Improving plastic manufacturing processes with the integration of Six Sigma and machine learning techniques: a case study

Abstract

ABSTRACT This research integrates machine learning (ML) and Six Sigma’s Define, Measure, Analyze, Improve, and Control (DMAIC) methodology to address these issues. The study details the selection and utilization of ML techniques, including Linear Regression (LR), Artificial Neural Network (ANN), Decision Tree (DT), K-nearest neighbors (KNN), and Cluster Analysis (CA). Implemented at the Innovative Plastic Manufacturing Company in Egypt, this research enhances the consistency of plastic bottle production by addressing issues such as surface marks, flashes, bubbles, and variations in liter capacity. Integrating Six Sigma with ML techniques reduces the average defect rate from approximately 67.8%. It elevates the Sigma level from 3.14 to 4.30, reducing material over-consumption costs from 5% to 1.7% of total manufacturing expenses. Notably, the KNN model achieves the best results for defect testing, with an R-squared value of 98.8%. These methodologies lead to cost reduction, increased competitiveness, and improved product quality when implemented.