REFERENCES

1. R. Kaur and A. Sharma, "Foundational Elements of HR Analytics: Leveraging Data-Driven Insights to Enhance Decision-Making," *Journal of Human Resource Management*, vol. 5, no. 3, pp. 45-58, Sep. 2017.
2. J. Li, X. Zhang, and L. Wang, "Predictive Modeling Techniques for Forecasting Employee Turnover and Job Dissatisfaction in Manufacturing," *International Journal of Data Science*, vol. 12, no. 4, pp. 221-235, Dec. 2020.
3. S. Reddy and V. Srinivasan, "Application of Machine Learning Algorithms for Skill Gap Analysis and Performance Evaluation in Manufacturing Firms," *Manufacturing Science and Technology Review*, vol. 18, no. 2, pp. 132-147, Jun. 2019.
4. A. Smith, B. Jones, and C. Lee, "Enhancing Decision-Making with Data Visualization Tools in HR Analytics," *Data Science Journal*, vol. 8, no. 1, pp. 55-70, Mar. 2021.
5. algorithms used in  HR analytics so that they are placed under ethical practice hence addressing the fairest way employees
6. should be treated within an entity. Apart from these traditional uses, there is now an expanding area for dynamic real-time
7. monitoring on what is happening in our organizations i.e. we can use internet-connected devices such as Internet of Things
8. (IoT)  gadgetsand  wearabletechnology  forstaff  welfareand  intervention  purposes.  Additionally,  cross-disciplinary
9. involvement in conducting empirical research will enrich HRA through the incorporation of studies from these fields hence,
10. bringing about a comprehensive approach to the analysis of human resources.
11. Lastly, it  would be useful  toexplore  longitudinal research on  how HR Analytics  impacts organizational performance and
12. employee satisfaction in the long run. Therefore, there is need for organizations to use HR analytics applications that continue
13. being refined by incorporating new sources and measurement methods so as not to be left behind by changes in their business
14. environment. To sum up, HR Analytics represents a seismic shift in Human Resource Management that provides insights and
15. tools for strategic decision-making and organizational success. While the future for HR Analytics may  seem limitless with
16. technological advancements  it represents nothing  butan  integral  part of modern  HRMpractices,  capable of transforming
17. workforce productivity and influencing business results.
18.  
19. References
20.  1. McCartney,  S.  and Fu,  N., 2022.  Promise versus  reality: A  systematicreview  ofthe  ongoingdebates  in people
21. analytics. Journal of Organizational Effectiveness: People and Performance, 9(2), pp.281-311.
22. 2. Dahlbom, P., Siikanen, N., Sajasalo, P. and Jarvenpää, M., 2020. Big data and HR analytics in the digital era. Baltic
23. Journal of Management, 15(1), pp.120-138.
24. 3. Lubis, N.W., 2022. Resource based view (RBV) in improving company strategic capacity. Research Horizon, 2(6),
25. pp.587-596.
26. 4. Bawono, S., 2021. Human capital, technology, and economic growth: A case study of Indonesia. Journal of Asian
27. Finance, Economics and Business.
28. 5. Shet, S.V., Poddar, T., Samuel, F.W. and Dwivedi, Y.K., 2021. Examining the determinants of successful adoption
29. of data analytics in human resource management–A framework for implications. Journal of Business Research, 131,
30. pp.311-326.
31. 6. Hakimi Niasari, K., Mojibi, T., Mahdizadeh Ashrafi, A. and Jahanguirfard, M., 2020. An analysis of the components
32. of  evidence  based succession  planning  in the  privatesector  of Iran. Journal  ofResearch  inHuman  Resources
33. Management, 12(2), pp.217-250.
34. 7. David,  M.E.,  David,  F.R.and  David,  F.R.,  2021.  Closing  thegap  between  graduates’  skillsand  employers’
35. requirements: A focus on the strategic management capstone business course. Administrative sciences, 11(1), p.10.
36. 8. Ajayi, F.A.  and Udeh,  C.A., 2024. Innovative  recruitment strategies in  the IT sector: A  review of successes and
37. failures. Magna Scientia Advanced Research and Reviews, 10(02), pp.150-164.
