Abstract
Recent data science advances in statistical classification techniques, and in particular, machine learning techniques, have resulted in more efficient and robust ways of continuously monitoring and managing processes to achieve continuous quality improvement. With increased automation and process data outputs in industrial processes, traditional univariate Statistical Process Control (SPC) is proving impractical in some aspects and slowly loosing overall relevance. The main aim of this study is to develop a predictive process control framework for online quality control. This framework was validated for efficacy when compared to univariate SPC in a selected metal rolling plant based in South Africa. This predictive process control framework employs data science approaches through machine learning techniques and algorithms from the Python programming language. The research methodology is a single case study. An experiment approach was undertaken at hot roughing and hot finishing processes. The results of the study revealed a marginal 17 percent improvement in the predictive process control defect rate compared to the univariate SPC defect rate. The predictive model was based on the Random Forest algorithm and achieved an AUC of 0.84 compared to a 0.81 AUC for the neural network model. Factors found to have a positive impact on the success and sustainability of predictive process control were compliance with predictive model prescriptions, data science knowledge, senior management commitment, and the Extract, Transform, and Load (ETL) approach. These results contribute to the theory of online quality control and can be used as a guide by rolling mill process engineers and quality practitioners.