Quality Control Improvement Using Statistical Process Control in Manufacturing Industry - Soeryono, Joan Michael; Putri, Rina Kartikasari; Doğan, Onur (RESWARA: Jurnal Riset Ilmu Teknik, 2025)
Quality improvement remains a critical challenge in manufacturing industries due to increasing product complexity, strict customer requirements, and competitive market conditions. Statistical Process Control (SPC) has been widely recognized as an effective approach for monitoring process stability, reducing variability, and improving product quality. This study aims to analyze the application of SPC as a systematic method to enhance quality control performance in manufacturing operations. A quantitative case-study-based research design was adopted, focusing on production processes where quality deviations frequently occurred. Data were collected through direct observation, historical production records, and defect inspection reports. SPC tools, including control charts, process capability analysis, and Pareto analysis, were employed to identify process variations and root causes of defects. The results demonstrate that SPC implementation significantly improves process stability, reduces defect rates, and enhances process capability indices. Comparative analysis with previous studies confirms that SPC contributes to continuous improvement when supported by structured data analysis and corrective actions. This study concludes that SPC is a practical and reliable quality control approach that supports decision-making and operational excellence in manufacturing industries. The findings provide practical insights for quality engineers and managers seeking to strengthen quality assurance systems.