38. 9. Paul,  T.  and Bommu,  R.,  2024. Strategic  Employee  Performance Analysis  in  the USA:  Leveraging  Intelligent
39. Machine Learning Algorithms. Unique Endeavor in Business & Social Sciences, 3(1), pp.113-124.
40. 10. El-Rayes,  N.,  Fang,  M.,  Smith,  M.and  Taylor,  S.M.,  2020.  Predicting  employeeattrition  using  tree-based
41. models. International Journal of Organizational Analysis, 28(6), pp.1273-1291.
42. 11. Taranenko, I., Chychun, V., Korolenko, O., Goncharenko, I. and Zhuvahina, I., 2021. Management of the Process of
43. E-Commerce  Developmentin  Businesson  theExample  ofthe  European  Union. Studies  of  Applied
44. Economics, 39(5).
45. 12. Yu, P.K., 2020. The algorithmic divide and equality in the age of artificial intelligence. Fla. L. Rev., 72, p.331.
46. 13. Baviskar, D., Ahirrao,  S.,  Potdar,  V.and  Kotecha,  K., 2021.  Efficient  automatedprocessing  of the unstructured
47. documents  usingartificial  intelligence:  A  systematicliterature  reviewand  future  directions. IEEE  Access, 9,
48. pp.72894-72936.
49. 14. Jin, Z., Shang, J., Zhu, Q., Ling, C., Xie, W. and Qiang, B., 2020. RFRSF: Employee turnover prediction based on
50. random  forestsand  survival analysis.  In Web  InformationSystems  Engineering–WISE  2020:  21st  International
51. Conference, Amsterdam,  TheNetherlands,  October 20–24, 2020,  Proceedings, Part  II 21 (pp. 503-515).  Springer
52. International Publishing.

Cite This Article As: Akshay NLN (2026), “Leveraging HR Analytics and AI for Strategic Decision-Making in the Manufacturing Sector”, International Journal of Human Resource& Industrial Research, Volume 13, Issue 1, 2026, pp 01-10.   DOI : https://doi.org/10.5281/zenodo.18979968

REFERENCES

1. R. Kaur and A. Sharma, "Foundational Elements of HR Analytics: Leveraging Data-Driven Insights to Enhance Decision-Making," *Journal of Human Resource Management*, vol. 5, no. 3, pp. 45-58, Sep. 2017.
2. J. Li, X. Zhang, and L. Wang, "Predictive Modeling Techniques for Forecasting Employee Turnover and Job Dissatisfaction in Manufacturing," *International Journal of Data Science*, vol. 12, no. 4, pp. 221-235, Dec. 2020.
3. S. Reddy and V. Srinivasan, "Application of Machine Learning Algorithms for Skill Gap Analysis and Performance Evaluation in Manufacturing Firms," *Manufacturing Science and Technology Review*, vol. 18, no. 2, pp. 132-147, Jun. 2019.
4. A. Smith, B. Jones, and C. Lee, "Enhancing Decision-Making with Data Visualization Tools in HR Analytics," *Data Science Journal*, vol. 8, no. 1, pp. 55-70, Mar. 2021.
5. algorithms used in  HR analytics so that they are placed under ethical practice hence addressing the fairest way employees
6. should be treated within an entity. Apart from these traditional uses, there is now an expanding area for dynamic real-time
7. monitoring on what is happening in our organizations i.e. we can use internet-connected devices such as Internet of Things
8. (IoT)  gadgetsand  wearabletechnology  forstaff  welfareand  intervention  purposes.  Additionally,  cross-disciplinary
9. involvement in conducting empirical research will enrich HRA through the incorporation of studies from these fields hence,
10. bringing about a comprehensive approach to the analysis of human resources.
