Menedzsmenttudományok

Advancing Maintenance 4.0 through an Asset Management Framework: a South African Petrochemical Industry Case Study

Megjelent:
2025-11-05
Szerzők
Megtekintés
Kulcsszavak
Licenc

Copyright (c) 2025 Rina Peach, Refilwe Matsha

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Hogyan hivatkozzuk
Kiválasztott formátum: APA
Peach, R., & Matsha, R. (2025). Advancing Maintenance 4.0 through an Asset Management Framework: a South African Petrochemical Industry Case Study. International Journal of Engineering and Management Sciences, 1-20. https://doi.org/10.21791/IJEMS.2025.20
Beküldött 2025-08-12
Elfogadott 2025-10-29
Publikált 2025-11-05
Absztrakt

The rapid advancement of digital technologies has raised uncertainty about the adequacy of traditional maintenance models to meet Industry 4.0 requirements. This study develops and validates an asset management framework to support the South African petrochemical industry’s transition to Maintenance 4.0. The framework was validated through a quantitative survey conducted within a leading petrochemical company in South Africa, ensuring its practical applicability. Descriptive statistical analysis confirmed 15 of 17 framework characteristics and supported five of seven theoretical propositions. Key enablers of Maintenance 4.0 adoption include the integration of human intelligence, machine learning, and real-time data, as well as the role of organizational culture and asset resilience in shaping outcomes. The study offers both theoretical contributions and practical guidance for maintenance professionals seeking to align maintenance practices with Industry 4.0 principles, with relevance extending beyond the immediate case context.

