Resource Planning Research Articles

Pengaruh Perencanaan Sumber Daya Manusia Terhadap Kinerja Karyawan Ritel

This study examines the relationship between human resource planning and employee performance in the retail sector. It focuses on how effective human resource strategies, such as workforce planning, recruitment, training, and development, impact the productivity, efficiency, and overall performance of retail employees. The study aimed to assess the effect of human resource planning on employee performance in the retail sector. Data collection was based on field observations and the distribution of questionnaires. Data analysis was performed using simple linear regression processed with SPSS, resulting in the equation Y=28.41+0.26X. The findings demonstrate that human resource planning has a positive and significant effect on employee performance. The hypothesis was accepted as the calculated t-value was greater than the t-table value (2.554 > 1.988).

Resource Planning and Collapse of Buildings in Kiambu County, Kenya

Purpose: The research sought to find out the impacts of resource planning on building collapse in Kiambu County, Kenya. Therefore, this research aimed to establish the impact of resource planning on building collapse in Kiambu to be able to advise on how to contain the problem and enhance the non-collapse of such buildings. Methodology: In this study, the authors adopted a simple random sampling technique to identify construction projects in Kiambu County based on Kahneman and Tversky’s Scheduling Fallacy Theory that covers problems such as over-optimism and false resource forecasting on resource requirement necessary for efficient completion of the project. The descriptive research method was also used in the study since it included both qualitative and quantitative data. The population of the study consisted of 545, which included the employees from the physical planning department, architects, surveyors, engineers, and contractors’ builders, and finally the sample size came to 231 as estimated By Yamane, 1967. Findings: The research findings revealed that poor resource management has partly influenced building failures. Attempts by the contractors to solve the problem and eradicate the vice have been frustrated by the locals and politicians for their selfish reasons which have not been highlighted in this study. The research also pointed out that handling of resources reveals the highest mean score of 4.1773 this means that most respondents hold positive attitudes towards their practice of resource planning. Unique Contribution to Theory, Policy, and Practice: The Scheduling Fallacy Theory of 1979 by Kahneman and Tversky was utilized to examine the construction collapses of Kiambu County and the cognitive biases of underestimating time, resources, and costs in construction projects. The study suggested that strict adherence to building codes, better regulatory standards, risk evaluation, and better resource allocation are needed. Prevention was recommended through cross-sector collaboration of government, developers, contractors, and regulatory bodies alike. Empirical research into resource planning faults and their impact on structural integrity was proposed.

Measuring IT Governance in ERP Systems: a COBIT 2019 Evaluation of SAP Material Management Module

Information technology systems such as Enterprise Resource Planning (ERP) are crucial for companies to meet their business needs. However, as companies grow, challenges arise, particularly in the Material Management (MM) module within SAP. This study aims to identify issues such as data inaccuracies and material duplication during the interfacing process with SAP MM and assess IT governance capabilities using the COBIT 2019 framework. The methods used include interviews and literature review, focusing on the domains of APO12 (Managed Risk), BAI03 (Managed Solutions Identification & Build), and DSS03 (Managed Problems). The results show that APO12 reached capability level 4, while BAI03 and DSS03 are at level 3, with a target of level 4. Recommendations include enhancing proactive system design evaluation, developing systematic procedures to address data issues, and implementing permanent solutions to prevent data duplication and inaccuracies during interfacing.

Actionable human–water system modelling under uncertainty

Abstract. This paper develops an actionable interdisciplinary model that quantifies and assesses uncertainties in water resource allocation under climate change. To achieve this objective, we develop an innovative socio-ecological grand ensemble that combines climate, hydrological, and microeconomic ensemble experiments with a widely used decision support system for water resource planning and management. Each system is populated with multiple models (multi-model), which we use to evaluate the impacts of multiple climate scenarios and policies (multi-scenario, multi-forcing) across systems so as to identify plausible futures where water management policies meet or miss their objectives and to explore potential tipping points. The application of the methods is exemplified by a study conducted in the Douro River basin (DRB), an agricultural basin located in central Spain. Our results show how marginal climate changes can trigger non-linear water allocation changes in the decision support systems (DSSs) and/or non-linear adaptive responses of irrigators to water shortages. For example, while some irrigators barely experience economic losses (average profit and employment fall by < 0.5 %) under mild water allocation reductions of 5 % or lower, profit and employment fall by up to 12 % (∼ 24 ×) when water allocation is reduced by 10 % or less (∼ 2×). This substantiates the relevance of informing the potential natural and socio-economic impacts of adaptation strategies and related uncertainties for identifying robust decisions.

