Predict failures in production lines: A two-stage approach with clustering and supervised learning

Abstract

The implementation of advanced technologies in manufacturing has created large amounts of data. The data can be utilized to create predictive models for quality control, which allows manufacturers to produce higher quality products at a lower cost. Bosch has provided a large-scale data set of a production line and hosted a challenge on Kaggle aiming to predict the manufacturing failures using the anonymized features. We proposed a two-stage method first to cluster the data into groups based on the manufacturing process and then use supervised learning to predict the failed product in each cluster. This approach reduces the sparsity of the data set. Various algorithms were compared. The random forest algorithm achieved the highest performance score and was chosen as the final model.