Statistical Quality Control in General Manufacturing: Principles, Methods and Contemporary Applications

Statistical Quality Control (SQC) represents a cornerstone methodology in modern manufacturing, providing systematic, data-driven frameworks for monitoring, analyzing and improving product quality and process performance. This paper presents a comprehensive examination of SQC as applied to general manufacturing environments, covering foundational statistical principles, the full taxonomy of SQC tools—including descriptive statistics, Statistical Process Control (SPC), acceptance sampling and design of experiments—and their integration into contemporary manufacturing paradigms such as Lean, Six Sigma and Industry 4.0. Drawing on established theoretical frameworks and documented industrial applications, the paper analyzes the effectiveness, limitations and evolving challenges of SQC implementation. Particular attention is given to control chart methodologies, capability indices, measurement system analysis and the role of digital technologies in transforming quality monitoring. Findings indicate that the strategic deployment of SQC tools yields measurable reductions in process variability, defect rates and production costs while simultaneously enhancing customer satisfaction and regulatory compliance. The paper concludes with a synthesis of best practices and directions for future research in smart quality control systems.