Resource Management System Research Articles

The impact of artificial intelligence on human resource management systems - Applications and risks

Organizations traditional human resource management model has been impacted by the ongoing optimization and advancement of artificial intelligence skills and technology, and the broadening of its application scope. The impact of artificial intelligence (AI) systems on employee recruitment, human resources allocations, and talent management is significant. This paper examines the interplay among AI, data applications, human resource management (HRM) systems and the resultant effects. It will examine the significance of effectively managing the deployment of AI systems, as existing literature defines. This study examines the effects of artificial intelligence AI technology on the effectiveness of company administration compared to traditional human resource management systems (HRMS). Several recommendations are offered to enhance the reformation and optimization of the organizations human resources (HR) division. The research findings indicate that incorporating the new system in conjunction with human involvement can significantly enhance the efficiency of employee recruitment, allocation of human resources, and management of talent within the firm. There was an improvement observed in both employee happiness and productivity elements.

Building cooperative and high-performance organizations with high-performance work systems: the role of firm age and industry dynamism

Although numerous empirical studies have linked human resource (HR) management systems, including systematic HR practices, such as high-performance work systems (HPWS), to superior organizational performance, our understanding of how these benefits vary among firms operating under different levels of environmental uncertainty is limited. By integrating insights from organizational theory, strategy, and strategic human resource management (SHRM), I theorize that an organization’s internal and external environments—specifically firm age and industry dynamism—modulate the benefits derived from HPWS. Utilizing longitudinal data encompassing 1,585 firm-year observations across five waves from 2009 to 2017, I demonstrate that the positive impact of HPWS on organizational performance is most pronounced for younger firms in highly dynamic industries. Conversely, for older firms, this positive effect weakens as industry dynamism increases. Additionally, I discovered that cooperative norms serve as a crucial mediating mechanism. My findings mark a significant advance in our understanding of when and how firms, amidst varying degrees of environmental uncertainty, reap differential benefits from HPWS—especially through the mediation of cooperative norms.

ANALISIS SWOT TERHADAP PENERAPAN SUMBER DAYA MANUSIA DAN DAMPAKNYA TERHADAP PENINGKATAN KINERJA KARYAWAN DI UD. JAYA MULYA MOTOR

Human resources have an impact on an organization's efforts to achieve its goals. To achieve the desired goals, the company needs good management of its system. Therefore, a strategy is needed for this management, and one way to determine this strategy is to use SWOT analysis, which is an indication of various factors that are systematically formulated to develop a company's strategy. One company that has implemented human resource management is UD. JAYA MULYA MOTOR in Surabaya. This company, led by Mr. Usman Maulana, provides products in the scrap metal trading industry and provides car spare parts. Violations committed by several employees have become a constraint. The low level of awareness of a employee's responsibility has made the company have to dismiss and replace employees who have met the criteria for dismissal. This study is titled "The Effectiveness of Human Resource Management in Efforts to Improve Employee Performance at UD. JAYA MULYA MOTOR." The aim is to find out how the human resource management system applied by UD. JAYA MULYA MOTOR and to explain the strategy using SWOT analysis

Sustainability of native maize family farms in Mexico: Current trends from a socio-agronomic perspective

The sustainability of family farms, or Family Production Units (FPUs), cultivating native maize varieties in Chiapas, Mexico, is intricately linked to economic, socio-productive, environmental, and cultural factors. Given their significance within the regional socio-productive framework, the objective of this study was to assess sustainability across environmental, social, and economic dimensions. The methodology employed for sustainability analysis was the Framework for Evaluation of Natural Resource Management Systems (MESMIS). Thirty FPUs were purposively selected from 14 communities spanning four municipalities and were typologically classified into three groups: Traditional, Maize Growers-Livestockers, and Diversified. In-depth interviews were conducted with individual farmers, complemented by group interviews involving entire families. The collected data underwent processing through multiple correspondence statistical analysis, analysis of variance for linear models, and multiple comparisons of means. The results indicated that the Diversified FPU group exhibited higher sustainability, covering 68% of the sustainability perimeter. Consequently, this group demonstrated better conditions for preserving native maize varieties over time and developing strategies to meet their needs concurrently. The Traditional FPU group covered 58%, while the Maize Growers-Livestockers group covered 52% of the sustainability perimeter. In conclusion, it was observed that family reproduction strategies, grounded in resource utilization (capitals), shape forms of nature appropriation that are continually reconfigured. These configurations play a pivotal role in defining the sustainability of FPUs engaged in native maize cultivation in the Frailesca region.

