Gorkem SariyerSachin Kumar ManglaYigit KazancogluCeren Ocal TasarSunil Luthra2025-10-0620210254-53301572-933810.1007/s10479-021-04215-9http://dx.doi.org/10.1007/s10479-021-04215-9https://gcris.yasar.edu.tr/handle/123456789/7221Advances in smart technologies (Industry 4.0) assist managers of Micro Small and Medium Enterprises (MSME) to control quality in manufacturing using sophisticated data-driven techniques. This study presents a 3-stage model that classifies products depending on defects (defects or non-defects) and defect type according to their levels. This article seeks to detect potential errors to ensure superior quality through machine learning and data mining. The proposed model is tested in a medium enterprise-a kitchenware company in Turkey. Using the main features of data set product customer country production line production volume sample quantity and defect code a Multilayer Perceptron algorithm for product quality level classification was developed with 96% accuracy. Once a defect is detected an estimation is made of how many re-works are required. Thus considering the attributes of product production line production volume sample quantity and product quality level a Multilayer Perceptron algorithm for re-work quantity prediction model was developed with 98% performance. From the findings re-work quantity has the highest relation with product quality level where re-work quantities were higher for major defects compared to minor/moderate defects. Finally this work explores the root causes of defects considering production line and product quality level through association rule mining. The top mined rule achieves a confidence level of 80% where assembly and material were identified as main root causes.EnglishMSME, Machine learning, Quality control, Industry 4, 0, Data analytics, Manufacturing, Association rule mining, Re-work and root causes of defectBIG DATA ANALYTICS, PERFORMANCEArticle