Identification and Causal Analysis of Artificial Intelligence Capabilities for Advancing Total Productive Maintenance toward World-Class Standards in the Automotive Industry

Saeed  Mohammadi Rad; Hassan Farsijani; Seyyed Hosein  Seyyed Esfahani; Maryam  Khademi

Authors

Saeed Mohammadi Rad Department of Management, NT.C., Islamic Azad University, Tehran, Iran Author
Hassan Farsijani Associate Professor, Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran Corresponding author
Seyyed Hosein Seyyed Esfahani Department of Management, NT.C., Islamic Azad University, Tehran, Iran Author
Maryam Khademi Department of Mathematics, ST.C., Islamic Azad University, Tehran, Iran Author

Keywords:

Automotive Industry, Total Productive Maintenance, Artificial Intelligence, World, Class TPM

Abstract

In the era of Industry 5.0, transforming Total Productive Maintenance (TPM) in the automotive industry requires moving beyond reactive and experience-based maintenance toward a data-driven, predictive, and artificial intelligence-based system. Although research on intelligent maintenance has expanded considerably, much of the existing literature focuses on isolated algorithms, prediction accuracy, or narrow technological applications, while insufficient attention has been paid to identifying which AI capabilities are valid for enabling World-Class TPM and how they operate within a causal influence network. This study aims to identify, validate, and causally analyze artificial intelligence capabilities for World-Class TPM in the automotive industry. First, initial capabilities were extracted through thematic analysis of expert data. A single-round fuzzy Delphi method, was then applied to screen and stabilize the core capabilities, while expert consensus was controlled through fuzzy distance, consensus percentage, and alpha-cut criteria. The AI tools corresponding to the selected capabilities were conceptually validated based on the literature. Finally, fuzzy DEMATEL was used to analyze the intensity and direction of causal relationships. The findings show that 9 out of 20 initial capabilities were stabilized as core AI capabilities. Dynamic failure prediction, preventive quality control, resilient supply chain, and intelligent continuous improvement emerged as the strongest causal and leverage capabilities. The study contributes by providing a validated and causally analyzed set of AI capabilities that can support managerial decision-making for prioritizing World-Class TPM investments in the automotive industry.

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References

Andrianandrianina Johanesa, T. V., Equeter, L., & Mahmoudi, S. A. (2024). Survey on AI applications for product quality control and predictive maintenance in Industry 4.0. Electronics, 13(5), 976. https://doi.org/10.3390/electronics13050976

Ardehi, A., Javanmard, H., & Pilevari, N. (2023). Designing an Industry 4.0 implementation model to achieve sustainable development goals: A case study of Iran Khodro Company. International Journal of Industrial Engineering & Production Research, 34(2), 1-18. https://doi.org/10.22075/ijnaa.2023.29286.4111

Ebrahimi, S. H. (2023). Some equations to identify the threshold value in the DEMATEL method. Operations Research and Decisions, 33(2), 5-19.

Farsijani, H., & Dehghan, A. (2016). Performance evaluation for achieving world-class products based on the VU method. Industrial Management Perspective, 6(2), 45-65.

Farsijani, H., Shafiei Nikabadi, M., & Mojibian, F. (2012). The role of total productive maintenance in group technology to achieve world-class status. International Journal of Applied Industrial Engineering, 1(2), 25-35. https://doi.org/10.4018/ijaie.2012070103

Giordano, V., & Fantoni, G. (2025). Decomposing maintenance actions into sub-tasks using natural language processing: A case study in an Italian automotive company. Computers in Industry, 164, 104186. https://doi.org/10.1016/j.compind.2024.104186

Govindan, K., Khodaverdi, R., & Vafadarnikjoo, A. (2015). Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain. Expert Systems with Applications, 42(20), 7207-7220. https://doi.org/10.1016/j.eswa.2015.04.030

Hector, I., & Panjanathan, R. (2024). Predictive maintenance in Industry 4.0: A survey of planning models and machine learning techniques. Peerj Computer Science, 10, e2016. https://doi.org/10.7717/peerj-cs.2016

Hershowitz, B., Hodkiewicz, M., Bikaun, T., Stewart, M., & Liu, W. (2024). Causal knowledge extraction from long text maintenance documents. Computers in Industry, 161, 104110. https://doi.org/10.1016/j.compind.2024.104110

