Аннотації

Поєднання компетенцій та управління ризиками в інженерних проєктах: структура МІУРЛР управління людськими ресурсами

Автор(и):
Черненко Ю. В., Семко О. В. , Мисник Б. В.
Автор(и) (англ)
Chernenko Yu., Semko O., Mysnyk B.
Дата публікації:

25.12.2025

Анотація (укр):

Проведено аналіз критичних проблем, пов'язаних із робочою силою в інженерному секторі, включаючи високу плинність кадрів та невідповідність навичок, які перешкоджають ефективному виконанню проєктів. Виділено дві ключові особливості сучасних інженерних компаній: потреба в інтеграції оцінки компетенцій та підходів до управління ризиками для забезпечення стабільності проєктної діяльності. Запропоновано Метод інтегрального управління ризиками людських ресурсів (МІУРЛР) – структуровану систему, яка стратегічно поєднує розвиток компетенцій з оцінкою ризиків. У контексті розробки елементів моделі наведено її ефективність на прикладі компанії Mastergaz, яка спеціалізується на інженерних проєктах. Матричне представлення ефективності засноване на вимірюванні змін у коефіцієнтах плинності кадрів та коефіцієнтах виконання завдань до та після впровадження МІУРЛР. Впровадження моделі продемонструвало 20-відсоткове зниження коефіцієнта плинності кадрів та 30-відсоткове збільшення коефіцієнта виконання завдань, що було пояснено цільовими навчальними програмами та кращим узгодженням навичок співробітників з вимогами проєкту. Входами моделі МІУРЛР є кадрові ризики та рівень компетенцій, а виходами – підвищена стабільність та адаптивність робочої сили, оптимізація людських ресурсів у конкурентному середовищі. Методологічним підґрунтям моделі є підхід, який інтегрує оцінку компетенцій з управлінням ризиками. Зроблено висновок щодо потенційної ефективності запропонованого МІУРЛР як життєздатного шляху для оптимізації людських ресурсів. Сформульовано галузі подальших досліджень у обраному напрямі, серед яких: формалізація процесу інтеграції МІУРЛР у різні типи інженерних проєктів; адаптація моделі до умов високої конкуренції та швидких технологічних змін; розроблення додаткових показників ефективності для вимірювання задоволеності клієнтів та підвищення загальної задоволеності роботою співробітників; впровадження моделі у практичну діяльність інженерно-проєктних організацій. Сформульовано висновки з проведених досліджень.

Анотація (рус):

Анотація (англ):

Organizations in engineering sectors face critical workforce challenges, including high turnover and skill mismatches, which impede efficient project delivery. The Integral Risk Management Method for Human Resources (IRMMHR) was introduced to mitigate these issues by integrating competency assessments with risk management approaches. This study aimed to evaluate the effectiveness of IRMMHR in reducing personnel risks and enhancing employee competencies at Mastergaz, a company specializing in engineering projects. A mixed-methods design was employed, incorporating quantitative surveys of 150 employees and semi-structured interviews with 20 participants. Data were analyzed to measure changes in turnover rates, task completion rates, and overall job satisfaction pre- and post-implementation. The implementation of IRMMHR led to a 20 percent reduction in turnover rates and a 30 percent increase in task completion rates. These improvements were attributed to targeted training programs and better alignment of employee skills with project requirements. IRMMHR demonstrates promise as a structured framework that strategically integrates competency building with risk assessment. By enhancing workforce stability and adaptability, it offers a viable pathway for organizations seeking to optimize human resources in competitive environments.

Література:

