Leveraging Machine Learning Tools to Identify and Manage Suppliers Corresponded to Defective Parts During Warranty
Soroush Elyasi
1
(
دانشگاه تهران
)
فتانه تقییار
2
(
)
الکلمات المفتاحية: Supply Chain Management (SCM), Automotive Industry, AI-enabled Solutions, Industry 4.0, Digital Transformation,
ملخص المقالة :
Managing defective parts during the warranty period presents significant challenges in the automotive industry, including accurately identifying responsible suppliers, reducing human errors, and handling large volumes of data. Moreover, ensuring product quality and providing excellent after-sales services are crucial for customer satisfaction and increasing customer retention rates. This paper addresses these challenges by introducing a machine learning (ML) approach to automate supplier verification and identify misclassified faulty parts. Specifically, we employ Gradient Boosting Machines (GBM) due to their superior performance in handling complex datasets and imbalanced classifications. We trained and evaluated our model using 401,168 data collected from multiple analysis centers nationwide. The model achieved a recall of 92.3%, a precision of 90.7%, an F1-score of 91.5%, and an AUC of 0.96, ensuring accurate identification of faulty parts' suppliers across our diverse environments. By integrating AI-driven methodologies, our system not only reduces manual workload and human error but also enhances supplier accountability and operational efficiency, which shows the promising perspective of using AI in large industries like the automotive industry. While traditional manual processes are time-consuming, error-prone, and inefficient, leading to delays and increased operational costs, this ML framework is a useful tool for addressing those issues and can enhance warranty management, product quality, and customer satisfaction in the automotive industry, which can lead to securing market share and competitive advantage.
Aghaei Fishani, B., Mahmoodirad, A., Niroomand, S., & Fallah, M. (2022). Developing a Fuzzy Green Supply Chain Management Problem Considering Location Allocation Routing Problem: Hybrid Meta-Heuristic Approach. Journal of Optimization in Industrial Engineering, 15(1). https://doi.org/10.22094/joie.2021.1940563.1893
Babakmehr, M., Baumanns, S., Chehade, A., Hochkirchen, T., Kalantari, M., Krivtsov, V., & Schindler, D. (2024). Data‐driven framework for warranty claims forecasting with an application for automotive components. Engineering Reports, 6(5), e12764. https://doi.org/10.1002/eng2.12764
Baroud, S. Y., Yahaya, N. A., & Elzamly, A. M. (2023). Spare parts management analytics for smart maintenance: A review on dealing with challenges to warranty profitability. 040007. https://doi.org/10.1063/5.0133121
Becker, M., & Schölzel, S. (2025). Warranty Provisions: Machine-Learning Versus Human Estimates. European Accounting Review, 1–30. https://doi.org/10.1080/09638180.2024.2444521
Buyvol, P., Makarova, I., Voroshilov, A., & Krivonogova, A. (2023). The Process of Identifying Automobile Joint Failures during the Operation Phase: Data Analytics Based on Association Rules. Information, 14(5), 257. https://doi.org/10.3390/info14050257
Canciglierie, A. B., Da Rocha, T., Szejka, A. L., Dos Santos Coelho, L., & Junior, O. C. (2021). Real-Time Machine Learning Automation Applied to Failure Prediction in Automakers Supplier Manufacturing System. In A. Dolgui, A. Bernard, D. Lemoine, G. Von Cieminski, & D. Romero (Eds.), Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems (Vol. 630, pp. 303–310). Springer International Publishing. https://doi.org/10.1007/978-3-030-85874-2_32
Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2024). Artificial intelligence in supply chain and operations management: A multiple case study research. International Journal of Production Research, 62(9), 3333–3360. https://doi.org/10.1080/00207543.2023.2232050
