Abstract
The repair process of devices is an important part of the business of many original equipment manufacturers. The consumption of spare parts, during the repair process, is driven by the defects found during inspection of the devices, and these parts are a big part of the costs in the repair process. But current Supply Chain Control Tower solutions do not provide support for the automatic check of spare parts consumption in the repair process.
In this paper, we investigate a multi-label classification problem and present a learning-to-rank approach, where we simulate the passage of time while training hundreds of Logistic Regression Machine Learning models to provide an automatic check in the consumption of spare parts.
The results show that the trained models can achieve a mean NDCG@20 score of 81% when ranking the expected parts, while also marking a low volume of 10% of the consumed parts for alert generation. We briefly discuss how these marked parts can be aggregated and combined with additional data to generate more fine-grained alerts.
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Acknowledgments
This work is funded by Ingenico do Brasil LTDA using incentive resources by the law 13.969/2019, and in a technical cooperation agreement with Venturus under CATI resolution 135/2020.
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Duarte, E., de Haro Moraes, D., Padula, L.L. (2022). A Learning-to-Rank Approach for Spare Parts Consumption in the Repair Process. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_50
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