The Impact of Human Resource Planning on Strategic Goals of Companies

Today's world, especially the world of companies, is undergoing significant and continuous changes and transformations. All dimensions of companies, from internal to external environments, from human to non-human factors, are rapidly transitioning from one state to another. In today's competitive world, one of the important tools for creating transformation, ensuring the survival of a company, and achieving desired goals and missions is the human element. This research aims to examine the impact of human resource planning on the strategic goals of the company (case study: Exon Group). The research population includes all employees of Exon Group in Tehran, totalling 360 individuals. Structural equation modelling was used to test the hypotheses, and the software used in this study includes SPSS version 22 and Smart PLS version 2. The research findings indicate that human resource planning has a significant impact on the strategic goals of the company.

Development of a Smart Material Resource Planning System in the Context of Warehouse 4.0

Development of a Smart Material Resource Planning System in the Context of Warehouse 4.0

Location-aware job scheduling for IoT systems using cloud and fog

The rapid growth of Internet of Things (IoT) applications generates vast volumes of high-speed data streams from numerous devices. Cloud computing solutions often handle and manage this data; however, for certain applications, the latency introduced by transmitting data from edge devices to the cloud may be unacceptable. This issue is exacerbated by the bandwidth constraints of public networks, which become significant barriers in large-scale IoT implementations. Consequently, effective resource management, service management, data storage, and power management require more robust infrastructure and complex protocols. An "intelligent gateway" based on fog computing can enhance the efficient utilization of cloud and network resources. Resource planning and management in a fog-cloud environment significantly impact system performance, particularly latency. This problem is known to be NP-hard. This paper addresses the challenge of lifetime optimization for scheduling data-intensive tasks in fog and cloud-based IoT systems. Initially, we propose an Integer Linear Programming (ILP) optimization model to frame the problem. Subsequently, we introduce a heuristic method named Data-Locality Aware Job Scheduling in Fog-Cloud (DLSFC), which is derived from the proposed formulation. The effectiveness of DLSFC is evaluated across various system characteristics, with results demonstrating that DLSFC achieves solutions within 85 % of the ideal outcome provided by the LP solver.

The costs of school exclusion: a case study analysis of England, Wales and Scotland

ABSTRACT The cost of permanent exclusion from school is an issue that has been gaining increasing attention in recent years. However, efforts to estimate these costs have mainly focused on those incurred after the student has been excluded, such as those for alternative education providers. Less focus has been given to costs associated with processes leading up to exclusion, including the time spent by school staff and other professionals (e.g. counsellors, special education needs specialists) in efforts to prevent exclusion, or to mitigate negative outcomes. Moreover, the unpaid time and effort spent by parents/carers during this process often go unaccounted for, despite significant financial implications from foregone productivity and income. In this paper, we use a series of five case studies based on in-depth interviews with different stakeholders from England, Scotland and Wales to illustrate the potential involvement of the professionals and parents/carers in the process leading up to – or the measures preventing – permanent exclusion, and estimate the associated costs. It is important to highlight these costs for future resource planning, as well as to present a more comprehensive picture of the true costs of permanent exclusion.

