How to cite this paper
APA: Yahyaoui, S., Bilad, A., Zaim, M & Zaim, F. (2026). Artificial intelligence towards a smart automotive supply chain performance KPIs aligned with IATF 16949 standards. Uncertain Supply Chain Management, 14(3), 213-226.
Chicago/Turabian: Yahyaoui, S., Bilad, A., Zaim, M & Zaim, F. 2026. "Artificial intelligence towards a smart automotive supply chain performance KPIs aligned with IATF 16949 standards." Uncertain Supply Chain Management 14, no. 3 (2026): 213-226.
AMA: Yahyaoui, S., Bilad, A., Zaim, M & Zaim, F. Artificial intelligence towards a smart automotive supply chain performance KPIs aligned with IATF 16949 standards. Uncertain Supply Chain Management. 2026;14(3):213-226.
References
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Assia, B., Mounia, Z., & Faical, Z. (2024). A survey study on Industry 4.0 for Moroccan manufacturing. ActaLogistica (AL), 11(3). https://doi.org/10.22306/al.v11i3.525
Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European journal of operational research, 184(3), 1140-1154. https://doi.org/10.1016/j.ejor.2006.12.004
Chinnaiyan, B., Balasubaramanian, S., Jeyabalu, M., & Warrier, G. S. (2025). AI Applications–Computer Vision and Natural Language Processing. Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications, 25-41. https://doi.org/10.1002/9781394219230.ch2
Chouchene, A., Carvalho, A., Lima, T. M., Charrua-Santos, F., Osório, G. J., & Barhoumi, W. (2020, February). Artificial intelligence for product quality inspection toward smart industries: quality control of vehicle non-conformities. In 2020 9th international conference on industrial technology and management (ICITM) (pp. 127-131). IEEE. doi:10.1109/ICITM48982.2020.9080396.
Dwivedi, A., & Dwivedi, A. (2013). Role of computer and automation in design and manufacturing for mechanical and textile industries: CAD/CAM. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 3(3), 8.
El Affaki, O., Benhadou, M., & Haddout, A. (2024). Roadmap to achieve operational excellence through Lean Management implementation and quality management system conformance. Acta Logistica (AL), 11(1). https://doi.org/10.22306/al.v11i1.452
Fonseca, L. M., & Domingues, J. P. (2017). Reliable and flexible Quality Management Systems in the automotive industry: monitor the context and change effectively. Procedia Manufacturing, 11, 1200-1206. doi: 10.1016/j.promfg.2017.07.245
He, Y., Ma, X., Tian, Y., He, Z., Zhang, X., Kim, K. J., & Bae, Y. M. (2024). Set Response Surface Methodology and its Application in Solving the Wrinkle and Crack Problem in the Auto Industry. IEEE Transactions on Reliability. doi: 10.1109/TR.2024.3421552
Helo, P., & Hao, Y. (2021). Artificial intelligence in operations management and supply chain management: an exploratory case study. Production Planning & Control, 33(16), 1573–1590. https://doi.org/10.1080/09537287.2021.1882690
Hofmann, M., Neukart, F., & Bäck, T. (2017). Artificial intelligence and data science in the automotive industry. arXiv preprint arXiv:1709.01989. https://doi.org/10.48550/arXiv.1709.01989
Hussain, M. (2023). When, where, and which?: Navigating the intersection of computer vision and generative ai for strategic business integration. IEEE Access, 11, 127202-127215. doi: 10.1109/ACCESS.2023.3332468
Jacob, A. (2008). Automating cutting of composites. Reinforced Plastics, 52(6), 20-25. https://doi.org/10.1016/S0034-3617(08)70211-X
Jamous, R., ALRahhal, H., & El-Darieby, M. (2021). A New ANN‐Particle Swarm Optimization with Center of Gravity (ANN‐PSOCoG) Prediction Model for the Stock Market under the Effect of COVID‐19. Scientific Programming, 2021(1), 6656150. https://doi.org/10.1155/2021/6656150
Khare, P., & Srivastava, S. (2022). The Impact of AI on Product Management: A Systematic Review and Future Trends. International Journal of Research Analysis Review, 9(4), 736-741.
Kitzmann, H., Strimovskaya, A., & Serova, E. (2023, December). Application of Artificial Intelligence Methods for Improvement of Strategic Decision-Making in Logistics.In International Working Conference on Transfer and Diffusion of IT (pp. 132-143). Cham: Springer Nature Switzerland.