Hossain, M., Rahman, M., & Ramasamy, D. (2024). Artificial intelligence-driven vehicle fault diagnosis to revolutionize automotive maintenance: A review. Computer Modeling in Engineering & Sciences, 141(2), 951-996. https://doi.org/10.32604/cmes.2024.056022

Khan, T., Khan, U., Khan, A., Mollan, C., Morkvenaite-Vilkonciene, I., & Pandey, V. (2025). Data-driven digital twin framework for predictive maintenance of smart manufacturing systems. Machines, 13(6), 481. https://doi.org/10.3390/machines13060481

Kotecha, N., & del Rio-Chanona, E. A. (2025). Leveraging graph neural networks and multi-agent reinforcement learning for inventory control in supply chains. Computers & chemical engineering, 199, 109111. https://doi.org/10.1016/j.compchemeng.2025.109111

Li, J., Chen, M., Wang, Y., Liu, Y., & Zhang, H. (2025). Application of an integrated DEMATEL-ISM-BN and gray relational analysis model. Sustainability, 17(16), 7204.

Li, Z., He, Q., & Li, J. (2024). A survey of deep learning-driven architecture for predictive maintenance. Engineering Applications of Artificial Intelligence, 133, 108285. https://doi.org/10.1016/j.engappai.2024.108285

Liang, L., & Chen, J. (2025). VST-YOLOv8: A trustworthy and secure defect detection framework for industrial gaskets. Electronics, 14(19), 3760. https://doi.org/10.3390/electronics14193760

Lucantoni, L., Antomarioni, S., Ciarapica, F. E., & Bevilacqua, M. (2025). A data-driven framework for supporting the Total Productive Maintenance strategy. Expert Systems with Applications, 268, 126283. https://doi.org/10.1016/j.eswa.2024.126283

Mahale, Y., Kolhar, S., & More, A. S. (2025). A comprehensive review on artificial intelligence-driven predictive maintenance in vehicles: Technologies, challenges and future research directions. Discover Applied Sciences, 7, 243. https://doi.org/10.1007/s42452-025-06681-3

Morales Matamoros, O., Takeo Nava, J. G., Moreno Escobar, J. J., & Ceballos Chávez, B. A. (2025). Artificial intelligence for quality defects in the automotive industry: A systemic review. Sensors, 25(5), 1288. https://doi.org/10.3390/s25051288

Mouhib, Z., Gallab, M., Merzouk, S., Soulhi, A., Di Nardo, M., & El Alami, J. (2025). Total Productive Maintenance and Industry 4.0: A literature-based path toward a proposed standardized framework. Applied System Innovation, 8(4), 98.

Mozaffari Mehr, M., & Taghavi Fard, M. (2024). Designing and modeling digital transformation based on the Fourth Industrial Revolution in the automotive industry: A case study of Iran Khodro Company. Industrial Management, 16(1), 148-174. https://doi.org/10.22059/imj.2024.373674.1008135

Nyimbili, P. H., Erden, T., & Karaman, H. (2023). A DEMATEL-based approach of multi-criteria evaluation for urban disaster resilience. Frontiers in Environmental Economics, 2, 1198541. https://doi.org/10.3389/frevc.2023.1198541

Taoufyq, H., El Guemmat, K., Mansouri, K., & Akef, F. (2025). Predictive maintenance approaches: A systematic literature review. Journal of Industrial Engineering and Management, 18(3), 427-458. https://doi.org/10.3926/jiem.8537

Toke, L. K., & Kalpande, S. D. (2023). An assessment of key performance indicators and its relationship for implementation of total productive maintenance in manufacturing sector. International Journal on Interactive Design and Manufacturing, 17(4), 1741-1753. https://doi.org/10.1007/s12008-023-01252-5

Wang, Y., & Liang, X. (2025). Application of reinforcement learning methods combining graph neural networks and self-attention mechanisms in supply-chain route optimization. Sensors, 25(3), 955. https://doi.org/10.3390/s25030955

Wang, Y., Yuan, T., Yang, Y., He, M., Wu, R., & Birman, K. P. (2025). Accelerating visual anomaly detection in smart manufacturing with RDMA-enabled data infrastructure. Electronics, 14(12), 2427. https://doi.org/10.3390/electronics14122427

Yazdinejad, A., Kazemi, M., Parizi, R. M., Dehghantanha, A., & Karimipour, H. (2023). An ensemble deep learning model for cyber threat hunting in industrial internet of things. Digital Communications and Networks, 9(1), 101-110. https://doi.org/10.1016/j.dcan.2022.09.008