Application of the Pareto Principle in Quality Management through an Integrated Model of Statistical Process Control and Design of Experiments
DOI:
https://doi.org/10.34021/ve.2025.08.03(3)Keywords:
quality management, Pareto principle, statistical process control, design of experiments, parameter optimisation, machine learning, optimisingAbstract
Quality management in production processes faces ongoing challenges related to variability and process stability, particularly in the context of complex manufacturing environments. Despite extensive use of Statistical Process Control (SPC) and Design of Experiments (DoE), there remains a gap in integrating these approaches with the Pareto Principle for effective decision-making and continuous improvement. This study aims to address this gap by evaluating the combined application of the Pareto Principle with SPC and DoE in managing production quality. The case study was conducted in a single-component paint manufacturing company, focusing on dynamic viscosity as a key quality parameter. The research methodology included process data compliance evaluation, identification of variability sources, measurement system analysis, and the development of a fractional experimental design. The results revealed that operator performance was the primary source of variability. Among the studied factors, maintenance time and temperature during thickener activation had the most significant impact on process stability. Optimising these parameters led to a substantial reduction in natural variability, keeping viscosity within specification limits. The findings suggest that the integrated SPC-DoE-Pareto model provides an effective framework for addressing key sources of deviation and improving quality management decisions. This approach not only enhances process stability but also aligns with the principles of continuous improvement. Further research could explore the broader applicability of the SPC-DoE-Pareto integration in different industries and its impact on long-term process performance. Additionally, investigating the role of advanced automation and machine learning in supporting these models could offer new avenues for improvement in quality management.
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