Analysis of machine repair time prediction using machine learning at one of leading footwear manufacturers in Indonesia

Abstract

<p>Machine breakdowns in the production line mostly finish in more than 18<br />minutes, since the machine that needs repair more time is done on the<br />production line, not in the machine warehouse. Historical machine<br />breakdown data is digitally recorded through the Andon system, but it is still<br />not being used adequately to aid decision-making. This research introduces<br />an analysis of historical machine breakdown data to provide predictions of<br />repair time intervals with a focus on finding the best algorithm accuracy.<br />The research method uses machine learning techniques with a classification<br />model. There are five algorithms used: logistic regression (LR), naive bayes<br />(NB), k-nearest neighbor (KNN), support vector machine (SVM), and<br />random forest (RF). The results of this study prove that historical machine<br />breakdown data can be optimized to predict machine repair time intervals in<br />the production line. The accuracy of LR algorithm is slightly better than the<br />other algorithms. Based on the receiver operating characteristic–area under<br />curve (ROC-AUC) performance evaluation metric, the quality value of the<br />accuracy of LR model is satisfied with a percentage of 69% with a<br />difference of 0.5% between the train and test data.</p>

Use of lean manufacturing tools to support sustainability initiatives

“…In turn, it facilitates permanent supervision and feedback tasks within an organization (Hernández et al 2018). • Andon: It is a visual and auditory communication tool that allows operators to interact with different manufacturing areas to solve problems that arise throughout the workday, preventing workers from having to travel in search of assistance (Purmala and Sudarto 2023). According to Martínez-Hernández et al (2020), this tool increases quality and productivity levels at the lowest cost and prevents failures from passing to the next level of production.…”