11. Lastly, it  would be useful  toexplore  longitudinal research on  how HR Analytics  impacts organizational performance and
12. employee satisfaction in the long run. Therefore, there is need for organizations to use HR analytics applications that continue
13. being refined by incorporating new sources and measurement methods so as not to be left behind by changes in their business
14. environment. To sum up, HR Analytics represents a seismic shift in Human Resource Management that provides insights and
15. tools for strategic decision-making and organizational success. While the future for HR Analytics may  seem limitless with
16. technological advancements  it represents nothing  butan  integral  part of modern  HRMpractices,  capable of transforming
17. workforce productivity and influencing business results.
18.  
19. References
20.  1. McCartney,  S.  and Fu,  N., 2022.  Promise versus  reality: A  systematicreview  ofthe  ongoingdebates  in people
21. analytics. Journal of Organizational Effectiveness: People and Performance, 9(2), pp.281-311.
22. 2. Dahlbom, P., Siikanen, N., Sajasalo, P. and Jarvenpää, M., 2020. Big data and HR analytics in the digital era. Baltic
23. Journal of Management, 15(1), pp.120-138.
24. 3. Lubis, N.W., 2022. Resource based view (RBV) in improving company strategic capacity. Research Horizon, 2(6),
25. pp.587-596.
26. 4. Bawono, S., 2021. Human capital, technology, and economic growth: A case study of Indonesia. Journal of Asian
27. Finance, Economics and Business.
28. 5. Shet, S.V., Poddar, T., Samuel, F.W. and Dwivedi, Y.K., 2021. Examining the determinants of successful adoption
29. of data analytics in human resource management–A framework for implications. Journal of Business Research, 131,
30. pp.311-326.
31. 6. Hakimi Niasari, K., Mojibi, T., Mahdizadeh Ashrafi, A. and Jahanguirfard, M., 2020. An analysis of the components
32. of  evidence  based succession  planning  in the  privatesector  of Iran. Journal  ofResearch  inHuman  Resources
33. Management, 12(2), pp.217-250.
34. 7. David,  M.E.,  David,  F.R.and  David,  F.R.,  2021.  Closing  thegap  between  graduates’  skillsand  employers’
35. requirements: A focus on the strategic management capstone business course. Administrative sciences, 11(1), p.10.
36. 8. Ajayi, F.A.  and Udeh,  C.A., 2024. Innovative  recruitment strategies in  the IT sector: A  review of successes and
37. failures. Magna Scientia Advanced Research and Reviews, 10(02), pp.150-164.
38. 9. Paul,  T.  and Bommu,  R.,  2024. Strategic  Employee  Performance Analysis  in  the USA:  Leveraging  Intelligent
39. Machine Learning Algorithms. Unique Endeavor in Business & Social Sciences, 3(1), pp.113-124.
40. 10. El-Rayes,  N.,  Fang,  M.,  Smith,  M.and  Taylor,  S.M.,  2020.  Predicting  employeeattrition  using  tree-based
41. models. International Journal of Organizational Analysis, 28(6), pp.1273-1291.
42. 11. Taranenko, I., Chychun, V., Korolenko, O., Goncharenko, I. and Zhuvahina, I., 2021. Management of the Process of
43. E-Commerce  Developmentin  Businesson  theExample  ofthe  European  Union. Studies  of  Applied
44. Economics, 39(5).
45. 12. Yu, P.K., 2020. The algorithmic divide and equality in the age of artificial intelligence. Fla. L. Rev., 72, p.331.
46. 13. Baviskar, D., Ahirrao,  S.,  Potdar,  V.and  Kotecha,  K., 2021.  Efficient  automatedprocessing  of the unstructured
47. documents  usingartificial  intelligence:  A  systematicliterature  reviewand  future  directions. IEEE  Access, 9,
48. pp.72894-72936.
49. 14. Jin, Z., Shang, J., Zhu, Q., Ling, C., Xie, W. and Qiang, B., 2020. RFRSF: Employee turnover prediction based on
50. random  forestsand  survival analysis.  In Web  InformationSystems  Engineering–WISE  2020:  21st  International
51. Conference, Amsterdam,  TheNetherlands,  October 20–24, 2020,  Proceedings, Part  II 21 (pp. 503-515).  Springer
52. International Publishing.