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\begin{document}

\title{An empirical study of learning-to-rank for spare parts consumption in
the repair process}



\author[1]{Edson Duarte}%
\author[1]{Daniel de Haro Moraes}%
\author[1]{Lucas Leonardo Padula}%
\affil[1]{Venturus Innovation Center}%


\vspace{-1em}



  
  \date{June 11, 2024}


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\begin{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. In previous work we proposed a data-driven method
for Supply Chain Control Tower solutions to provide support for the
automatic check of spare parts consumption in the repair process. In
this paper, we continue our investigation of a multi-label
classification problem and explore alternatives in the learning-to-rank
approach, where we simulate the passage of time using more data while
training and comparing hundreds of Machine Learning models to provide an
automatic check in the consumption of spare parts. We investigate the
effects of different train set sizes, retraining intervals, models and
hyper-parameter search using Bayesian Optimization. The results show
that we were able to improve the trained models and achieve a higher
mean NDCG@20 score of 86\% when ranking the expected parts. Focusing on
the most recent data, we achieve a NDCG@20 score of 90\%, while
obtaining a ratio of marked parts of just 4\% of the consumed parts for
use in alert generation.%
\end{abstract}%



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