Konstantinidis, F. K., Mouroutsos, S. G., & Gasteratos, A. (2021, August). The role of machine vision in industry 4.0: an automotive manufacturing perspective. In 2021 IEEE international conference on imaging systems and techniques (IST) (pp. 1-6). IEEE. https://doi.org/10.1109/IST50367.2021.9651453
Loch, C. H., Van der Heyden, L., Van Wassenhove, L. N., Huchzermeier, A., Escalle, C., Loch, C. H., ... & Escalle, C. (2003). Industrial Excellence Revisited. Industrial Excellence: Management Quality in Manufacturing, 3-21. https://doi.org/10.1007/978-3-540-24758-6_1
Lodhi, S. K., Gill, A. Y., & Hussain, I. (2024). AI-Powered Innovations in Contemporary Manufacturing Procedures: An Extensive Analysis. International Journal of Multidisciplinary Sciences and Arts, 3(4), 15-25. https://doi.org/10.47709/ijmdsa.v3i4.4616
Min, H. (2009). Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics Research and Applications, 13(1), 13–39. https://doi.org/10.1080/13675560902736537
Mitta, N. R. (2024). Leveraging AI for Smart Inventory Management in Retail: Developing Machine Learning Models for Predictive Replenishment, Stock Optimization, and Demand-Supply Balancing. Australian Journal of Machine Learning Research & Applications, 4(2), 113-146.
Moniz, A. B., Candeias, M., & Boavida, N. (2022). Changes in productivity and labour relations: artificial intelligence in the automotive sector in Portugal. International Journal of Automotive Technology and Management, 22(2), 222-244. https://doi.org/10.1504/IJATM.2022.124366
Montenegro, I. S. B. (2020). Zero-Defects in the Automotive Industry Supply Chain: Development of a Framework for an OEM Process Audit Tool.
Palle, R. R., & Kathala, K. C. R. (2024). AI and data security. In Privacy in the Age of Innovation: AI Solutions for Information Security (pp. 119-127). Berkeley, CA: Apress. https://doi.org/10.1007/979-8-8688-0461-8_10
Ramadhani, W., Khuzaini, K., & Shaddiq, S. (2024). Resistance to Change: Human Resources Issues in the Implementation of Industry 4.0 Technology. Proceeding: Islamic University of Kalimantan. DOI: http://dx.doi.org/10.31602/piuk.v0i0.15802
Riahi, Y., Saikouk, T., Gunasekaran, A., & Badraoui, I. (2021). Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications, 173, 114702. https://doi.org/10.1016/j.eswa.2021.114702
Riasanow, T., Galic, G., & Böhm, M. (2017). Digital transformation in the automotive industry: towards a generic value network.
Richey Jr, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532-549. https://doi.org/10.1111/jbl.12364
Rother, E., Baboli, A., & Bernardini, L. (2019, October). Industrial Excellence and Industry 4.0: Management Concepts Evolution-Case Study in Automotive Industry. In The 49th International Conference on Computers & Industrial Engineering (CIE49). DOI : 10.1109/IEA.2019.8714779
Ruswanto, T. P., & Saroso, D. S. (2018). Gap analysis study on the compliance of automotive standard IATF 16949 based on internal quality audit score in automotive industry. Journal of applied research on industrial engineering, 5(4), 271-285. https://doi.org/10.22105/jarie.2018.148773.1055
Schlüter, M., Lickert, H., Schweitzer, K., Bilge, P., Briese, C., Dietrich, F., & Krüger, J. (2021). AI-enhanced identification, inspection and sorting for reverse logistics in remanufacturing. Procedia CIRP, 98, 300-305. https://doi.org/10.1016/j.procir.2021.01.107
Sedgwick, P. (2013). Simple linear regression. BMJ, 346. doi: https://doi.org/10.1136/bmj.f2340
Singh, N. (2014). Automotive industry response to its global QMS standard ISO/TS-16949. Globalization and Standards: Issues and Challenges in Indian Business, 121-142. https://doi.org/10.1007/978-81-322-1994-1_6
Smith, E. M. (2023). The challenge of complexity: research quality in assistive technology. Assistive Technology, 35(4), 291-291. https://doi.org/10.1080/10400435.2023.2240212
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability engineering & system safety, 215, 107864. https://doi.org/10.1016/j.ress.2021.107864
Tubaro, P., & Casilli, A. A. (2019). Micro-work, artificial intelligence and the automotive industry. Journal of Industrial and Business Economics, 46, 333-345. https://doi.org/10.1007/s40812-019-00121-1
Vanoy, R. J. A. (2023). Logistics 4.0: Exploring Artificial Intelligence Trends in Efficient Supply Chain Management. Data and Metadata, 2, 145-145. https://doi.org/10.56294/dm2023145
Wan, J., Li, X., Dai, H. N., Kusiak, A., Martinez-Garcia, M., & Li, D. (2020). Artificial-intelligence-driven customized manufacturing factory: key technologies, applications, and challenges. Proceedings of the IEEE, 109(4), 377-398. doi: 10.1109/JPROC.2020.3034808
Yan, X., & Su, X. (2009). Linear regression analysis: theory and computing. world scientific.
