Roles Conceptualization, Data curation, Formal analysis, Methodology, Resources, Writing – original draft, Writing – review & editing * E-mail: zhao_yaxuan@outlook.com Affiliation Business School, University of International Business and Economics, Beijing, China
The planning of human resources and the management of enterprises consider the organization’s size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations’ poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses’ operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs.
Citation: Zhao Y (2024) Development of big data assisted effective enterprise resource planning framework for smart human resource management. PLoS ONE 19(5): e0303297. https://doi.org/10.1371/journal.pone.0303297
Editor: Muhammad Hashim, National Textile University, PAKISTAN
Received: June 1, 2023; Accepted: April 22, 2024; Published: May 20, 2024
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Data Availability: All relevant data are within the manuscript.
Funding: The author(s) received no specific funding for this work.
Competing interests: NO authors have competing interests.
Enterprises rapidly realize the importance of big data in today’s data-driven environment for streamlining their operations and decision-making procedures. Enterprise Resource Planning (ERP) and Human Resource Management (HRM) are key areas where big data has a lot of technology that can revolutionize how businesses manage their workforces, boost productivity, and promote economic growth when integrated with ERP systems [1]. The smart HRM system may intelligently assign resources to enterprises or departments following their appropriateness and abilities through analysis of information about human resources, including worker abilities, expertise, and productivity. This wise resource allocation boosts production and operational effectiveness [2]. Real-time feedback and tracking systems in HRM using big data technology by utilizing real-time HR data collection and analysis enables managers to quickly spot performance gaps, respond to employee requirements, and give timely feedback for development [3]. The technology can provide a more accurate and holistic evaluation of employee performance by analyzing full HR data and considering many elements outside typical measures. Technology can help smart recruitment and hiring, making objective and equitable performance appraisals easier. Effective HRM is essential for an organization’s success since it directly impacts worker efficiency, involvement, and overall business results. The manual and subjective approaches used in traditional HRM processes made it difficult to thoroughly understand employee abilities, achievement, and learning needs. However, big data technologies can potentially make HRM a more planned and data-focused function. The study aims to identify innovative approaches for streamlining HRM procedures, bettering resource allocation, and reaching greater efficiency and productivity by investigating how to incorporate big data technologies into the present ERP system [4].
The fundamental element of human capital is expertise and describe working expertise, specifically as the competence or knowledge developed via regular work practice. The importance of human capital is growing as many sectors and businesses move from labor-intensive to knowledge-intensive models. It is thought that building up human capital will result in company success and economic expansion [5]. To understand staffing, HR development, establishment, and overall enterprise operation, a full corporate development improvement necessitates an all-encompassing research and choice help model of the venture enhanced [6]. This study emphasizes how big data improves HRM through ERP systems, leveraging modern analytics for informed decision-making. It aims to integrate big data into ERP for simplified HR data management and assess its impact on HRM, productivity, and organizational success. HRM involves hiring, creating labor rules, and retaining them, which is essential for every company. Businesses use human resource management to keep employees secure and contented [7]. Enterprise resource planning (ERP) is a software module businesses use to connect the company with its employees better. ERP examines the specific pieces of information needed by businesses. ERP considers a company’s resources and uses this data to make informed decisions. ERP is a condition- and function-based business management system. Companies of any size may benefit from using an electronic HRM system. The primary goal of electronic human resource management (E-HRM) is to lessen the financial and time commitment involved in administrative tasks. Electronic human resource management (E-HRM) pinpoints the critical human resource-related components that provide superior data for downstream business operations. By providing incentives based on resource availability, E-HRM encourages workers to do quality work [8].
Human resource management (HRM) uses big data (BD) accumulation in businesses and organizations. Information extraction from large data sets is a popular use of big data. The big data accumulation approach shortens the time it takes for calculations to finish, expanding the effective scope of the systems. Information useful for forecasting in management systems may be accumulated using the accumulation approach [9]. Human resource management at the corporate level extensively uses big data to enhance management capabilities. Effective data required to complete a business task is predicted by BD [10]. BD advocates for a wide variety of high-quality HRM solutions in businesses. Database complexity is shown by the BD accumulation approach [11]. The accumulation approach simplifies the identification and calculation procedures involved in managing resources. The accumulation of BD is also employed in high-tech businesses to maximize returns on investments of scarce resources [12]. Data is gathered using the Accumulation technique, then analyzed using predetermined criteria. Increased accuracy in management is made possible by the availability of big data [13].
Big data analytics (BDA) is a method for extracting meaningful information from massive datasets. The widespread use of BDA has real-world consequences for certain industries [14]. Smart human resource management (SHRM) and utilization are two more business intelligence applications (BDA). Among BDA’s many applications is tracking database changes [15]. BDA reveals the technological tactics used by SHRM systems. BDA generates data that can be used to boost the effectiveness of a company’s human resource management and upkeep procedures [16]. BDA improves SHRM systems’ overall efficiency and dependability. In HRM systems, the BDA approach supported by AI is often employed [17].
The database is the primary location where AI is put to use. AI uses the feature selection approach to choose the characteristics and elements needed for organizational tasks [18]. Important SHRM strategies and methods are identified using AI. Using BDA powered by AI reduces calculation time, increasing system efficiency [19,20]. This article explores the prospects HR professionals have thanks to talent analytics. This paper compares talent analytics to other branches of business analytics and explores the advantages and disadvantages of using this strategy inside a company. Several examples of how talent analytics has helped a company make better choices will be shown. This research identifies critical pathways via which enterprise performance analytics might enhance the effectiveness of human resources departments and, by extension, whole businesses. This study addresses the costs (in terms of governance of data and ethics) that broad usage of talent analytics might produce while emphasizing the benefits that worker analytics brings to businesses. The significance of trust in ensuring the effective rollout of HR analytics initiatives. The study focuses on the difficulty of bettering business operations and human resource management. Manual and subjective HR practices prevent companies from maximizing the talents of their employees. This research proposes a Placement-Assisted Resource Management Scheme (PRMS) to increase organizational efficiency and financial performance by optimizing resource allocation using big data and transfer learning.