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  3. Benabou, A., Touhami, F., & Abdelouahed Sabri, M. (2025). Predicting employee turnover using machine learning techniques. Acta Informatica Pragensia. URL: https://doi.org/10.18267/j.aip.255.
  4. Bargavi, N., Roy, A., Kumar, V. S., Shrivastava, G., Varma, R., Shrivastava, A., & Roy, A. (2023). An empirical study on employee turnover and job satisfaction in human resource management practices. E3S Web of Conferences. URL: https://doi.org/10.1051/e3sconf/202339907001.
  5. Liu, C., & Miao, W. (2022). The role of employee psychological stress assessment in reducing human resource turnover in enterprises. Frontiers in Psychology, 13. URL: https://doi.org/10.3389/fpsyg.2022.1005716.
  6. Sugiarto, I. (2023). Human resource development strategies to achieve digital transformation in businesses. Journal of Contemporary Administration and Management (ADMAN). URL: https://doi.org/10.61100/adman.v1i3.66.
  7. Al-Jubouri, A., & Youssef, M. (2024). Integrating human resources management and digital competencies: A strategic approach in higher education. Journal of Educational Transformation Studies, 16 (2), 85–98. URL: https://doi.org/10.35445/alishlah.v16i2.5286.
  8. Pomperada, J. R. (2022). Human resource information system with machine learning integration. Qubahan Academic Journal, 2 (2). URL: https://doi.org/10.48161/qaj.v2n2a120.
  9. Sharma, R., & Dhingra, L. (2024). Advancing human resource strategies with deep learning: Predictive analytics for improving employee retention rates. 2024 2nd World Conference on Communication & Computing (WCONF), 1–4. URL: https://doi.org/10.1109/WCONF61366.2024.10692087.
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  11. Mozaffari, F., Rahimi, M., Yazdani, H., & Sohrabi, B. (2022). Employee attrition prediction in a pharmaceutical company using both machine learning approach and qualitative data. Benchmarking: An International Journal. URL: https://doi.org/10.1108/bij-11-2021-0664.
  12. Yazdi, A. M., Mirsepasi, N., Mousakhani, M., & Hanifi, F. (2024). Investigating the status of human resource management development indicators based on competency components in the e-commerce development center. Dynamic Management in Business Analysis. URL: https://doi.org/10.61838/dmbaj.2.4.12.
  13. Yahia, N. B., Colomo-Palacios, R., & Hlel, J. (2021). From big data to deep data to support people analytics for employee attrition prediction. IEEE Access, 9, 60447–60458. URL: https://doi.org/10.1109/access.2021.3074559.
  14. Puli, J., & Sagi, S. (2022). Competency mapping building a competent workforce through human resource information system. Journal of Information and Optimization Sciences, 43 (7), 1885–1899. URL: https://doi.org/10.1080/02522667.2022.2140261.
  15. Muñoz-Pascual, L., Curado, C., & Galende, J. (2019). Human resource management contributions to knowledge sharing for a sustainability-oriented performance: A mixed methods approach. Sustainability, 12 (1), 161. URL: https://doi.org/10.3390/su12010161.
  16. Talapbayeva, G., Yerniyazova, Z., Kultanova, N. B., & Alibekova, A. B. (2024). Object: To study the impact of human resource management (hrm)on employee outcomes, organizational, and financial performance. Bulletin of the Karaganda University Economy Series. URL: https://doi.org/10.31489/2024ec3/101-111.
  17. Memon, M., Salleh, R., Mirza, M. Z., Cheah, J., Ting, H., Ahmad, M., & Tariq, A. (2021). Satisfaction matters: The relationships between hrm practices, work engagement, and turnover intention. International Journal of Manpower. URL: https://doi.org/10.1108/ijm-04-2018-0127.
  18. Cristiani, A., & Peiró, J. (2019). Calculative and collaborative hrm practices, turnover, and performance. International Journal of Manpower. URL: https://doi.org/10.1108/IJM-11-2016-0207.
  19. Haque, A. (2020). Strategic hrm and organizational performance: Does turnover intention matter? International Journal of Organizational Analysis. URL: https://doi.org/10.1108/ijoa-09-2019-1877.
  20. Papa, A., Dezi, L., Gregori, G., Mueller, J., & Miglietta, N. (2018). Improving innovation performance through knowledge acquisition: The moderating role of employee retention and human resource management practices. Journal of Knowledge Management, 24 (4), 589–605. URL: https://doi.org/10.1108/JKM-09-2017-0391.
  21. Elsafty, A., & Oraby, M. (2022). The impact of training on employee retention. International Journal of Business and Management, 17 (5). URL: https://doi.org/10.5539/ijbm.v17n5p58.
  22. Alabi, O. A., Ajayi, F. A., Udeh, C. A., & Efunniyi, C. P. (2024). Predictive analytics in human resources: Enhancing workforce planning and customer experience. International Journal of Research and Scientific Innovation. URL: https://doi.org/10.51244/ijrsi.2024.1109016.
  23. Shrestha, P., & Prajapati, M. P. (2024). Impact of strategic human resource management practices on employee retention. The Batuk. URL: https://doi.org/10.3126/batuk.v10i1.62298.
  24. Davidescu, A., Apostu, S.-A., Paul, A., & Cășuneanu, I. (2020). Work flexibility, job satisfaction, and job performance among Romanian employees – implications for sustainable human resource management. Sustainability, 12 (15), 6086. URL: https://doi.org/10.3390/su12156086.
  25. Lakshman, C., Wang, L., Adhikari, A., & Cheng, G. (2020). Flexibility-oriented hrm practices and innovation: Evidence from china and india. The International Journal of Human Resource Management, 33 (12), 2473–2502. URL: https://doi.org/10.1080/09585192.2020.1861057.
  26. Dutta, S., Ray, A., Chinya, M., Ghatak, S., Mukherjee, A., Bhattacharjee, K., & Das, A. (2024). Predictive hr analytics to optimize decision-making processes and enhance workforce performance. International Journal of Recent Trends in Multidisciplinary Research. URL: https://doi.org/10.59256/ijrtmr.20240402014.
  27. Tiwari, V. (2023). Revolutionizing workplace practices in human resource management with iot-enabled solutions and analytics. Financial Technology and Innovation. URL: https://doi.org/10.54216/fintech-i.020205.
  28. Safarishahrbijari, A. (2018). Workforce forecasting models: A systematic review. Journal of Forecasting. URL: https://doi.org/10.1002/FOR.2541.
  29. Nurani, M., Khuzaini, K., & Shaddiq, S. (2024). Competency-based hr management strategy in the digital era: Systematic literature review. At-Tadbir: Jurnal Ilmiah Manajemen. URL: https://doi.org/10.31602/piuk.v0i0.15798.
  30. Msacky, R. (2024). Retention of human resources for health in the decentralised health system in tanzania: Does training matter? Journal of Policy and Development Studies. URL: https://doi.org/10.4314/jpds.v16i1.5.