Distributed Energy Resources Management System (DERMS) and Its Coordination with Transmission System: A Review and Co-Simulation

Ever-increasing penetration of distributed energy resources (DERs) in the power grids, alongside their numerous benefits, brings new challenges that call for enhanced solutions in the field of control and management of power grids. The majority of the available research have considered either distribution or transmission grids in their studies. In this paper, a comprehensive review of the effects of DERs on the distribution and transmission grids is performed. The focus of this paper is on hierarchical management methods in order to categorize different approaches and highlight the gaps. Moreover, a review is conducted in the field of the newly introduced distributed energy resources management system (DERMS) concept. A DERMS can facilitate the hierarchical energy management procedure due to its functionalities and broad capabilities. Hence, its implementation in energy management and its impact on the power grid will be assessed with the aid of a co-simulation platform that considers both transmission and distribution grids.

Graph-based meta-learning for context-aware sensor management in nonlinear safety-critical environments

This study introduces a novel framework for optimizing energy efficiency and computational load in safety-critical robotic systems operating in nonlinear domains. Leveraging Graph Attention Networks for state awareness and decision-making, the framework employs adaptive sensor and filter toggling strategies to dynamically manage system resources through real-time inferential processes. Our framework maintains continuous robot operation in the presence of sensor noise and environmental disturbances by activating additional sensors, thus preventing system shutdowns or stalls. Few-shot meta-learning techniques further augment the model's adaptability, allowing it to generalize and make real-time decisions across varying operational conditions. An extensive evaluation reveals a reduced average energy consumption, compared to ‘always-on’ configurations, by 13.71% and CPU utilization by 29.07%, without compromising system performance and safety. We also introduce Matching Networks and Siamese Networks with different loss functions to assess the system's capability to adapt to different levels of criticality. Our experiments demonstrate that the system prioritizes performance and safety in high-critical scenarios while maximizing energy efficiency in less critical situations. The framework's real-time decision-making capability is particularly crucial in human–robot environments and holds significant implications for future applications in nonlinear control systems and resilient robotic systems.

Opportunities and Challenges in AI Based Modern Power System

Nowadays, the power grid has transformed into a dynamic and extensive resource generation and management system. This transformation is primarily driven by the widespread adoption of renewable energy sources and the utilization of intelligent information and communication technologies to handle dynamic workloads. The smart grid encompasses various innovative operations, including power electrification, intelligent information integration at the physical layer, and intricate interconnections. These operations leverage data-driven deep learning, big data, and machine learning paradigms to effectively analyze and control transient issues within the electric power system. Artificial intelligence (AI) has emerged as a crucial tool in addressing and resolving challenges associated with transient stability assessment (TSA) and power generation control. In this research paper, we present a comprehensive review that explores the role of AI and its sub-procedures in tackling TSA problems. The article outlines an AI-based intelligent power system structure, along with the rationality of applying AI to transient situations in power system TSA. Distinguishing itself from other reviews, this paper delves into the AI-based TSA framework and design process, highlighting intelligent applications and their analytical capabilities in addressing power system transient problems. Furthermore, our analysis extends beyond AI alone, as we also explore the integration of big data, which aligns seamlessly with AI. The paper discusses future trends, opportunities, challenges, and open issues pertaining to AI-Big data based transient stability assessment in the smart power grid.

How to measure employees’ interests so as to be a more socially-responsible employer: A proposal of a new scale and its validation

Research background: Many authors emphasize that successful human resource management (HRM) practices align with employees’ needs associated with the construct of employees’ interests. In particular, the importance of considering employees’ interests is emphasized in the process of shaping the architecture of Socially Responsible Human Resource Management (SR-HRM) systems. Purpose of the article: The aim of the article is to contribute to understanding employees’ interests by designing and validating a measure to recognize these interests. Methods: Through the use of literature sources and expert opinions, the authors developed a list of employee interests. Empirical data collected via the survey method in Poland was used to statistically verify the measurement scale. In particular, exploratory factor analysis and exploratory structural equation modelling were applied. Findings & value added: This article shows that it is important to create a comprehensive list of interests, as well as validate the research tool used. The newly developed scale has 22 items and five dimensions: support and development at the level of the enterprise, employee participation, support and development at the departmental level, employment security, working conditions and remuneration. It may be used in a variety of companies, as well as in complex research models, and developed further taking into consideration the context of other countries.

