Human Factors, Organizational Culture, and Artificial Intelligence in Total Productive Maintenance

Oladapo Babafemi Fakiyesi¹, Charles Chikwendu Okpala², Obiora Jeremiah Obiafudo³

^1,3 Lecturer, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.

² Professor, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria

Abstract

Manufacturing organizations face mounting pressure to reconcile productivity demands with measurable environmental and social performance improvements. While Total Productive Maintenance (TPM) has long been associated with operational excellence, its integration with Artificial Intelligence (AI) and sustainability outcomes remains under-theorized and empirically fragmented. This study develops and tests a multilevel sociotechnical framework that links human factors, organizational culture, and AI-enabled predictive maintenance to Overall Equipment Effectiveness (OEE) and triple-bottom-line sustainability performance. Drawing on data from 84 manufacturing plants across 11 industrial sectors (N = 2,436 employees; 18,912 machine-month observations; 36 months of archival ESG data), the study employed Multilevel Structural Equation Modeling (MSEM) to simultaneously estimate within-plant and between-plant effects. The results indicate that human factors significantly predict OEE (β = 0.41, p < .001), organizational culture strengthens human capability deployment (β = 0.52, p < .001), and AI maturity amplifies the TPM-OEE relationship (interaction β = 0.29, p < .001). OEE mediates the relationship between sociotechnical capability and sustainability outcomes, explaining 52% of variance in environmental performance and 61% in economic performance. Plants in the highest AI-TPM maturity quartile achieved 21% lower energy intensity, 18% lower material scrap, 15% lower CO₂-equivalent emissions, 17% lower maintenance cost per unit, and 12% fewer lost-time injuries when compared to low-maturity counterparts. Methodologically, the study advances sustainable operations research through the integration of perceptual, sensor-derived, and archival ESG data within a cross-level moderated mediation framework. The findings demonstrate that AI-augmented TPM, when embedded within supportive human and cultural systems, constitutes a measurable pathway towards human-centered and environmentally sustainable manufacturing performance.     

Keywords: Total productive maintenance (TPM), Artificial intelligence, Overall equipment effectiveness (OEE), Multilevel structural equation modeling, Sustainable manufacturing, Organizational culture, Predictive maintenance

