A Decision Tree-Based Approach for Classifying and Characterizing the Failure Type of White Goods

Year 2025, Volume: 12 Issue: 3, 858 - 872, 30.09.2025

Çağlar Solmaz , Serhat Peker , Onur Doğan

https://doi.org/10.54287/gujsa.1655744

Abstract

After-sales service plays a vital role in the white goods industry, significantly affecting both customer satisfaction and operational performance. This paper presents a decision tree-based approach for classifying and characterizing failure types in white goods, using after-sales service data from a white goods manufacturer. We employ the Classification and Regression Tree (CART) algorithm to identify patterns in failure occurrences based on product category, region, usage duration, and brand. The model generates interpretable decision rules, providing insights into the factors contributing to failures. The results reveal that product category and region are the most significant factors influencing product failures. These findings support manufacturers and service providers in optimizing maintenance strategies and improving service operations. The proposed approach enhances decision-making processes in after-sales service, leading to higher customer satisfaction and extended product life cycles.

Keywords

Data Mining , Classification , Decision Tree Analysis , Rule-Based Approach , Predictive Maintenance , Business Analytics

References

• Abbasi, A., Sarker, S., & Chiang, R. H. L. (2016). Big Data Research in Information Systems: Toward an Inclusive Research Agenda. Journal of the Association for Information Systems, 17(2), 3. https://doi.org/10.17705/1jais.00423
• Al-Refaie, A., Abu Hamdieh, B., & Lepkova, N. (2023). Prediction of maintenance activities using generalized sequential pattern and association rules in data mining. Buildings, 13(4), 946. https://doi.org/10.3390/buildings13040946
• Barros, R. C., Basgalupp, M. P., De Carvalho, A. C. P. L. F., & Freitas, A. A. (2012). A survey of evolutionary algorithms for decision-tree induction. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 42(3), 291–312. https://doi.org/10.1109/TSMCC.2011.2157494
• Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. CRC Press.
• Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
• Casagrande, V., Fenu, G., Pellegrino, F. A., Pin, G., Salvato, E., & Zorzenon, D. (2021). Machine learning for computationally efficient electrical loads estimation in consumer washing machines. Neural Computing and Applications, 33(22), 15159-15170. https://doi.org/10.1007/s00521-021-06138-9
• Chapman P., Clinton J., Kerber R., Khabaza T., Reinartz T., & Shearer C., & W. (1999). Guía paso a paso de Minería de Datos CRISP-DM 1.0.
• Cinar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211
• Cohen, M. A., Agrawal, N., & Agrawal, V. (2006). Winning in the aftermarket. Harvard business review, 84(5), 129.
• Damanik, I. S., Windarto, A. P., Wanto, A., Poningsih, Andani, S. R., & Saputra, W. (2019). Decision Tree Optimization in C4.5 Algorithm Using Genetic Algorithm. Journal of Physics: Conference Series, 1255(1), 012012. https://doi.org/10.1088/1742-6596/1255/1/012012
• Es-sakali, N., Cherkaoui, M., Mghazli, M. O., & Naimi, Z. (2022). Review of predictive maintenance algorithms applied to HVAC systems. Energy Reports, 8, 1003-1012. https://doi.org/10.1016/j.egyr.2022.07.130
• Falatouri, T., Brandtner, P., Nasseri, M., & Darbanian, F. (2023). Maintenance forecasting model for geographically distributed home appliances using spatial-temporal networks. Procedia Computer Science, 219, 495-503. https://doi.org/10.1016/j.procs.2023.01.317
• Fayyad, U. (2001). Knowledge Discovery in Databases: An Overview. Relational Data Mining, 28–47. https://doi.org/10.1007/978-3-662-04599-2_2
• Fernandes, S., Antunes, M., Santiago, A. R., Barraca, J. P., Gomes, D., & Aguiar, R. L. (2020). Forecasting appliances failures: A machine-learning approach to predictive maintenance. Information, 11(4), 208. https://doi.org/10.3390/info11040208
• Ferreira, L. L., Oliveira, A., Teixeira, N., Bulut, B., Landeck, J., Morgado, N., & Sousa, O. (2021, October). Predictive maintenance of home appliances: Focus on washing machines. In: IECON 2021–47th Annual Conference of the IEEE Industrial Electronics Society (pp. 1-6). IEEE.
• Fonseca, T., Chaves, P., Ferreira, L. L., Gouveia, N., Costa, D., Oliveira, A., & Landeck, J. (2023). Dataset for identifying maintenance needs of home appliances using artificial intelligence. Data in Brief, 48, 109068. https://doi.org/10.1016/j.dib.2023.109068
• Gartner. (1999). Gartner AMR Research Report.
• Gavankar, S. S., & Sawarkar, S. D. (2017, April 07-09). Eager decision tree. In: 2nd International Conference for Convergence in Technology, I2CT, (pp. 837–840). https://doi.org/10.1109/I2CT.2017.8226246
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
• Kantardzic, M. (2003). Data Mining: Concepts, Models, Methods, and Algorithms. (2003). Technometrics, 45(3), 277. https://doi.org/10.1198/tech.2003.s785
• Ko, T., Hyuk Lee, J., Cho, H., Cho, S., Lee, W., & Lee, M. (2017). Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data. Industrial Management and Data Systems, 117(5), 927–945. https://doi.org/10.1108/IMDS-06-2016-0195/FULL/XML
• Liu, Y., Hu, S., Zhang, H., Dong, Q., & Liu, W. (2024). Intelligent mining methodology of product field failure data by fusing deep learning and association rules for after-sales service text. Engineering Applications of Artificial Intelligence, 133, 108303. https://doi.org/10.1016/J.ENGAPPAI.2024.108303
• Mahesh, B. (2018). Machine Learning Algorithms-A Review. International Journal of Science and Research. https://doi.org/10.21275/ART20203995
• Molnar, C. (2020). Interpretable machine learning: A guide for making black box models explainable (2nd ed.). Lean Publishing.
• Mrva, J., Neupauer, S., Hudec, L., Sevcech, J., & Kapec, P. (2019, November 21-23). Decision support in medical data using 3D decision tree visualisation. In: 7th E-Health and Bioengineering Conference (EHB). https://doi.org/10.1109/EHB47216.2019.8969926
• Papaioannou, A., Dimara, A., Papaioannou, C., Papaioannou, I., Krinidis, S., Anagnostopoulos, C.-N., Korkas, C., Kosmatopoulos, E., Ioannidis, D., & Tzovaras, D. (2024). Simulation of Malfunctions in Home Appliances' Power Consumption. Energies (19961073), 17(17). https://doi.org/10.3390/en17174529
• Rohaan, D., Topan, E., & Groothuis-Oudshoorn, C. G. M. (2022). Using supervised machine learning for B2B sales forecasting: A case study of spare parts sales forecasting at an after-sales service provider. Expert Systems with Applications, 188, 115925. https://doi.org/10.1016/J.ESWA.2021.115925
• Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
• Nikam S. S. (2015). A Comparative Study of Classification Techniques in Data Mining Algorithms | Oriental Journal of Computer Science and Technology. http://www.computerscijournal.org/?p=1592
• Stein, G., Chen, B., Wu, A. S., & Hua, K. A. (2005). Decision tree classifier for network intrusion detection with GA-based feature selection. In: Proceedings of the 43rd annual ACM Southeast Conference (vol.2) (pp. 2136–2141). https://doi.org/10.1145/1167253.1167288
• Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, (3rd ed.). Elsevier. https://doi.org/10.1016/C2009-0-19715-5