Cao, Z. (2022). Brand equity, warranty costs, and firm value. International Journal of Research in Marketing, 39(4), 1166–1185. https://doi.org/10.1016/j.ijresmar.2022.02.002
Çelebi, D., & Bayraktar, D. (2008). An integrated neural network and data envelopment analysis for supplier evaluation under incomplete information. Expert Systems with Applications, 35(4), 1698–1710. https://doi.org/10.1016/j.eswa.2007.08.107
Erdoğmuş, İ. E., & Çiçek, M. (2012). The Impact of Social Media Marketing on Brand Loyalty. Procedia - Social and Behavioral Sciences, 58, 1353–1360. https://doi.org/10.1016/j.sbspro.2012.09.1119
Ghafori, K. (2023). A Soft Approach to Supplier Selection Problem for a Steel Company under Future Uncertainty. Journal of Optimization in Industrial Engineering, Online First. https://doi.org/10.22094/joie.2022.1970921.1996
Hemmer, P., Kühl, N., & Schöffer, J. (2021). Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2110.09023
Hongfu Huang, Feng Liu, & Peng Zhang. (2021). To outsource or not to outsource? Warranty service provision strategies considering competition, costs and reliability. International Journal of Production Economics, 242. https://doi.org/10.1016/j.ijpe.2021.108298
Ibrahim, B., & Aljarah, A. (2023). The era of Instagram expansion: Matching social media marketing activities and brand loyalty through customer relationship quality. Journal of Marketing Communications, 29(1), 1–25. https://doi.org/10.1080/13527266.2021.1984279
Iskandar, Y. (2024). Supplier Selection Using Fuzzy Analytical Hierarchy Process: A Bibliometric Analysis. Journal of Optimization in Industrial Engineering, 17(2). https://doi.org/10.22094/qjie.2024.1123244
Jamaluddin, F., & Saibani, N. (2021). Systematic Literature Review of Supply Chain Relationship Approaches amongst Business-to-Business Partners. Sustainability, 13(21), 11935. https://doi.org/10.3390/su132111935
Karim, M. R. (2023). Divide and recombine approach for warranty database: Estimating the reliability of an automobile component. Data Science and Management, S2666764923000590. https://doi.org/10.1016/j.dsm.2023.12.002
Kato, T. (2021). Factors of loyalty across corporate brand images, products, dealers, sales staff, and after-sales services in the automotive industry. Procedia Computer Science, 192, 1411–1421. https://doi.org/10.1016/j.procs.2021.08.144
Lee, J.-G., Kim, T., Sung, K. W., & Han, S. W. (2021). Automobile parts reliability prediction based on claim data: The comparison of predictive effects with deep learning. Engineering Failure Analysis, 129, 105657. https://doi.org/10.1016/j.engfailanal.2021.105657
Li, L.-H., & Lai, C.-Y. (2021). Comparing the Impact of Service Quality on Customers’ Repurchase Intentions Based on Statistical Methods and Artificial Intelligence-Taking an Automotive Aftermarket (AM) Parts Sales Company as an Example. 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM), 1–8. https://doi.org/10.1109/IMCOM51814.2021.9377415
MANISH, C. (2023). A Study on Determining and Analysing Consumer Buying Behaviour in the Automobile Industry. International Journal For Multidisciplinary Research, 5(2), 2359. https://doi.org/10.36948/ijfmr.2023.v05i02.2359
Mittal, U., Panchal, D., & Dapertment of Mechanical Engineering, National Institute of Technology Kurukshetra, India. (2023). AI-based evaluation system for supply chain vulnerabilities and resilience amidst external shocks: An empirical approach. Reports in Mechanical Engineering, 4(1), 276–289. https://doi.org/10.31181/rme040122112023m
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
Msakni, M. K., Risan, A., & Schütz, P. (2023). Using machine learning prediction models for quality control: A case study from the automotive industry. Computational Management Science, 20(1), 14. https://doi.org/10.1007/s10287-023-00448-0
