantages of the proposed approach, relative to the traditional ones, are described. The functionality and interface of the automated module of the system was tested with the involvement of a group of experts interested in using the development, which showed its operability and effectiveness of application. Conclusion. The proposed method of choosing alternative routes can be used as a mathematical tool for automating cargo transportation management in decision support systems, especially when planning activities by small firms using their own fleet.

An Overview on Decision Support Systems for Risk Management in Emergency Conditions: Present, Past and Future Trends

An Overview on Decision Support Systems for Risk Management in Emergency Conditions: Present, Past and Future Trends

PROCESS MODEL FOR THE THEORETICAL FRAMEWORK OF MANAGEMENT DECISIONS SUPPORT SYSTEMS

The article examines how the correct use of management decision-making tools helps enterprises achieve their short- and long-term goals. This will allow managers to make balanced decisions that will primarily benefit the enterprise or organization. It will also allow a comprehensive approach to solving the problems that arise in their activities. It has been established that the management decision support system is a multifunctional tool that requires detailed analysis in high-quality journals. The history of the management decision support system was analyzed. It was found that the vast majority of domestic scientists research applied aspects of developments in the field of management decision support systems. And the first scientific works were based on experimental research. It is noted that the first experimental studies were based on creating a decision-making environment. The need to introduce a management decision support system into the activities of institutions and organizations has been revealed. A process model of the theoretical foundations of the management decision support system and strategies for improving the research of the management decision support system are proposed. Its main components are described, and the main implementation problems are identified. It has been established that any constructed model or process model of management decision support system theoretical foundations contains appropriate components that must be applied comprehensively. These components have their own characteristics and are used in different situations. For their application, it is necessary to implement actions of a different nature. First of all, it is obtaining timely, complete, and high-quality information through the interaction of the software complex with available management information. A management decision support system requires a significant number of experts in the chosen field of knowledge who can manage data and process it in large quantities. Accordingly, the more complete the information, the greater the chance of making the right management decision, which can affect the further activities of the enterprise or organization.

Design and implementation of a decision support system for flood management

This work deals with the design and implementation of a decision support system for flood management. In the society today emergency issues arises due to terrorist attack, flood disaster, epidemics, widespread massive riot etc. In other to handle this situation government has established a number of agents supported by Non-governmental organization (NGO). Due to non-existence of integrated database and communication support system for managing flood emergency has resulted to poor handling, huge losses and reoccurrences. This work involves managing the inter operations/ interlinks of flood incidents sources by the handling agents, design of database report format and communication media for the system automation. In other to solve this problem waterfall methodology was adopted in this study. The programming tool adopted was adobe dreamer. Its host has front end and back end. The front end is the user interface and serves as a channel through which users communicates to the system. Adobe dreamer being the tools used for building such web-based application supports many mark-up languages like HTML, CSS and Java script. The most common available tools for the development of this system is the Adobe Dream waver which is IDE (integrated Development Environment. It is a creation management tool and requires no programming language and good enough for experienced website developers.

Математическая модель интеллектуальной системы поддержки принятия управленческих решений в энергосистемах

The article considers the problem of taking into account the information uncertainty for building knowledge bases in decision support systems for the operational management of the electric power system modes on the example of the Pridnestrovian Moldavian Republic power system. As part of the study, the categorical method “Universal Interaction Scheme” is used, because it allows building a conceptual model of the Pridnestrovian Moldavian Republic power systems necessary for the further development of a mathematical model. To create a mathematical model of an intelligent management decision support system in power systems, the Decision-Support System knowledge base method based on fuzzy logic is used. It is substantiated that formulating the control logic is carried out mainly at the level of qualitative representations and concepts. The latter can be formalised in terms of logical-linguistic models, which should be considered from the viewpoint of the theory of fuzzy sets and linguistic variables. The analysis of existing approaches to representing and processing fuzzy knowledge about the power system management is carried out. An approach to demonstrating the incorporation of various forms of professional ontology representation based on fuzzy logic is substantiated. The research methods involve using the apparatus of fuzzy logic, formal languages, the theory of artificial intelligence systems and decision support systems.

Forage economics calculator web tool: A decision support system for forage management

Economic analysis of forage production and handling aids in making an educated decision related to growing or buying forage, setting forage prices, and purchasing additional equipment. Manual calculation of economics is highly complex, tedious, and time-consuming. Therefore, a multi-device web-based tool, called the “forage economics calculator web tool” (FECWT) was developed using HTML, CSS, and JavaScript. The tool’s interface was designed for a user-friendly experience with easy access to commands set in an easy-to-follow layout. The FECWT uses 29 input variables to generate results “dynamically” for 37 output variables, based either on the supplied default data or with user-provided inputs (imperial or metric units). The tool emphasizes the cost of the bale collection activity performed using tractor or tractor pulling an “automatic bale picker” (ABP), while the cost of other activities (planting, fertilizer & chemical application, harvesting, and baling) are provided as direct inputs. The economic analysis outputs include net return, break-even ratio, payback period, and return on investment. Features of FECWT include the sensitivity of profit to changes in input values, and downloadable reports and charts for record-keeping. Two case studies using farmers’ actual field data inputs demonstrated the FECWT’s effectiveness, with its generated results corroborating well with farmers’ records. The tool allows for “what-if” scenario analysis related to equipment purchase, effect of forage crop, net-return, and low-inputs scenario for different field areas suggests that an ABP purchase for field areas greater than ≥65ha (≥160ac) could be profitable; alfalfa and corn were the most profitable forage and grain-forage crops, where the grain revenue stream of grain-forage crops easily covers the costs and makes a profit; and a low-inputs scenario (no fertilizers, chemicals, and/or seeds) for forages (alfalfa and grass) decreased the net returns on average by 8.1 times at 50% forage yield potential suggesting the use of all inputs and increased field areas (≥65ha) make a better profit in this low-inputs scenario. Potential users of this web tool include farmers, hay producers, custom hay operators, educators, agricultural extension and financial personnel, and others interested in the economics of handling bales.

Design and implementation of a decision support system for college student management based on the 5G communication system

Design and implementation of a decision support system for college student management based on the 5G communication system