Enhancing ERP Usage Through Absorptive Capacity: A Case Study from Indonesia Enterprise

This research investigates the effect of individual users' absorptive capacity to the usage of Enterprise Resource Planning (ERP) at PT ABC, a transportation company in Indonesia. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM). This study tests the relationships between three facets of absorptive capacity and ERP usage. This study revealed that among aspects of absorptive capacity, only the capacity associated with applying ERP knowledge directly affects ERP usage. Meanwhile, prior understanding and proficiency in integrating ERP systems have a significant indirect impact on ERP usage performance. Based on these findings, it is suggested that PT ABC strengthens individual employees' absorptive capacities and encourages them to acquire ERP expertise before implementation. Moreover, cultivating a culture that supports collaboration and knowledge sharing is crucial for maximizing the benefits of ERP systems within the organization. Implementing these strategies is expected to improve ERP adoption outcomes at PT ABC

Convergence and Innovation of Artificial Intelligence in Corporate Strategic Planning: Opportunities, Challenges and Future Research Directions

In the contemporary digital era, the swift evolution of Artificial Intelligence (AI) has not only revolutionized various sectors but has also become an indispensable asset in corporate strategic planning, decision-making processes, and operational efficiency. This paper delves into the multifaceted applications of AI across a myriad of industries, providing a comprehensive analysis of its integration into corporate strategy and its profound impact on business operations. This study adopts a comprehensive research methodology that merges qualitative case studies with quantitative data analysis to scrutinize the practical use of AI in strategic planning. It delves into the interdependent relationship between AI and conventional digital frameworks, such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems. The research aims to illuminate how the fusion of these technologies can substantially enhance corporate competencies and optimize operational workflows. The study's findings indicate that while AI has the potential to offer transformative solutions, its integration into strategic planning is not without challenges. Ethical considerations, legal ramifications, and societal implications must be carefully navigated. Additionally, the study reveals a notable void in existing literature concerning the enduring strategic ramifications of AI and its effects on corporate culture and talent management practices. This gap highlights the need for further exploration into how AI shapes the long-term strategic direction and internal dynamics within organizations.

Energy-water-carbon system management in response to climate change in China under uncertainties (2026–2050)

Climate change has seriously affected the resource utilization of the energy-water-carbon system, while the system carbon emissions continuously act on climate change. Given the interaction between the two, there is an urgent need to facilitate the rational allocation of system resources to realize the positive feedback between climate change and energy-water-carbon system development. In this study, an interval stochastic fuzzy integer programming (ISFIP) model is developed to support resource planning and management of the energy-water-carbon system in response to climate change in China. The developed model has advantages in characterizing the complexity and uncertainty of the energy-water-carbon system and providing optimal system decision-making options under multiple climate change scenarios. The results show that the SSP126-NZE scenario power system would reduce carbon emissions by 16.23 %, air pollutant emissions by 19.31 %, water consumption by 30.96 %, and increase economic benefits by 10.97 %, respectively, compared to the SSP585-STEPS baseline scenario.

Designing an optimal task scheduling and VM placement in the cloud environment with multi-objective constraints using Hybrid Lemurs and Gannet Optimization Algorithm

ABSTRACT An efficient resource utilization method can greatly reduce expenses and unwanted resources. Typical cloud resource planning approaches lack support for the emerging paradigm regarding asset management speed and optimization. The use of cloud computing relies heavily on task planning and allocation of resources. The task scheduling issue is more crucial in arranging and allotting application jobs supplied by customers on Virtual Machines (VM) in a specific manner. The task planning issue needs to be specifically stated to increase scheduling efficiency. The task scheduling in the cloud environment model is developed using optimization techniques. This model intends to optimize both the task scheduling and VM placement over the cloud environment. In this model, a new hybrid-meta-heuristic optimization algorithm is developed named the Hybrid Lemurs-based Gannet Optimization Algorithm (HL-GOA). The multi-objective function is considered with constraints like cost, time, resource utilization, makespan, and throughput. The proposed model is further validated and compared against existing methodologies. The total time required for scheduling and VM placement is 30.23%, 6.25%, 11.76%, and 10.44% reduced than ESO, RSO, LO, and GOA with 2 VMs. The simulation outcomes revealed that the developed model effectively resolved the scheduling and VL placement issues.