References:

  1. Rahman, H., & Raju, V. (2020). Employee turnover intention through human resource management practices: A review of literature. International Research Journal of Management Science, 1 (2), 21–26. URL: https://doi.org/10.47857/irjms.2020.v01si02.035.
  2. Olawale, O., Ajayi, F. A., Udeh, C. A., & Odejide, O. A. (2024). Risk management and hr practices in supply chains: Preparing for the future. Magna Scientia Advanced Research and Reviews. URL: https://doi.org/10.30574/msarr.2024.10.2.0065.
  3. Benabou, A., Touhami, F., & Abdelouahed Sabri, M. (2025). Predicting employee turnover using machine learning techniques. Acta Informatica Pragensia. URL: https://doi.org/10.18267/j.aip.255.
  4. Bargavi, N., Roy, A., Kumar, V. S., Shrivastava, G., Varma, R., Shrivastava, A., & Roy, A. (2023). An empirical study on employee turnover and job satisfaction in human resource management practices. E3S Web of Conferences. URL: https://doi.org/10.1051/e3sconf/202339907001.
  5. Liu, C., & Miao, W. (2022). The role of employee psychological stress assessment in reducing human resource turnover in enterprises. Frontiers in Psychology, 13. URL: https://doi.org/10.3389/fpsyg.2022.1005716.
  6. Sugiarto, I. (2023). Human resource development strategies to achieve digital transformation in businesses. Journal of Contemporary Administration and Management (ADMAN). URL: https://doi.org/10.61100/adman.v1i3.66.
  7. Al-Jubouri, A., & Youssef, M. (2024). Integrating human resources management and digital competencies: A strategic approach in higher education. Journal of Educational Transformation Studies, 16 (2), 85–98. URL: https://doi.org/10.35445/alishlah.v16i2.5286.
  8. Pomperada, J. R. (2022). Human resource information system with machine learning integration. Qubahan Academic Journal, 2 (2). URL: https://doi.org/10.48161/qaj.v2n2a120.
  9. Sharma, R., & Dhingra, L. (2024). Advancing human resource strategies with deep learning: Predictive analytics for improving employee retention rates. 2024 2nd World Conference on Communication & Computing (WCONF), 1–4. URL: https://doi.org/10.1109/WCONF61366.2024.10692087.
  10. Hitchcock, J. (2022). Applying mixed methods research to conduct human resources development inquiry: An update. Human Resource Development Review, 21 (4), 517–538. URL: https://doi.org/10.1177/15344843221129397.
  11. Mozaffari, F., Rahimi, M., Yazdani, H., & Sohrabi, B. (2022). Employee attrition prediction in a pharmaceutical company using both machine learning approach and qualitative data. Benchmarking: An International Journal. URL: https://doi.org/10.1108/bij-11-2021-0664.
  12. Yazdi, A. M., Mirsepasi, N., Mousakhani, M., & Hanifi, F. (2024). Investigating the status of human resource management development indicators based on competency components in the e-commerce development center. Dynamic Management in Business Analysis. URL: https://doi.org/10.61838/dmbaj.2.4.12.
  13. Yahia, N. B., Colomo-Palacios, R., & Hlel, J. (2021). From big data to deep data to support people analytics for employee attrition prediction. IEEE Access, 9, 60447–60458. URL: https://doi.org/10.1109/access.2021.3074559.
  14. Puli, J., & Sagi, S. (2022). Competency mapping building a competent workforce through human resource information system. Journal of Information and Optimization Sciences, 43 (7), 1885–1899. URL: https://doi.org/10.1080/02522667.2022.2140261.
  15. Muñoz-Pascual, L., Curado, C., & Galende, J. (2019). Human resource management contributions to knowledge sharing for a sustainability-oriented performance: A mixed methods approach. Sustainability, 12 (1), 161. URL: https://doi.org/10.3390/su12010161.