Hivatkozások
  1. [1] S. Werbińska-Wojciechowska and K. Winiarska, "Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review," (in eng), Sensors (Basel), vol. 23, no. 3, Jan 27 2023, doi: 10.3390/s23031409.
  2. [2] M. Haarman, M. Mulders, and C. Vassiliadis, "Predictive maintenance 4.0: predict the unpredictable," PwC and Mainnovation, vol. 4, 2017.
  3. [3] M. Achouch et al., "On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges," Applied Sciences, vol. 12, no. 16, p. 8081, 2022. [Online]. Available: https://www.mdpi.com/2076-3417/12/16/8081.
  4. [4] I. Els and K. Visser, "Application of industry standards and management commitment to asset management in a petrochemical company," in Proceedings of the 32nd European Safety and Reliability Conference, ESREL 2022 - Understanding and Managing Risk and Reliability for a Sustainable Future, 2022, pp. 72–79, doi: 10.3850/978-981-18-5183-4_R02-02-135-cd. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208274329&doi=10.3850%2f978-981-18-5183-4_R02-02-135-cd&partnerID=40&md5=cb756c2b07e4b17b7fcdf5f422f83528
  5. [5] Z. Li, K. Wang, and Y. He, "Industry 4.0-potentials for predictive maintenance," in 6th International Workshop of Advanced Manufacturing and Automation, 2016: Atlantis Press, pp. 42–46.
  6. [6] J. Bokrantz, A. Skoogh, C. Berlin, T. Wuest, and J. Stahre, "Smart Maintenance: a research agenda for industrial maintenance management," International journal of production economics, vol. 224, p. 107547, 2020.
  7. [7] Z. Shi, Y. Xie, W. Xue, Y. Chen, L. Fu, and X. Xu, "Smart factory in Industry 4.0," Systems Research and Behavioral Science, vol. 37, no. 4, pp. 607–617, 2020.
  8. [8] C. Zhuang, J. Liu, and H. Xiong, "Digital twin-based smart production management and control framework for the complex product assembly shop-floor," The international journal of advanced manufacturing technology, vol. 96, pp. 1149–1163, 2018.
  9. [9] W. Yu, P. Patros, B. Young, E. Klinac, and T. G. Walmsley, "Energy digital twin technology for industrial energy management: Classification, challenges and future," Renewable and Sustainable Energy Reviews, vol. 161, p. 112407, 2022.
  10. [10] M. M. Mabkhot, A. M. Al-Ahmari, B. Salah, and H. Alkhalefah, "Requirements of the smart factory system: A survey and perspective," Machines, vol. 6, no. 2, p. 23, 2018.
  11. [11] S. Suakanto, E. T. Nuryatno, R. Fauzi, R. Andreswari, and V. S. Yosephine, "Conceptual Asset Management framework: A Grounded Theory Perspective," presented at the 2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS), 2021.
  12. [12] ISO, "ISO 55000: 2014: Asset management–overview, principles and terminology," ed: ISO Geneva, Switzerland, 2014.
  13. [13] J. Campos, P. Sharma, U. G. Gabiria, E. Jantunen, and D. Baglee, "A Big Data Analytical Architecture for the Asset Management," Procedia CIRP, vol. 64, pp. 369–374, 2017/01/01/ 2017, doi: https://doi.org/10.1016/j.procir.2017.03.019.
  14. [14] J. E. Cates, S. S. Gill, and N. Zeituny, "The Ladder of Business Intelligence (LOBI): a framework for enterprise IT planning and architecture," International Journal of Business Information Systems, vol. 1, no. 1-2, pp. 220–238, 2005.
  15. [15] J. H. Loaiza and R. J. Cloutier, "Analyzing the implementation of a digital twin manufacturing system: Using a systems thinking approach," Systems, vol. 10, no. 2, p. 22, 2022.
  16. [16] T. R. Wanasinghe et al., "Digital twin for the oil and gas industry: Overview, research trends, opportunities, and challenges," IEEE access, vol. 8, pp. 104175–104197, 2020.
  17. [17] D. T. Anh, K. Dąbrowski, and K. Skrzypek, "The predictive maintenance concept in the maintenance department of the “Industry 4.0” production enterprise," Foundations of Management, vol. 10, no. 1, pp. 283–292, 2018.
  18. [18] L. R. A. Manickam, "Proposal for the fourth generation of maintenance and the future trends & challenges in production," 2012.
  19. [19] N. N. Hien, G. Lasa, I. Iriarte, and G. Unamuno, "An overview of Industry 4.0 applications for advanced maintenance services," Procedia Computer Science, vol. 200, pp. 803–810, 2022.
  20. [20] F. Zeghmar, L. Benmansour, and L. Zemmouchi-Ghomari, "Maintenance 4.0 Systems Architecture: Challenges and Opportunities," 2022.
  21. [21] T. Mashaba and T. N. D. Mathaba, "Evaluating the Impact of Integrating Fourth Industrial Revolution (4IR) Technologies into Maintenance of Pressure Vessels and Pipelines in the Petrochemical Industry," Journal of Pipeline Science and Engineering, p. 100283, 2025/03/26/ 2025, doi: https://doi.org/10.1016/j.jpse.2025.100283.
  22. [22] M. A. Navas, C. Sancho, and J. Carpio, "Disruptive Maintenance Engineering 4.0," International Journal of Quality & Reliability Management, vol. 37, no. 6/7, pp. 853–871, 2020, doi: 10.1108/ijqrm-09-2019-0304.
  23. [23] A. Arnaiz, E. Konde, and J. Alarcón, "Continuous improvement on information and on-line maintenance technologies for increased cost-effectiveness," Procedia CIRP, vol. 11, pp. 193–198, 2013.
  24. [24] L. Spendla, M. Kebisek, P. Tanuska, and L. Hrcka, "Concept of predictive maintenance of production systems in accordance with industry 4.0," in 2017 IEEE 15Th International symposium on applied machine intelligence and informatics (SAMI), 2017: IEEE, pp. 000405–000410.
  25. [25] M. Jasiulewicz-Kaczmarek, S. Legutko, and P. Kluk, "Maintenance 4.0 technologies–new opportunities for sustainability driven maintenance," Management and production engineering review, vol. 11, 2020.
  26. [26] B. Al-Najjar, H. Algabroun, and M. Jonsson, "Maintenance 4.0 to fulfil the demands of Industry 4.0 and Factory of the Future," International Journal of Engineering Research and Applications, vol. 8, no. 11, pp. 20–31, 2018.