Yousif, I., Burns, L., El Kalach, F., & Harik, R. (2024). Leveraging computer vision towards high-efficiency autonomous industrial facilities. Journal of Intelligent Manufacturing, 1-26. https://doi.org/10.1007/s10845-024-02396-1
Arena, F., Collotta, M., Luca, L., Ruggieri, M., & Termine, F. G. (2021). Predictive maintenance in the automotive sector: A literature review. Mathematical and Computational Applications, 27(1), 2. https://doi.org/10.3390/mca27010002
Assia, B., Mounia, Z., & Faical, Z. (2024). A survey study on Industry 4.0 for Moroccan manufacturing. ActaLogistica (AL), 11(3). https://doi.org/10.22306/al.v11i3.525
Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European journal of operational research, 184(3), 1140-1154. https://doi.org/10.1016/j.ejor.2006.12.004
Chinnaiyan, B., Balasubaramanian, S., Jeyabalu, M., & Warrier, G. S. (2025). AI Applications–Computer Vision and Natural Language Processing. Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications, 25-41. https://doi.org/10.1002/9781394219230.ch2
Chouchene, A., Carvalho, A., Lima, T. M., Charrua-Santos, F., Osório, G. J., & Barhoumi, W. (2020, February). Artificial intelligence for product quality inspection toward smart industries: quality control of vehicle non-conformities. In 2020 9th international conference on industrial technology and management (ICITM) (pp. 127-131). IEEE. doi:10.1109/ICITM48982.2020.9080396.
Dwivedi, A., & Dwivedi, A. (2013). Role of computer and automation in design and manufacturing for mechanical and textile industries: CAD/CAM. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 3(3), 8.
El Affaki, O., Benhadou, M., & Haddout, A. (2024). Roadmap to achieve operational excellence through Lean Management implementation and quality management system conformance. Acta Logistica (AL), 11(1). https://doi.org/10.22306/al.v11i1.452
Fonseca, L. M., & Domingues, J. P. (2017). Reliable and flexible Quality Management Systems in the automotive industry: monitor the context and change effectively. Procedia Manufacturing, 11, 1200-1206. doi: 10.1016/j.promfg.2017.07.245
He, Y., Ma, X., Tian, Y., He, Z., Zhang, X., Kim, K. J., & Bae, Y. M. (2024). Set Response Surface Methodology and its Application in Solving the Wrinkle and Crack Problem in the Auto Industry. IEEE Transactions on Reliability. doi: 10.1109/TR.2024.3421552
Helo, P., & Hao, Y. (2021). Artificial intelligence in operations management and supply chain management: an exploratory case study. Production Planning & Control, 33(16), 1573–1590. https://doi.org/10.1080/09537287.2021.1882690
Hofmann, M., Neukart, F., & Bäck, T. (2017). Artificial intelligence and data science in the automotive industry. arXiv preprint arXiv:1709.01989. https://doi.org/10.48550/arXiv.1709.01989
Hussain, M. (2023). When, where, and which?: Navigating the intersection of computer vision and generative ai for strategic business integration. IEEE Access, 11, 127202-127215. doi: 10.1109/ACCESS.2023.3332468
Jacob, A. (2008). Automating cutting of composites. Reinforced Plastics, 52(6), 20-25. https://doi.org/10.1016/S0034-3617(08)70211-X
Jamous, R., ALRahhal, H., & El-Darieby, M. (2021). A New ANN‐Particle Swarm Optimization with Center of Gravity (ANN‐PSOCoG) Prediction Model for the Stock Market under the Effect of COVID‐19. Scientific Programming, 2021(1), 6656150. https://doi.org/10.1155/2021/6656150
Khare, P., & Srivastava, S. (2022). The Impact of AI on Product Management: A Systematic Review and Future Trends. International Journal of Research Analysis Review, 9(4), 736-741.
Kitzmann, H., Strimovskaya, A., & Serova, E. (2023, December). Application of Artificial Intelligence Methods for Improvement of Strategic Decision-Making in Logistics.In International Working Conference on Transfer and Diffusion of IT (pp. 132-143). Cham: Springer Nature Switzerland.