Evaluating prioritization of strategic business model for efficient wastewater resource management system

In recent decades, the world has witnessed a sudden rise in population and rapid urbanization. This has led to an increase in the water demand and consequently an upsurge in wastewater. Water scarcity, coupled with untreated wastewater, has become a major concern throughout the world. Municipalities are challenged with the task of treating wastewater and meeting domestic, agricultural, and industrial demands. Mismanagement of wastewater results in economic, social and environmental loss. The high concentration of population, lack of sanitation and water security, reduced awareness, and evident pollution have made India a home full of opportunity and innovation in the wastewater management (WWM) business. The paper gives a comprehensive review to understand the wastewater management and business approach. The research uses Total Interpretive Structural Modelling (TISM) to identify, acknowledge, and interpret the major factors for the stakeholders and for future companies who wish to venture into the wastewater management business. The study provides insight into developing a circular economy solution and gives a vivid framework highlighting the relevant factors for a successful WWM business. The study also provides direction to the practitioners who are involved in the WWM industry and helps evaluate their options, prioritizing resource-efficient solutions.

Intelligent Application of Digital Shared Management System in Journal Resource Integration

In this paper, a journal resource integration system under digital sharing was designed to assess the symmetry of the journal resource matrix using the shared resource matrix analysis method, so that the journal resources can transmit information covertly among the subjects. Subsequently, the collected journal resource data were processed to improve the accuracy of information categorization and integration to form a perfect resource management system. The nature of journal resources was deeply understood by calculating the characteristic observable vectors of journal resources. Finally, with the help of ant colony particle optimization algorithm, the sharing sequence and time of journal resources were calculated, and the metadata format of all journals was unified and standardized to complete the design of journal resource integration system. The results show that the whole average integration efficiency of journal resources is 92.8%. Compared with the traditional integration system, the integration efficiency is improved by 28.3%, which fully confirms the significant performance improvement of the designed digital system in the integration of shared journal resources.

재난 위험 및 자원 운영·관리 시스템

In this study, we present a disaster situation and response resource management system. This system aims to proactively prepare for and respond to both single and complex disaster scenarios within a city. Additionally, we propose strategies for the effective operation of such resource management systems during emergencies. Notably, we leverage blockchain technology to enhance the transparency of disaster management resource operations and the overall system. While our research successfully designed and developed a disaster resource management system using 4th industrial revolution technologies, practical considerations remain. This process is critical not only for the secure operation of disaster management resources but also for the overall usability of the prototype system, especially when integrating it with government departments and management agencies. The feasibility of blockchain implementation in this context needs further review and evaluation. Future studies will focus on refining the prototype system to enhance its performance and ultimately create a practical solution for real-world disaster response.

Addressing Issues and Challenges in Disaster Emergency Resource Management: Practical Improvement Strategies for Shandong Province, China

This study aims to identify the issues and challenges within Shandong's disaster emergency resource management system and propose practical improvement plans to enhance its efficiency and responsiveness, thereby reducing the impact of disasters. In Shandong province, several key issues are identified in the management of disaster emergency resources: suboptimal management system for emergency resource reserves; structural deficiencies in emergency resource reserves; the gap in professional talent: challenges and strategies; limited legal oversight in management systems; and inadequate rotation of emergency resources. To address these challenges, we suggest practical improvement strategies: enhancing the reserve management system; strengthening the foundations of legal framework development; enhancing the efficacy of talent development programs; optimizing the rotation system for emergency resource management; advancements in infrastructure development strategies.

Artificial Intelligence In Human Resource Management of Organizations

Relevance. Digitalization covers all spheres of our life, the pace of development of information technologies is accelerating – their importance in modern society is increasing. There is an active introduction of modern technologies into various business processes, including personnel management, which can change the human resource management system we are used to. The field of artificial intelligence development is not left behind, impressing the whole world with the pace of development in many areas, including work with personnel: recruitment, hiring and training. The purpose of the article is to study the introduction of digital technologies in personnel management. Objectives: to study the directions of artificial intelligence development in economics and management; to consider the history of the origin of the idea of such an application of artificial intelligence tools; to assess the interest in artificial intelligence in the field of human resource management; to consider the opinions of leading experts on the successes and prospects of the introduction of artificial intelligence in human resource management. Methodology. In the course of scientific research, empirical, theoretical, statistical methods and methods of graphical representation were used. Results. The theoretical aspects of the application of artificial intelligence technologies were studied; the popularity of this topic in science and business was shown. The experience of using artificial intelligence in human resource management of large global and Russian companies is summarized and the prospects for the development of this direction are shown. Examples of the positive effect of the introduction of artificial intelligence in the field of human resource management are shown. Conclusions. The article emphasizes the relevance and importance of the topic under consideration, taking into account the latest trends, perspectives and views of leading analysts. The possibilities that artificial intelligence technologies open up in the field of human resources are considered.