  27. [27] K. Charmaz and R. Thornberg, "The pursuit of quality in grounded theory," Qualitative Research in Psychology, vol. 18, no. 3, pp. 305–327, 2021/07/03 2021, doi: 10.1080/14780887.2020.1780357.
  28. [28] J. Y. Cho and E.-H. Lee, "Reducing confusion about grounded theory and qualitative content analysis: Similarities and differences," Qualitative report, vol. 19, no. 32, 2014.
  29. [29] Y. Chun Tie, M. Birks, and K. Francis, "Grounded theory research: A design framework for novice researchers," (in eng), SAGE Open Med, vol. 7, p. 2050312118822927, 2019, doi: 10.1177/2050312118822927.
  30. [30] C. Makri and A. Neely, "Grounded Theory: A Guide for Exploratory Studies in Management Research," International Journal of Qualitative Methods, vol. 20, p. 16094069211013654, 2021, doi: 10.1177/16094069211013654.
  31. [31] J. Sembiring, D. E. Nuryatno, and Y. S. Gondokaryono, "Analyzing the indicators and requirements in main components of Enterprise Architecture methodology development Using Grounded Theory in qualitative methods," in Society of Interdisciplinary Business Research (SIBR) 2011 Conference on Interdisciplinary Business Research, 2011, doi: 10.2139/ssrn.1867875.
  32. [32] G. Culot, G. Nassimbeni, G. Orzes, and M. Sartor, "Behind the definition of Industry 4.0: Analysis and open questions," International Journal of Production Economics, vol. 226, p. 107617, 2020/08/01/ 2020, doi: https://doi.org/10.1016/j.ijpe.2020.107617.
  33. [33] F. J. Folgado, D. Calderón, I. González, and A. J. Calderón, "Review of Industry 4.0 from the Perspective of Automation and Supervision Systems: Definitions, Architectures and Recent Trends," Electronics, vol. 13, no. 4, p. 782, 2024. [Online]. Available: https://www.mdpi.com/2079-9292/13/4/782.
  34. [34] L. Li, "Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 and Beyond," Information Systems Frontiers, vol. 26, no. 5, pp. 1697–1712, 2024/10/01 2024, doi: 10.1007/s10796-022-10308-y.
  35. [35] B. Mrugalska and J. Ahmed, "Organizational Agility in Industry 4.0: A Systematic Literature Review," Sustainability, vol. 13, no. 15, p. 8272, 2021. [Online]. Available: https://www.mdpi.com/2071-1050/13/15/8272.
  36. [36] M. Jaafar, K. N. Khan, and A. Salman, "A systematic review and framework for organizational agility antecedents towards industry 4.0," Management Review Quarterly, 2025/02/12 2025, doi: 10.1007/s11301-025-00489-6.
  37. [37] Y. Li, Q. Wang, X. Pan, J. Zuo, J. Xu, and Y. Han, "Digital Twins for Engineering Asset Management: Synthesis, Analytical Framework, and Future Directions," Engineering, vol. 41, pp. 261–275, 2024/10/01/ 2024, doi: https://doi.org/10.1016/j.eng.2023.12.006.
  38. [38] G. L. Rajora, M. Sanz-Bobi, L. Bertling Tjernberg, and J. Urrea Cabus, "A review of asset management using artificial intelligence‐based machine learning models: Applications for the electric power and energy system," IET Generation, Transmission & Distribution, vol. 18, 06/12 2024, doi: 10.1049/gtd2.13183.
  39. [39] C. B. H. Nel and J. Jooste, "A technologically-driven asset management approach to managing physical assets-a literature review and research agenda for'smart'asset management," South African Journal of Industrial Engineering, vol. 27, no. 4, pp. 50–65, 2016, doi: https://doi.org/10.7166/27-4-1478.
  40. [40] T. Zhu, Y. Ran, X. Zhou, and Y. Wen, "A survey of predictive maintenance: Systems, purposes and approaches," arXiv preprint arXiv:1912.07383, 2019.
  41. [41] H. Smith, "AI-Driven Predictive Maintenance 2.0: Self-Healing Systems and Automated Fault Diagnosis," 02/14 2023.
  42. [42] M. Molęda, B. Małysiak-Mrozek, W. Ding, V. Sunderam, and D. Mrozek, "From Corrective to Predictive Maintenance-A Review of Maintenance Approaches for the Power Industry," (in eng), Sensors (Basel), vol. 23, no. 13, Jun 27 2023, doi: 10.3390/s23135970.
  43. [43] S. Ma, K. A. Flanigan, and M. Bergés, "State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies," Advanced Engineering Informatics, vol. 62, p. 102800, 2024/10/01/ 2024, doi: https://doi.org/10.1016/j.aei.2024.102800.
  44. [44] S. S. A. Basuki and N. Kurniati, "Asset Useful Life Evaluation Model by Annualized Total Cost of Ownership."
  45. [45] J. E. Amadi-Echendu, K. Brown, R. Willett, and J. Mathew, Definitions, Concepts and Scope of Engineering Asset Management (Engineering Asset Management Review). Springer Verlag London Limited, 2010.
  46. [46] D. G. Carmichael, "Risk – a commentary," Civil Engineering and Environmental Systems, vol. 33, no. 3, pp. 177–198, 2016/07/02 2016, doi: 10.1080/10286608.2016.1202932.
  47. [47] L. Pinciroli, P. Baraldi, and E. Zio, "Maintenance optimization in industry 4.0," Reliability Engineering & System Safety, vol. 234, p. 109204, 2023/06/01/ 2023, doi: https://doi.org/10.1016/j.ress.2023.109204.
  48. [48] Z. Kang, C. Catal, and B. Tekinerdogan, "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks," (in eng), Sensors (Basel), vol. 21, no. 3, Jan 30 2021, doi: 10.3390/s21030932.
  49. [49] I. El-Thalji, "Emerging Practices in Risk-Based Maintenance Management Driven by Industrial Transitions: Multi-Case Studies and Reflections," Applied Sciences, vol. 15, no. 3, p. 1159, 2025. [Online]. Available: https://www.mdpi.com/2076-3417/15/3/1159.
  50. [50] C. Lalonde and O. Boiral, "Managing risks through ISO 31000: A critical analysis," Risk Management, vol. 14, no. 4, pp. 272–300, 2012/11/01 2012, doi: 10.1057/rm.2012.9.
  51. [51] E. Gavrikova, I. Volkova, and Y. Burda, "Strategic Aspects of Asset Management: An Overview of Current Research," Sustainability, vol. 12, no. 15, p. 5955, 2020. [Online]. Available: https://www.mdpi.com/2071-1050/12/15/5955.
  52. [52] I. Diop, G. Abdul-Nour, and D. Komljenovic, "Overview of strategic approach to asset management and decision-making," International Journal of Engineering Research & Technology, vol. 10, pp. 64–89, 2021.