The novelty of the proposed work lies in the PRMS integrates big data analytics, transfer learning, and skill-based resource allocation, making it innovative to optimize resource management within enterprises. This novel strategy improves efficiency by matching resources to talent-based tasks, optimizing strategies using data-driven decision-making, and establishing exact management states for optimal business results. The PRMS improves performance indicators over existing methods, giving it a credible platform for improving company efficiency and financial results.
The proposed system is motivated to enhance business operations and financial outcomes through efficient resource management. Optimizing resource placement and utilization is essential for producing better results because HR planning and management of enterprises are critical to an organization’s success. This research attempts to provide an effective and productivity-focused resource allocation strategy by utilizing knowledge, organizational requirements, and a data-driven approach. The objective is to improve enterprise operations, allocate resources efficiently, boost efficiency, and produce better financial results, eventually enhancing organizations’ competitive edge and growth. This research proposes an innovative HRM and business growth method by utilizing the existing ERP system to incorporate big data technologies, machine learning, and transfer learning. The novel aspect is the implementation of a Placement-Assisted Resource Management Scheme (PRMS), which optimizes resource allocation by employee talents to boost output and efficiency. PRMS uses trial periods to assess resources and historical data to position them in roles that best utilize their skill sets. Adaptive resource allocation made possible by transfer learning boosts business performance. The new aspect of this research is its holistic approach to resource management. This can help businesses greatly by streamlining HR procedures and increasing efficiency. The article’s contribution is summarized in the points below:
The upcoming sections in this paper are as follows: section 2 examines the related work; Section 3 determines the enterprise’s performance and improves the enterprise management; Section 4 demonstrates the results and discussion; Section 5 concludes the overall paperwork.
Chen et al. [21] developed a new human resource management (HRM) system using B/S mode. The actual goal of an HRM system is to analyze the system function, activities, roles, and non-function of an application. Both small and medium-sized enterprises contain HRM, which reduces the complexity of further processes. The exact aspects or content of the function are identified, producing appropriate HRM data. The B/S mode improves the overall performance level of HRM systems. When there are some inevitable problems in the system, the system can be easily repaired. For example, the database can be automatically backed up and restored to avoid the loss caused by system damage to the enterprise. Additionally, the section only discussed the system’s economic advantages without taking other crucial aspects of HRM into account, like staff involvement, talent development, and organizational culture.
Liu et al. [22] introduced an FPGA and data mining-based human resource management (HRM) platform for enterprises. The data mining approach provides control strategies that detect the relevant data for management systems. Multi-dimensional variables and attributes are analyzed using the FPGA model, reducing the computation process’s latency. Spatial and temporal features are also detected from the database that produces relevant data for management processes. Experimental results show that the introduced platform maximizes the efficiency and reliability range of the systems. However, data similarities and differences are not identified, and the text also omitted information on the precise variables or sources used to evaluate and manipulate HR data.
Jian et al. [23] proposed a data mining-based human resource decision support system for enterprises. The main aim of the proposed method is to support decision-making by providing appropriate human resources. Data mining technique predicts the exact data which are required for decision-making processes. The proposed method achieves high accuracy in the decision-making process. The proposed method improves an organization’s effectiveness and performance level of human resource management. However, generating a classifier quickly and effectively is a big problem for a given data set. One weakness of the study is the absence of particular information regarding the design and implementation of the business HR decision support system based on data mining.
Ahlemann et al. [24] designed a resource-based theory (RBT) human resource management system for enterprise architecture management (EAM). The RBT is mainly used here to understand the exact need for a function or process. As a result, RBT reduces both time and energy-consuming ranges in the computation process. Furthermore, RBT identifies the necessary data required for EAM benefits that reduce the computational cost of the systems. As a result, EAM creates effective benefits and functionalities that enhance the performance range of small enterprises. However, the study relies primarily on interview data and document analysis. Considering the sample size restrictions, theoretical viewpoint, lack of specific practical advice, and alternative suggestions are important.
Jaouadi et al. [25] proposed a big data analytics-based human resource management method for organizations. The goal is to innovate the supply chain, providing relevant ideas for the organizations. Big data analytics is mainly used here to detect the exact capability level of data presented in the database. The proposed method creates an integrated platform to maintain the human resources in the organizations. However, the impact of BDA in achieving business performance is not substantial due to the lack of firm technological capabilities. The report makes recommendations for politicians, but it doesn’t offer any guidance or ideas on how to deal with the elements it has found.
Liu et al. [26] developed a data-driven analysis of employees’ enterprise expertise. The random forest (RF) model is used here to identify the excellent talent among the employees. RF is a machine learning (ML) model used here to achieve high accuracy in detection and prediction processes. RF model produces the exact data for human resource management systems. The actual aim of the proposed method is to improve the performance range of employees in enterprises. Additionally, there may be restrictions on the data used for training and generalizability to diverse employee groups for the machine learning model presented for forecasting development potential.
Table 1 presents the summary of references that deals with enterprise development. However, supervised learning classes in the data are imbalanced.