Intelligent Management of Human Capital

Intelligent Management of Human Capital

Intelligent Prediction of Human Resource Requirements in Organizations: A Data Mining Approach

Document Type : Original Article

Authors
Shahrzad Arab karami 1
Ebrahim Nazari Farrokhi 2
Behrouz Dolatshah 2
1 Master's degree in information technology management, Islamic Azad University, Science and Research Branch, Tehran, Iran.
2 Assistant Professor and Faculty Member at the Command and Staff University of the Army of the Islamic Republic of Iran (AJA), Tehran, Iran.
10.22034/imhr.2024.464939.1007
Abstract
Objective: Data mining plays a crucial role in optimizing human resource management by uncovering valuable patterns from large datasets. This study aims to predict human resource requirements using a novel approach that combines neural networks with the Whale Optimization Algorithm (WOA) and Simulated Annealing (SA).
Method: This applied, quantitative research analyzed a dataset of 30,000 records. The proposed method integrates neural networks' pattern recognition capabilities with WOA and SA's optimization strengths to enhance the accuracy and efficiency of human resource needs prediction. The hybrid model was designed to address limitations in traditional forecasting methods.
Findings: The hybrid model demonstrated significant improvements over traditional methods, achieving a 15% increase in prediction accuracy and a 20% reduction in computational time. It also showed enhanced robustness when tested with varied input parameters, maintaining consistent performance across different scenarios.
Conclusion: This study underscores the potential of advanced data mining techniques in enhancing human resource planning. The proposed method offers organizations a more reliable tool for forecasting staffing needs and making strategic decisions. Future research should explore its applicability across various industries and investigate its potential for real-time workforce optimization. Additionally, integrating this model with existing HR systems could provide a comprehensive solution for dynamic workforce management.
Keywords
Human Resources
Data Mining
Artificial Neural Network
Blue Whale Algorithm
Refrigeration Simulation
Subjects
Abbracciavento, F., Formentin, S., Gualandi, E., Nanni, R., Paoli, A., & Savaresi, S. M. (2020). Modeling and prediction for optimal human resources management. IFAC-PapersOnLine, 53(2),16996 17001. https://doi.org/10.1016/j.ifacol.2020.04.161
-Agarwal, A., Sharma, S., Kumar, V., & Kaur, M. (2021). Effect of E-learning on public health and environment during COVID-19 lockdown. Big Data Mining and Analytics, 4(2), 104-115. https://doi.org/10.26599/BDMA.2020.9020014
-Ahmadi, Y., & Khani, A. (2019). Predicting human resources for the strategic planning of human resources at the Real Estate Registration Office of District One, Shiraz, 2020-2025, using regression analysis. In the 5th International Conference on Management Sciences and Accounting, Tehran, [In Persian].
-Alinaqian, N. (2021). Challenges and opportunities of electronic human resource management. In the Digital Transformation Conference: Challenges and Opportunities, Isfahan, [In Persian].
-Beardwell, I., Holden, L., & Claydon, T. (2004). Human resource management. Harlow: FT/Prentice Hall.
-Chien, C.-F., & Chen, L.-F. (2007). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34(1), 280-290.
-Daniel, T., & Larose, J. (2005). Discovery knowledge in data: An introduction to data mining. Wiley Interscience.
-Haghi Kashani, M., Madanipour, M., Nikravan, M., Asghari, P., & Mahdipour, E. (2021). A systematic review of IoT in healthcare: Applications, techniques, and trends. Journal of Network and Computer Applications, 192, 1-16. https://doi.org/10.1016/j.jnca.2021.103007
-Jokar, K., & Sadeghi, A. (2022). The role of strategic management planning in attracting competent human resources for the organization in the moral education of students in the education system. In the 2nd National Conference on Clinical Psychology of Children and Adolescents, Ardabil, [In Persian].
-Kumar, S., & Sachdeva, R. (2023). A survey of different supervised learning-based classification models for student’s academic performance prediction. In Proceedings of the International Conference on Innovative Computing and Communications (Vol. 492, pp. 529-538). Springer. https://doi.org/10.1007/978-3-030-99993-7_47
-Palacios, C. A., Reyes-Suárez, J. A., Bearzotti, L. A., Leiva, V., & Marchant, C. (2021). Knowledge discovery for higher education student retention based on data mining: Machine learning algorithms and case study in Chile. Entropy, 23(4), 485. https://doi.org/10.3390/e23040485
-Rangriz, H., & Memari, M. (2017). Forecasting the supply of human resources using the Markov model during 2016-2021, [In Persian].
-Rehman, K. U., Mata, M. N., Martins, J. M., Mariam, S., Rita, J. X., & Correia, A. B. (2021). SHRM practices, employee and organizational resilient behavior: Implications for open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 107. https://doi.org/10.3390/joitmc7020107
-Saeedbakhsh, S., Sattari, M., Mohammadi, M., & Najafian, J. (2021). Application of data mining techniques in predicting coronary heart disease: A systematic review. International Journal of Environmental Health Engineering (IJEHE), 10(3), 1-8. https://doi.org/10.4103/IJEHE.IJEHE_47_21
-Thapa, C., & Camtepe, S. (2021). Precision health data: Requirements, challenges and existing techniques for data security and privacy. Computers in Biology and Medicine, 129, 104164. https://doi.org/10.1016/j.compbiomed.2020.104164
-Visweswaran, S., et al. (2022). An atomic approach to the design and implementation of a research data warehouse. Journal of the American Medical Informatics Association, 29(4), 601-608. https://doi.org/10.1093/jamia/ocab204
-Woods, R. H. (2020). Predicting is difficult, especially about the future: Human resources in the new millennium. International Journal of Hospitality Management, 18(4), 443-456. https://doi.org/10.1016/j.ijhm.2020.04.011
-Zhang, L., Wu, L., Wu, T., & Alamin, A. (2023). Research on application status and deepening strategy of smart campus in primary and secondary schools: Case study of a county in Western China. In 2023 IEEE 12th International Conference on Educational and Information Technology (ICEIT) (pp. 298-303). IEEE. https://doi.org/10.1109/ICEIT57125.2023.10107777
-Zhang, Y., Xu, S., Zhang, L., & Yang, M. (2021). Big data and human resource management research: An integrative review and new directions for future research. Journal of Business Research, 133, 34-50. https://doi.org/10.1016/j.jbusres.2020.10.036