A bidirectional gated recurrent unit based novel stacking ensemble regressor for foretelling the global horizontal irradiance

The rapid expansion of solar power generation has led to new challenges in solar intermittency, requiring precise forecasts of Global Horizontal Irradiance (GHI). Accurate GHI predictions are crucial for integrating sustainable energy sources into traditional electrical grid management. The article proposes an innovative solution, the novel Enhanced Stack Ensemble with a Bi-directional Gated Recurrent Unit (ESE-Bi-GRU), which uses machine learning (ML) boosting regressors such as Ada Boost, Cat Boost, Extreme Gradient Boost, and Gradient Boost, and Light Gradient Boost Machine acts as a base learner and the deep learning (DL) algorithms such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for both directions are taken as a meta-learner. The predictive performance of the proposed ESE-Bi-GRU model is evaluated against individual models, showing significant reductions in mean absolute error (MAE) by 86.03 % and root mean squared error (RMSE) by 66.43 %. The model's ability to minimize prediction errors, such as MAE and RMSE holds promise for more effective planning and utilization of sporadic solar resources. By improving GHI forecast accuracy, the ESE-Bi-GRU model contributes to optimizing the integration of sustainable energy sources within the broader energy grid, fostering a more sustainable and environmentally conscious approach to energy management.

Lake water storage changes and their cause analysis in Mongolia

Lake is an important water resources in Mongolia, which has undergone a large variation in past decades. However, it is still challenging to monitor long-term changes in lake water storage (LWS) due to the lack of lake level monitoring and long-term satellite altimetry data for Mongolian lakes. According to the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM) and the Joint Research Centre (JRC) dataset, we estimated the LWS changes of 55 Mongolian lakes (> 10 km2) from 1991 to 2020. The results showed that the LWS increased by 40.24 km3 from 1991 to 1997, especially for northwestern Mongolia with 31.47 km3. However, the LWS decreased by 32.44 km3 from 1998 to 2010, and the lakes in the northwestern and southern decreased by 20.24 km3 (62%) and 7.38 km3 (23%), respectively, and then the LWS continued to decrease by 10.22 km3 from 2011 to 2020. The precipitation was the primary cause of lake change, which could explain 45.13% of LWS change based on Generalized Linear Model, followed by temperature (26.33%) and irrigated area (16.72%). Analyzing the changing characteristic and driving mechanisms of LWS can provide a scientific basis for local water resources management and planning.

AI-Driven Resource Provisioning: Enhancing Elasticity and Efficiency with Hybrid RNN-LSTM Models

Automatic resource provisioning techniques that dynamically modify resources in response to service demand are essential to implementing elasticity services. For systems with strict latency or reaction time requirements, such as Enterprise Resource Planning (ERP) systems under high traffic loads, this flexibility is crucial for lowering power consumption and guaranteeing quality of service (QoS). Determining the best point at which to scale resources is still a difficult task. In this research, we provide an AI-integrated system that adjusts resources according to anticipated demand. To predict load requests, the system uses an LSTM model and a Hybrid Recurrent Neural Network (RNN). This strategy seeks to minimize overprovisioning, which will lower the cost of infrastructure and energy usage. The deep learning model aims to estimate the resources required to improve service response time and satisfy customer requests, as well as to anticipate with high accuracy the processing load of distributed servers. Proactive provisioning decisions are made for the servers based on the anticipated load. Our tests on a common server request dataset show that the suggested RNN+LSTM model performs better than traditional deep learning models in terms of efficient resource management and prediction accuracy.

Possibilities of the utilization of trauma networks of the German Society for Trauma Surgery using digital solutions

This paper describes the use of digital solutions to improve the care of trauma patients in Germany. The focus is on the trauma networks of the German Society for Trauma Surgery (Deutsche Gesellschaft für Unfallchirurgie, DGU). The use of digital solutions includes quality assurance through the TraumaRegister, which enables comprehensive data analysis as well as preregistration and resource utilization through programs such as the interdisciplinary proof of treatment IVENA eHealth, Rescuetrack and Rescue-Net. In addition, Predictive Hospital Resource Planning is presented, which optimizes resource forecasting using artificial intelligence (AI). Telemedical services such as Medgate and teleradiology solutions (Nexus/Chili) offer additional support, especially in rural areas. The paper shows how the digitalization of medical care is crucial to improving the efficiency and quality of treatment of trauma patients. In addition, the paper shows possible developments in the field of clinical decision making through AI.