References

1. Aguh, P.S. and Okpala, C.C. (2025) ‘Learning in the age of artificial intelligence tutors: Cognitive outcomes and equity in automated education systems’, International Journal of Engineering Research and Development, 21(12), pp. 88-98. https://ijerd.com/paper/vol21-issue12/21128898.pdf
2. Aguh, P.S., Udu, C.E., Chukwumuanya, E.O., et al. (2025) ‘Machine learning applications for production scheduling optimization’, Journal of Exploratory Dynamic Problems, 2(4), pp. 63-79. https://edp.web.id/index.php/edp/article/view/137
3. Ahuja, I.P.S. and Khamba, J.S. (2008) ‘Total productive maintenance: Literature review and directions’, International Journal of Quality and Reliability Management, 25(7), pp. 709-756.
4. Bag, S., Gupta, S., Kumar, A., et al. (2021) ‘Role of technological dimensions of Industry 4.0 on circular economy practices and performance’, International Journal of Production Economics, 231, 107831.
5. Bamber, C.J., Sharp, J.M. and Hides, M.T. (1999) ‘Factors affecting successful implementation of total productive maintenance’, Journal of Quality in Maintenance Engineering, 5(3), pp. 162-181.
6. Barney, J. (1991) ‘Firm resources and sustained competitive advantage’, Journal of Management, 17(1), pp. 99-120.
7. Bliese, P.D. (2000) ‘Within-group agreement, non-independence, and reliability: Implications for data aggregation and analysis’. In: Multilevel theory, research, and methods in organizations; Klein, K.J. and Kozlowski, S.W.J. (eds.). San Francisco: Jossey-Bass, pp. 349-381.
8. Bousdekis, A., Magoutas, B., Apostolou, D., et al. (2020) ‘Review of data-driven decision-making methods for predictive maintenance’, Computers in Industry, 115, 103125.
9. Carvalho, T.P., Soares, F.A.A.M.N., Vita, R., et al. (2019) ‘A systematic literature review of machine learning methods applied to predictive maintenance’, Computers and Industrial Engineering, 137, 106024.
10. Chukwumuanya, E.O., Okpala, C.C. and Onukwuli, S.K. (2025) ‘Ergonomics-aware scheduling: Biomechanical models with production planning integration for musculoskeletal risk reduction’, Journal Majelis Paspama, 3(1), pp. 21-36. https://paspama.org/index.php/majelis/article/view/209
11. Chukwunedum, O.C., Okpala, C.C. and Udu, C.E. (2026) ‘A data-driven integration of total productive maintenance and Industry 4.0 technologies: A machine learning framework for predictive OEE optimization’, International Journal of Engineering Research and Development, 22(3), pp. 85-95. https://www.ijerd.com/paper/vol22-issue3/22038595.pdf
12. Denison, D.R. (1996) ‘What is the difference between organizational culture and organizational climate? A native’s point of view on a decade of paradigm wars’, Academy of Management Review, 21(3), pp. 619-654.
13. Deswal, P. (2025) ‘Article 6 of the Paris Agreement: A comprehensive review of mechanisms, progress, and persistent challenges’, International Journal of Technology, Health and Sustainability, 1(2), pp. 111-125. https://ijths.com/wp-content/uploads/IJTHS-010235.pdf
14. Deswal, S. and Deswal, P. (2025) ‘Sustainability: Greenhouse Gas Protocol and global GHG emissions’ status and trends’, International Journal of Multidisciplinary Research and Growth Evaluation, 6(1), pp. 2051-2063. https://doi.org/10.54660/.IJMRGE.2025.6.1.2051-2063
15. Dubey, R., Gunasekaran, A. and Ali, S.S. (2017) ‘Sustainable supply chain management: Theoretical foundations and future research directions’, International Journal of Production Economics, 195, pp. 27-38.
16. EC (2021) Industry 5.0: Towards a sustainable, human-centric and resilient European industry. European Commission, Publications Office of the European Union, Luxembourg.  https://op.europa.eu/en/publication-detail/-/publication/468a892a-5097-11eb-b59f-01aa75ed71a1/language-en
17. Egwuagu, O.M. and Okpala, C.C. (2016) ‘Benefits and challenges of total productive maintenance implementation’, International Journal of Advanced Engineering Technology, 7(3), pp. 196-200. http://www.technicaljournalsonline.com/ijeat/VOL%20VII/IJAET%20VOL%20VII%20ISSUE%20III%20JULY%20SEPTEMBER%202016.html
18. Elkington, J. (1997) Cannibals with forks: The triple bottom line of 21^st century business. Oxford: Capstone Publishing.
19. Ezeanyim, O.C., Okpala, C.C. and Igbokwe, B.N. (2025) ‘Precision agriculture with AI-powered drones: Enhancing crop health monitoring and yield prediction’, International Journal of Latest Technology in Engineering, Management and Applied Science, 14(3). https://doi.org/10.51583/IJLTEMAS.2025.140300020
