Abstract

In the highly competitive manufacturing environment of the 21st century, maintaining consistent product quality is critical for operational efficiency, customer satisfaction, and sustainability. Statistical Quality Control (SQC) techniques offer a scientific, data-driven approach to monitor, control, and improve manufacturing processes. This study explores the practical application of SQC tools in a real-world manufacturing setting through a case study of a mid-sized industrial unit specializing in precision components. The research employs key SQC methods, including X̄-R control charts, P-charts, Pareto analysis, cause-and-effect diagrams, and process capability indices (Cp and Cpk), to assess process stability and detect assignable causes of variation. Data was collected over a period of three months from the production line, focusing on a critical process where quality issues were frequently reported. Analysis revealed significant variation in output quality, with multiple points falling outside control limits. Through root cause identification and implementation of corrective measures, the process was stabilized and overall defect rates were significantly reduced. The study highlights the effectiveness of SQC in pinpointing inefficiencies and improving quality assurance protocols. The findings demonstrate that regular implementation of SQC techniques not only enhances process control but also contributes to long-term productivity and cost savings. This research supports the adoption of statistical tools in manufacturing quality management and offers a replicable framework for similar industries. The paper concludes by recommending future integration of automated statistical monitoring systems and AI-based quality forecasting for greater process reliability.