Pengaruh Budaya Organisasi dan Disiplin Kerja Terhadap Kinerja Karyawan Pada PT Duta Raja Gas Depok

Human resource management is the development of human resources that functions in carrying out human resource planning, implementation, recruitment, training, employee career development and conducting initiatives for the organizational development of an organization or company. The purpose of this study was to determine the organizational culture and work discipline affect employee performance. In this study, researchers used statistical quantitative analysis, multiple linear regression analysis and coefficient of determination analysis with the help of IBM SPSS 25 software. The sample used in this study was a saturated sample of 40 employees. The results of this study obtained using the t test that organizational culture partially has a positive and significant effect on employee performance with a significant level of 0.002 <0.05 and t test results of 3.316> 2.022. Work discipline partially has a positive and significant effect on employee performance with a significant level of 0.001 <0.05 and t test results of 3.491> 2.022. Organizational culture and work discipline simultaneously have a positive and significant effect with a significant level of 0.000 <0.05 and F test results of 17,066> 3.25 on employee performance.

Crop Yield Prediction Using Machine-Learning

This study examines the utilization of machine learning techniques for forecasting agricultural yields. This is critical because it helps to improve farm productivity and food security. Agriculture is the backbone of Pakistan’s economy, and accurate crop yield prediction can greatly assist in economic planning and allocation of resources. “Several ML algorithms, including K-Nearest Neighbor, Lasso regression, Ridge Regression, Decision Tree, and Linear Regression,” were used in this research to forecast crop yield using historical data and climate variables. Among all the models developed, the KNN algorithm was the most effective, with MAE=108023825.91 and R2=0.98494, meaning that it had the highest accuracy. These results suggest that ML has a large potential to give good predictions about crop yields, which would be useful for decision-making in agriculture and crop management or strategy development. The paper also enlightens on the limitations of some algorithms, like Decision Trees, known for their low-performance abilities; it suggests other ensemble models as better alternatives to increase predictive power. Machine learning integration with big data platforms and advanced computational methods offers a promising approach toward smart agriculture, ensuring food security.

AI and Machine Learning in Enhancing Scalability and Efficiency of Integrated E-commerce and ERP Systems

This article explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing the integration of E-commerce platforms with Enterprise Resource Planning (ERP) systems. As E-commerce experiences explosive growth and ERP systems become increasingly complex, businesses face significant challenges in maintaining scalability and efficiency. We examine how AI and ML can optimize various aspects of these integrated systems, from intelligent automation and predictive analytics to anomaly detection and decision support. Through case studies and analysis of current trends, we demonstrate the tangible benefits of AI/ML implementation, including reduced costs, improved accuracy, and enhanced customer experiences. The article also addresses key challenges such as data quality, scalability, ethical considerations, and the skills gap. Finally, we explore future research directions in explainable AI, edge computing, blockchain integration, and natural language processing, highlighting their potential impacts on the E-commerce and ERP landscape.

Prediction of Overall Equipment Effectiveness in Assembly Processes Using Machine Learning

Abstract Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the support of various modern technical and Information Technology (IT) tools, the recording, storage and processing of large amounts of data is now a routine activity. Based on machine learning, efficiency metrics including Overall Equipment Effectiveness (OEE), can be partially predicted, but industrial companies need more accurate and reliable methods. The analyzed algorithms can be used in general for all production units or machines where production data is recorded by Manufacturing Execution System (MES) or other Enterprise Resource Planning (ERP) systems are available. This paper presents and determinates which most used machine learning methods should be combined with each other in order to achieve a better prediction result.