20. Ezeanyim, O.C., Okpala, C.C. and Udu, C.E. (2026) ‘Artificial intelligence-enabled Lean Six Sigma: A multi-industry longitudinal analysis of operational performance and sustainable digital transformation’, International Journal of Technology, Health and Sustainability, 2(2), pp. 428-439. https://ijths.com/wp-content/uploads/IJTHS-0202001.pdf
21. Felin, T., Foss, N.J., Heimeriks, K.H., et al. (2012) ‘Microfoundations of routines and capabilities: Individuals, processes, and structure’, Journal of Management Studies, 49(8), pp. 1351-1374.
22. Frank, A.G., Dalenogare, L.S. and Ayala, N.F. (2019) ‘Industry 4.0 technologies: Implementation patterns in manufacturing companies’, International Journal of Production Economics, 210, pp. 15-26.
23. Godwin, H.C. and Okpala, C.C. (2026) ‘Data-driven ergonomic optimization in manufacturing systems: Productivity, safety, and sustainability impacts’, International Journal of Technology, Health and Sustainability, 2(2), pp. 695-704. https://ijths.com/wp-content/uploads/IJTHS-0202041.pdf
24. Hair, J.F., Black, W.C., Babin, B.J., et al. (2022) Multivariate data analysis. 8^th edn. Boston: Cengage.
25. Hart, S.L. and Dowell, G. (2011) ‘A natural-resource-based view of the firm: Fifteen years after’, Journal of Management, 37(5), pp. 1464-1479.
26. Hox, J.J., Moerbeek, M. and van de Schoot, R. (2017) Multilevel analysis: Techniques and applications. 3^rd edn. New York: Routledge.
27. IEA (2023) World energy outlook 2023. International Energy Agency Publications, Paris. https://www.iea.org/reports/world-energy-outlook-2023
28. Jaca, C., Viles, E., Paipa-Galeano, L., et al. (2012) ‘Learning 5S principles from Japanese best practitioners: Case studies of five manufacturing companies’, International Journal of Production Research, 50(17), pp. 4574-4586.
29. Kane, G.C., Palmer, D., Phillips, A.N., et al. (2015) ‘Strategy, not technology, drives digital transformation’, MIT Sloan Management Review, 14(1), pp. 1-25.
30. McKone, K.E., Schroeder, R.G. and Cua, K.O. (2001) ‘The impact of total productive maintenance practices on manufacturing performance’, Journal of Operations Management, 19(1), pp. 39-58.
31. Muchiri, P. and Pintelon, L. (2008) ‘Performance measurement using overall equipment effectiveness (OEE): Literature review and practical application discussion’, International Journal of Production Research, 46(13), pp. 3517-3535.
32. Nakajima, S. (1988) Introduction to TPM: Total productive maintenance. Cambridge, MA: Productivity Press.
33. Neal, A. and Griffin, M.A. (2006) ‘A study of the lagged relationships among safety climate, safety motivation, safety behavior, and accidents’, Journal of Applied Psychology, 91(4), pp. 946-953.
34. Nwamekwe, C.O. and Okpala, C.C. (2025) ‘Machine learning-augmented digital twin systems for predictive maintenance in high-speed rail networks’, International Journal of Multidisciplinary Research and Growth Evaluation, 6(1), pp. 1783-1795. https://www.allmultidisciplinaryjournal.com/uploads/archives/20250212104201_MGE-2025-1-306.1.pdf
35. Nwankwo, C.O., Ezeanyim, O.C., Okpala, C.C., et al. (2024) ‘Enhancing injection moulding productivity through overall equipment effectiveness and total preventive maintenance approach’, International Journal of Advances in Engineering and Management, 6(3), pp. 1-9. https://ijaem.net/issue_dcp/Enhancing%20Injection%20Moulding%20Productivity%20through%20Overall%20Equipment%20Effectiveness%20and%20Total%20Preventive%20Maintenance%20Approach.pdf
36. Okpala, C.C. (2026) ‘Human-centered design of jigs and fixtures: A data-driven approach to productivity, safety, and manufacturing sustainability’, International Journal of Technology, Health and Sustainability, 2(1), pp. 291-299. https://ijths.com/wp-content/uploads/IJTHS-020185.pdf
37. Okpala, C.C. and Aguh, P.S. (2025) ‘The human factor in the future of cybersecurity: Trust, privacy, and responsibility’, International Journal of Engineering Research and Development, 21(9), pp. 22-29. https://ijerd.com/paper/vol21-issue9/21092229.pdf
38. Okpala, C.C. and Anozie, S.C. (2018) ‘Overall equipment effectiveness and the six big losses in total productive maintenance’, Journal of Scientific and Engineering Research, 5(4), pp. 156-164. https://jsaer.com/download/vol-5-iss-4-2018/JSAER2018-05-04-156-164.pdf
39. Okpala, C.C. and Chukwumuanya, E.O. (2025) ‘The future of cybersecurity: Predictive analytics and machine learning applications’, Journal of Engineering Research and Applied Science, 14(2), pp. 190-201. https://www.journaleras.com/index.php/jeras/article/view/398
40. Okpala, C.C. and Nwankwo, C.O. (2025) ‘Blockchain and artificial intelligence integration in cybersecurity: Towards intelligent and decentralized defenses’, International Journal of Engineering Inventions, 14(9), pp. 9-17. https://www.ijeijournal.com/papers/Vol14-Issue9/14090917.pdf