Konstantinidis, F. K., Mouroutsos, S. G., & Gasteratos, A. (2021, August). The role of machine vision in industry 4.0: an automotive manufacturing perspective. In 2021 IEEE international conference on imaging systems and techniques (IST) (pp. 1-6). IEEE. https://doi.org/10.1109/IST50367.2021.9651453
Loch, C. H., Van der Heyden, L., Van Wassenhove, L. N., Huchzermeier, A., Escalle, C., Loch, C. H., ... & Escalle, C. (2003). Industrial Excellence Revisited. Industrial Excellence: Management Quality in Manufacturing, 3-21. https://doi.org/10.1007/978-3-540-24758-6_1
Lodhi, S. K., Gill, A. Y., & Hussain, I. (2024). AI-Powered Innovations in Contemporary Manufacturing Procedures: An Extensive Analysis. International Journal of Multidisciplinary Sciences and Arts, 3(4), 15-25. https://doi.org/10.47709/ijmdsa.v3i4.4616
Min, H. (2009). Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics Research and Applications, 13(1), 13–39. https://doi.org/10.1080/13675560902736537
Mitta, N. R. (2024). Leveraging AI for Smart Inventory Management in Retail: Developing Machine Learning Models for Predictive Replenishment, Stock Optimization, and Demand-Supply Balancing. Australian Journal of Machine Learning Research & Applications, 4(2), 113-146.
Moniz, A. B., Candeias, M., & Boavida, N. (2022). Changes in productivity and labour relations: artificial intelligence in the automotive sector in Portugal. International Journal of Automotive Technology and Management, 22(2), 222-244. https://doi.org/10.1504/IJATM.2022.124366
Montenegro, I. S. B. (2020). Zero-Defects in the Automotive Industry Supply Chain: Development of a Framework for an OEM Process Audit Tool.
Palle, R. R., & Kathala, K. C. R. (2024). AI and data security. In Privacy in the Age of Innovation: AI Solutions for Information Security (pp. 119-127). Berkeley, CA: Apress. https://doi.org/10.1007/979-8-8688-0461-8_10
Ramadhani, W., Khuzaini, K., & Shaddiq, S. (2024). Resistance to Change: Human Resources Issues in the Implementation of Industry 4.0 Technology. Proceeding: Islamic University of Kalimantan. DOI: http://dx.doi.org/10.31602/piuk.v0i0.15802
Riahi, Y., Saikouk, T., Gunasekaran, A., & Badraoui, I. (2021). Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications, 173, 114702. https://doi.org/10.1016/j.eswa.2021.114702
Riasanow, T., Galic, G., & Böhm, M. (2017). Digital transformation in the automotive industry: towards a generic value network.
Richey Jr, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532-549. https://doi.org/10.1111/jbl.12364
Rother, E., Baboli, A., & Bernardini, L. (2019, October). Industrial Excellence and Industry 4.0: Management Concepts Evolution-Case Study in Automotive Industry. In The 49th International Conference on Computers & Industrial Engineering (CIE49). DOI : 10.1109/IEA.2019.8714779
Ruswanto, T. P., & Saroso, D. S. (2018). Gap analysis study on the compliance of automotive standard IATF 16949 based on internal quality audit score in automotive industry. Journal of applied research on industrial engineering, 5(4), 271-285. https://doi.org/10.22105/jarie.2018.148773.1055
Schlüter, M., Lickert, H., Schweitzer, K., Bilge, P., Briese, C., Dietrich, F., & Krüger, J. (2021). AI-enhanced identification, inspection and sorting for reverse logistics in remanufacturing. Procedia CIRP, 98, 300-305. https://doi.org/10.1016/j.procir.2021.01.107
Sedgwick, P. (2013). Simple linear regression. BMJ, 346. doi: https://doi.org/10.1136/bmj.f2340
Singh, N. (2014). Automotive industry response to its global QMS standard ISO/TS-16949. Globalization and Standards: Issues and Challenges in Indian Business, 121-142. https://doi.org/10.1007/978-81-322-1994-1_6
Smith, E. M. (2023). The challenge of complexity: research quality in assistive technology. Assistive Technology, 35(4), 291-291. https://doi.org/10.1080/10400435.2023.2240212
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability engineering & system safety, 215, 107864. https://doi.org/10.1016/j.ress.2021.107864
Tubaro, P., & Casilli, A. A. (2019). Micro-work, artificial intelligence and the automotive industry. Journal of Industrial and Business Economics, 46, 333-345. https://doi.org/10.1007/s40812-019-00121-1
Vanoy, R. J. A. (2023). Logistics 4.0: Exploring Artificial Intelligence Trends in Efficient Supply Chain Management. Data and Metadata, 2, 145-145. https://doi.org/10.56294/dm2023145
Wan, J., Li, X., Dai, H. N., Kusiak, A., Martinez-Garcia, M., & Li, D. (2020). Artificial-intelligence-driven customized manufacturing factory: key technologies, applications, and challenges. Proceedings of the IEEE, 109(4), 377-398. doi: 10.1109/JPROC.2020.3034808
Yan, X., & Su, X. (2009). Linear regression analysis: theory and computing. world scientific.
Yousif, I., Burns, L., El Kalach, F., & Harik, R. (2024). Leveraging computer vision towards high-efficiency autonomous industrial facilities. Journal of Intelligent Manufacturing, 1-26. https://doi.org/10.1007/s10845-024-02396-1