Integrating Classical and Advanced SPC Tools for Preventive Quality Management

Abstract Preventive (proactive) quality management requires monitoring process behavior over time and early detection of changes that may lead to future quality issues. Utilizing both classic and advanced Statistical Process Control (SPC) tools provides a more comprehensive picture of process behavior, identifying both large and small, incremental changes, and enabling proactive remedial action, which is a key component of quality prevention. The aim of this study was to demonstrate how combining different SPC tools can support preventive quality management by assessing current process stability and capability, identifying early warning signals, and predicting future process behavior. The study was conducted using data from a PCB copper plating process regarding copper layer thickness. The dataset of thickness measurements was analyzed using descriptive statistics, outlier detection, and normality tests. Classical control charts, including Individual and Moving Range charts, XmR Target USL/LSL, and XmR Trend charts, were applied together with advanced, special control charts such as MA, EWMA, and CUSUM. In addition, a multivariate Hotelling T² control chart was used to assess the combined effect of key process parameters, and process capability indices (Cp, Cpk) were calculated to evaluate compliance with specification requirements. The results confirmed that the analyzed process was statistically stable, predictable, and quality capable. At the same time, the advanced charts revealed temporary drifts and a slight downward tendency that were less visible in classical control charts. The study showed that integrating classical and advanced SPC tools improves the detection of both evident and subtle process changes, strengthens predictive insight into future process behavior, and provides effective support for preventive quality management.