41. Okpala, C.C. and Udu, C.E. (2025) ‘Autonomous drones and artificial intelligence: A new era of surveillance and security applications’, International Journal of Science, Engineering and Technology, 13(2), pp. 1-8. https://www.ijset.in/wp-content/uploads/IJSET_V13_issue2_520.pdf
42. Okpala, C.C. and Udu, C.E. (2026) ‘What Drives Successful Six Sigma Implementation? A Data-Driven Multidisciplinary Analysis’, International Journal of Technology, Health and Sustainability, 2(2), pp. 721-730. https://ijths.com/wp-content/uploads/IJTHS-0202048.pdf
43. Okpala, C.C., Anozie, S.C. and Ezeanyim, C.E. (2018) ‘The application of tools and techniques of total productive maintenance in manufacturing’, International Journal of Engineering Science and Computing, 8(6).  http://ijesc.org/articles-in-press.php?msg=1&page=article
44. Okpala, C.C., Anozie, S.C. and Mgbemena, C.E. (2020) ‘The optimization of overall equipment effectiveness factors in a pharmaceutical company’, Heliyon, 6, e03796. https://doi.org/10.1016/j.heliyon.2020.e03796
45. Okpala, C.C., Ezeanyim, O.C. and Igbokwe, N.C. (2023) ‘Human-robot interaction enhancement through ergonomics and human factors: Future directions’, International Journal of Engineering Research and Development, 19(6). http://www.ijerd.com/paper/vol19-issue6/E19063440.pdf
46. Okpala, C.C., Udu, C.E. and Nwamekwe, C.O. (2025) ‘Artificial intelligence-driven total productive maintenance: The future of maintenance in smart factories’, International Journal of Engineering Research and Development, 21(1), pp. 68-74. https://ijerd.com/paper/vol21-issue1/21016874.pdf
47. Okpala, S.C. and Okpala, C.C. (2025) ‘Harnessing big data and predictive analytics for modern healthcare delivery transformation’, International Journal of Health and Pharmaceutical Research, 10(7), pp. 92-108. DOI: 10.56201/ijhpr.vol.10.no7.2025.pg92.108
48. Onukwuli, S.K., Okpala, C.C. and Okpala, P.C. (2026) ‘The extension of total productive maintenance with digital intelligence for data-driven maintenance in smart manufacturing’, International Journal of Technology, Health and Sustainability, 2(2), pp. 883-892. https://ijths.com/wp-content/uploads/IJTHS-0202062.pdf
49. Onukwuli, S.K., Okpala, C.C., Edeh, M. O., et al. (2025) ‘Human-centric cybersecurity: The integration of psychological insights and sociotechnical systems’, International Journal of Industrial and Production Engineering, 3(4). https://journals.unizik.edu.ng/ijipe/article/view/6777
50. Pasmore, W., Winby, S. and Mohrman, S.A. (2019) ‘Sociotechnical systems: A North American reflection on empirical studies of the seventies’, Journal of Applied Behavioral Science, 55(3), pp. 1-21.
51. Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., et al. (2003) ‘Common method biases in behavioral research: A critical review of the literature’, Journal of Applied Psychology, 88(5), pp. 879-903.
52. Preacher, K.J., Zyphur, M.J. and Zhang, Z. (2010) ‘A general multilevel SEM framework for assessing multilevel mediation’, Psychological Methods, 15(3), pp. 209-233.
53. Schaltegger, S. and Burritt, R. (2018) ‘Business cases and corporate engagement with sustainability’, Accounting, Auditing and Accountability Journal, 31(4), pp. 1096-1119.
54. Schein, E.H. (2010) Organizational culture and leadership. 4^th edn. San Francisco: Jossey-Bass.
55. Stock, T. and Seliger, G. (2016) ‘Opportunities of sustainable manufacturing in Industry 4.0’, Procedia CIRP, 40, pp. 536-541.
56. Teece, D.J. (2018) ‘Business models and dynamic capabilities’, Long Range Planning, 51(1), pp. 40-49.
57. Udu, C.E. and Okpala, C.C. (2026a) ‘Artificial intelligence-enabled resilient scheduling: A systematic review and research roadmap for digital twin and machine learning in disruption-aware operations’, International Journal of Technology, Health and Sustainability, 2(2), pp. 486-497. https://ijths.com/wp-content/uploads/IJTHS-0202014.pdf
58. Udu, C.E. and Okpala, C.C. (2026b) ‘Engineering safety in complex systems: A data-driven and predictive framework for machine learning, human factors, and system dynamics integration’, International Journal of Technology, Health and Sustainability, 2(1), pp. 391-401. https://ijths.com/wp-content/uploads/IJTHS-020199-0.pdf
59. Udu, C.E., Okpala, C.C. and Edeh, M.O. (2025) ‘Global roadmap for circular economies: The integration of digital innovation, governance, and sustainable development goals’, International Journal of Industrial and Production Engineering, 3(4), pp. 1-17. https://journals.unizik.edu.ng/ijipe/article/view/6764