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

Abstract

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