Exploration of Human Resource Management in Enterprise Business Management

Human resource management is an integral part of enterprise operation and management, contributing to the avoidance of financial risks, alleviation of performance problems, promotion of organizational development, enhancement of operational efficiency, and fostering sustainable enterprise development. Enterprise management should prioritize attention to human resource management by establishing an independent human resource management department and strategically planning human resource management tasks. This involves building an information management platform, developing a professional human resource management model to mitigate market risks, actively recruiting human resource management talents to form a competent team, and enhancing the human resource management and supervision system. These measures are essential for comprehensively improving the level of enterprise operation and management.

Enhancing Coal Mining Efficiency

Coal is a prominent energy resource for several countries. Of late, exploring the automatic management and control of coal mining has been a significant task. This article presents a framework for a mine-wide integrated automation management and control platform with the goal of advancing coal mining through unified data, models, platforms, and plans. Utilizing cutting-edge technologies, the platform offers resource management, real-time monitoring, remote control, statistical analysis, and intelligent alarm systems. Data access design ensures standardized data collection and exchange, fostering interoperability. A big data storage center manages heterogeneous data sources. The platform interface design emphasizes flexibility and scalability through containerized applications and microservices frameworks, streamlining deployment. The functional design encompasses subsystem configuration access, real-time monitoring, remote access, etc. A detailed evaluation is presented to demonstrate the significance of the proposed platform in terms of functionality, performance, and scalability.

Identifying Failure Factors in the Implementation of Residential Complex Projects in Iraq

Residential complexes have witnessed a great demand in most countries worldwide, as they are one of the main infrastructure elements, in addition to achieving a developed urban landscape. However, complex residential projects in developing countries face various factors that could be improved in their implementation, especially in Iraq. Sixty-two experts in residential complex projects were interviewed and surveyed to verify these projects' failure factors,. Fifty-one factors were the main failure factors, divided into four main components (leadership, management system, external forces, and project resources). The Relatively Important Index (RII) is used to determine the relative importance factors and obtain the top twelve factors that cause the optimal failure factors, correspondingly: political interference (87.7%), change in governments (86.5%), partisan politics available (85.6%), Unclear strategic planning (84.3%), inadequate planning (84.3%), lack of materials and equipment (83.3%), the poor obligation for project leaders (82.6%), work suspension due to demonstrations (82.3%), lack of human capacity (81.8%), bureaucracy (81.7%), corruption (81.4%), and misalignment in culture or ethics (81.3%). The achieved RII readings were high for leadership (73.1%), followed by project resources (71.3%), management systems (70.5%), and external forces (69.9%). The study recommended addressing the problems of implementing complex residential projects and solving the crisis to start the state's economic growth phase according to new foundations and standards.

Real-Time Evaluation of the Uncertainty in Weather Forecasts Through Machine Learning-Based Models

Meteorological events have always been of great interest because they have influenced everyday activities in critical areas, such as water resource management systems. Weather forecasts are solved with numerical weather prediction models. However, it sometimes leads to unsatisfactory performance due to the inappropriate setting of the initial state. Precipitation forecasting is essential for water resource management in semi-arid climate and seasonal rainfall areas such as the Ebro basin. This research aims to improve the estimation of the uncertainty associated with real-time precipitation predictions presenting a machine learning-based method to evaluate the uncertainty of a weather forecast obtained by the Weather Research and Forecasting model. We use a model trained with ground-truth data from the Confederación Hidrográfica del Ebro, and WRF forecast results to compute uncertainty. Experimental results show that Decision Tree-based ensemble methods get the lowest generalization error. Prediction models studied have above 90% accuracy, and root mean square error has similar results compared to those obtained with the ground truth data. Random Forest presents a difference of -0.001 concerning the 0.535 obtained with the ground truth data. Generally, using the ML-based model offers good results with robust performance over more traditional forms for uncertainty calculation and an effective alternative for real-time computation.