PUSPANINGRUM, A. (2020). Social Media Marketing and Brand Loyalty: The Role of Brand Trust. The Journal of Asian Finance, Economics and Business, 7(12), 951–958. https://doi.org/10.13106/JAFEB.2020.VOL7.NO12.951
R., K. (2021). Understanding Customer Engagement and Purchase Behavior in Automobiles: The Role of Digital Technology. In N. Mohd Suki (Ed.), Advances in Marketing, Customer Relationship Management, and E-Services (pp. 1–13). IGI Global. https://doi.org/10.4018/978-1-7998-4772-4.ch001
Risan, A., Msakni, M. K., & Schütz, P. (2021). A Neural Network Model for Quality Prediction in the Automotive Industry. In A. Dolgui, A. Bernard, D. Lemoine, G. Von Cieminski, & D. Romero (Eds.), Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems (Vol. 634, pp. 567–575). Springer International Publishing. https://doi.org/10.1007/978-3-030-85914-5_60
Sabbagh, O., Ab Rahman, M. N., Ismail, W. R., & Wan Hussain, W. M. H. (2017). The moderation influence of warranty on customer satisfaction’s antecedents: An empirical evidence from automotive dealerships. The Service Industries Journal, 37(5–6), 381–407. https://doi.org/10.1080/02642069.2017.1326483
Schwab, M., Madeline-Derou, C., Klarmann, S., Thielen, N., Meier, S., Franke, J., Chintanippu, S., & Stork, W. (2022). Multi-Model Machine Learning based Industrial Vision Framework for Assembly Part Quality Control. 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), 1–4. https://doi.org/10.1109/ETFA52439.2022.9921587
Shokouhyar, S., Shokoohyar, S., & Safari, S. (2020). Research on the influence of after-sales service quality factors on customer satisfaction. Journal of Retailing and Consumer Services, 56, 102139. https://doi.org/10.1016/j.jretconser.2020.102139
Soroush Elyasi & Fattaneh Taghiyareh. (2024). An AI-enabled Approach to Predict Critical Components and Enhance Supply Chain Management. 11th International Symposium on Telecommunication, 11. https://doi.org/In Press
Soroush Elyasi & Fattaneh Taghiyareh. (2025, February 6). TIS: Three-tiered Intelligent Supply Chain Management to Enhance Quality and Supervision. 2025 29th International Computer Conference Computer Society of Iran (CSICC). 2025 29th International Computer Conference Computer Society of Iran (CSICC), Tehran, Iran, Islamic Republic of. https://doi.org/in press
Suhaib Kamran, S., Haleem, A., Bahl, S., Javaid, M., Prakash, C., & Budhhi, D. (2022). Artificial intelligence and advanced materials in automotive industry: Potential applications and perspectives. Materials Today: Proceedings, 62, 4207–4214. https://doi.org/10.1016/j.matpr.2022.04.727
Universitas Muhammadiyah Jember, & Qomariah, N. (2021). The Role of Promotion and Service Quality in Increasing Consumer Satisfaction and Loyalty in Pawnshops. Journal of Economics, Finance And Management Studies, 04(10). https://doi.org/10.47191/jefms/v4-i10-17
Wang, Q., Shui, H., Tran, T. T. T., Nezhad, M. Z., Upadhyay, D., Paynabar, K., & He, A. (2023). A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis (arXiv:2304.04755). arXiv. http://arxiv.org/abs/2304.04755
Yang, X., Zheng, Q., Hu, Y., Chen, R., Wang, X., & Liu, Y. (2022). Research on High-Quality Development of Auto Parts Manufacturing Industry Based on Machine Learning Model. Scientific Programming, 2022, 1–9. https://doi.org/10.1155/2022/3659742
Yi, S., Yu, L., & Zhang, Z. (2020). Research on Pricing Strategy of Dual-Channel Supply Chain Based on Customer Value and Value-Added Service. Mathematics, 9(1), 11. https://doi.org/10.3390/math9010011
Zhao, J., Butt, R. S., Murad, M., Mirza, F., & Saleh Al-Faryan, M. A. (2022). Untying the Influence of Advertisements on Consumers Buying Behavior and Brand Loyalty Through Brand Awareness: The Moderating Role of Perceived Quality. Frontiers in Psychology, 12, 803348. https://doi.org/10.3389/fpsyg.2021.803348