  16. Talapbayeva, G., Yerniyazova, Z., Kultanova, N. B., & Alibekova, A. B. (2024). Object: To study the impact of human resource management (hrm)on employee outcomes, organizational, and financial performance. Bulletin of the Karaganda University Economy Series. URL: https://doi.org/10.31489/2024ec3/101-111.
  17. Memon, M., Salleh, R., Mirza, M. Z., Cheah, J., Ting, H., Ahmad, M., & Tariq, A. (2021). Satisfaction matters: The relationships between hrm practices, work engagement, and turnover intention. International Journal of Manpower. URL: https://doi.org/10.1108/ijm-04-2018-0127.
  18. Cristiani, A., & Peiró, J. (2019). Calculative and collaborative hrm practices, turnover, and performance. International Journal of Manpower. URL: https://doi.org/10.1108/IJM-11-2016-0207.
  19. Haque, A. (2020). Strategic hrm and organizational performance: Does turnover intention matter? International Journal of Organizational Analysis. URL: https://doi.org/10.1108/ijoa-09-2019-1877.
  20. Papa, A., Dezi, L., Gregori, G., Mueller, J., & Miglietta, N. (2018). Improving innovation performance through knowledge acquisition: The moderating role of employee retention and human resource management practices. Journal of Knowledge Management, 24 (4), 589–605. URL: https://doi.org/10.1108/JKM-09-2017-0391.
  21. Elsafty, A., & Oraby, M. (2022). The impact of training on employee retention. International Journal of Business and Management, 17 (5). URL: https://doi.org/10.5539/ijbm.v17n5p58.
  22. Alabi, O. A., Ajayi, F. A., Udeh, C. A., & Efunniyi, C. P. (2024). Predictive analytics in human resources: Enhancing workforce planning and customer experience. International Journal of Research and Scientific Innovation. URL: https://doi.org/10.51244/ijrsi.2024.1109016.
  23. Shrestha, P., & Prajapati, M. P. (2024). Impact of strategic human resource management practices on employee retention. The Batuk. URL: https://doi.org/10.3126/batuk.v10i1.62298.
  24. Davidescu, A., Apostu, S.-A., Paul, A., & Cășuneanu, I. (2020). Work flexibility, job satisfaction, and job performance among Romanian employees – implications for sustainable human resource management. Sustainability, 12 (15), 6086. URL: https://doi.org/10.3390/su12156086.
  25. Lakshman, C., Wang, L., Adhikari, A., & Cheng, G. (2020). Flexibility-oriented hrm practices and innovation: Evidence from china and india. The International Journal of Human Resource Management, 33 (12), 2473–2502. URL: https://doi.org/10.1080/09585192.2020.1861057.
  26. Dutta, S., Ray, A., Chinya, M., Ghatak, S., Mukherjee, A., Bhattacharjee, K., & Das, A. (2024). Predictive hr analytics to optimize decision-making processes and enhance workforce performance. International Journal of Recent Trends in Multidisciplinary Research. URL: https://doi.org/10.59256/ijrtmr.20240402014.
  27. Tiwari, V. (2023). Revolutionizing workplace practices in human resource management with iot-enabled solutions and analytics. Financial Technology and Innovation. URL: https://doi.org/10.54216/fintech-i.020205.
  28. Safarishahrbijari, A. (2018). Workforce forecasting models: A systematic review. Journal of Forecasting. URL: https://doi.org/10.1002/FOR.2541.
  29. Nurani, M., Khuzaini, K., & Shaddiq, S. (2024). Competency-based hr management strategy in the digital era: Systematic literature review. At-Tadbir: Jurnal Ilmiah Manajemen. URL: https://doi.org/10.31602/piuk.v0i0.15798.
  30. Msacky, R. (2024). Retention of human resources for health in the decentralised health system in tanzania: Does training matter? Journal of Policy and Development Studies. URL: https://doi.org/10.4314/jpds.v16i1.5.