Machine learning-based decision-making approach for predicting defects detection: a case study

In today’s highly competitive global market, industries must produce faultless products to achieve profitability. Machine learning (ML) algorithms provide a possible method to improve quality standards by enabling the prediction of the outcome of quality control processes. This article presents a real case study based on ML algorithms suggested to develop a knowledge-based intelligent supervisory system to predict defect products in the fashion industry. Defect detection is formulated as a binary classification problem, and several ML algorithms have been compared to determine the most suitable one on the available data. The random forest (RF), LightGBM, and C5.0 algorithms exhibit comparable high-end performances on the pre-processed dataset made available by the company. Nevertheless, since the aim of the analysed industry is to reduce the rate of false negative observations (i.e., the proportion of defected-free products wrongly classified), the best method results is RF, as it minimizes this metric.