From District to City Scale: The Potential of Water-Sensitive Urban Design (WSUD)

The summer of 2022 was one of the hottest and driest summers that Germany experienced in the 21st century. Water levels in rivers sank dramatically with many dams and reservoirs running dry; as a result, fields could not be irrigated sufficiently, and even power generation and supply were affected. The impact of abnormally high temperatures for extended periods (heatwaves) is not restricted to nature and the economy but is also a considerable public health burden. Experts worldwide agree that these extreme weather events are being driven by climate change and will increase in intensity and frequency in the future. The adverse impact of these extreme weather events multiplies among dense urban environments, e.g., through heat islands. This calls for cities to take action to heat-proof and water-secure their urban developments. Water-Sensitive Urban Design (WSUD) is one such approach to mitigate the aforementioned challenges by leveraging the urban water ecosystem with special attention to the subject of water reclamation, retention, treatment and distribution. This paper introduces and builds upon a prototype of WSUD that centers around an artificial lake as an integrated water resource management system (IWRMS) fed by treated grey water and storm water obtained from two housing blocks flanking the water reservoir. Based on the specifications of this prototype, indicators of site suitability are derived and applied to identify potential locations for replicable projects in the city of Darmstadt. The results confirm the impact WSUD can have: a total of 22 sites with 2527 apartments are found suitable for prototype implementation in Darmstadt. Savings in town water consumption from these 22 sites would add up to 147 million liters. Further benefits include the provision of 24 million liters of irrigation water, storm water retention, adiabatic cooling during heatwave, increased biodiversity and the improvement in livability of the sites and the city.

A user acceptance model for e-HRM system implemented in IT and Non-IT business organizations

BACKGROUND: Nowadays organizations have made their human resource management process more strategic and serve as a strategic tool for achieving competitive advantage for the organization with the development of electronic-Human Resource Management (e-HRM) Systems. To measure e-HRM system acceptance for performing various HR functions of an organization, especially the HR department, a perception model for acceptance of e-HRM systems in IT and Non-IT organizations has been developed in this research paper. OBJECTIVE: The objective of the study is to derive an acceptance model that can predict the acceptability and the factors affecting the acceptance of e-HRM systems in IT and Non-IT organizations. For this purpose, an electronic-survey-based questionnaire with a set of questions with categorical response options has been developed and the responses collected through this questionnaire are eligible to measure the acceptance level with effectiveness and usefulness of the e-HRM systems by IT and Non-IT business organizations. METHODS: Here, the questionnaire has 22 parameters. During data collection, 400 samples are obtained from 20 IT and 20 Non-IT (other services) organizations where ten employees from each organization participated in the survey. For the Data analysis for the acceptance model and also for testing of the hypothesis, several statistical-based data analysis techniques such as Data Reliability, Cross Tabulation, Normality test, Correlation ∖& Regression-based analysis, Factor Analysis, along with Pearson Chi-Square, Mann-Whitney U, Wilcoxon W for hypothetical testings are done. Apart from these criteria, using the data analysis techniques, a well-known technology acceptance model for the e-HRM system has also been employed to test with variables of the proposed questionnaire for the acceptance of e-HRM systems. RESULTS: All the statistical tests and testing of hypotheses are done in a sequential manner for comparison purposes of factors that affect e-HRM systems in IT and Non-IT organizations. The results are reported in detail with explanations and justifications. CONCLUSION: The results of the data analyses can be concluded with finding and discussing that the acceptability of e-HRM systems by the end users in IT firms is much higher than in other service organizations because of the significant difference in presence of external factors, perceived usefulness, perceived ease of use, user attitude, and behavior between the IT and Non-IT groups of organizations.

Optimizing Resource Management for Shared Microservices: A Scalable System Design

A common approach to improving resource utilization in data centers is to adaptively provision resources based on the actual workload. One fundamental challenge of doing this in microservice management frameworks, however, is that different components of a service can exhibit significant differences in their impact on end-to-end performance. To make resource management more challenging, a single microservice can be shared by multiple online services that have diverse workload patterns and SLA requirements. We present an efficient resource management system, namely Erms, for guaranteeing SLAs with high probability in shared microservice environments. Erms profiles microservice latency as a piece-wise linear function of the workload, resource usage, and interference. Based on this profiling, Erms builds resource scaling models to optimally determine latency targets for microservices with complex dependencies. Erms also designs new scheduling policies at shared microservices to further enhance resource efficiency. Experiments across microservice benchmarks as well as trace-driven simulations demonstrate that Erms can reduce SLA violation probability by 5× and more importantly, lead to a reduction in resource usage by 1.6×, compared to state-of-the-art approaches.