The Role of Statistical Process Control in Enhancing Quality Assurance Across Manufacturing and Service Industries

Posted: Jan 08, 2014

• Harper Mitchell
• Liam Moore
• Harper Thomas

Abstract

Statistical Process Control (SPC) has long been recognized as a cornerstone methodology in quality management, with its origins tracing back to Walter Shewhart's pioneering work in the 1920s. Traditional applications of SPC have predominantly focused on manufacturing environments, where measurable characteristics and controlled processes lend themselves naturally to statistical monitoring. However, the contemporary economic landscape is increasingly dominated by service industries, which present unique challenges for quality assurance due to their intangible outputs, customer-centric nature, and higher variability. This research addresses a critical gap in quality management literature by developing and validating a comprehensive framework for SPC application across both manufacturing and service domains. The fundamental research question guiding this investigation is: How can Statistical Process Control methodologies be systematically adapted and integrated to enhance quality assurance in both manufacturing and service industries while maintaining statistical rigor and practical applicability? This paper makes several original contributions to quality management theory and practice. First, we introduce the Cross-Domain SPC Integration Model (CD-SPC-IM), which provides a structured approach for translating manufacturing-based SPC principles to service contexts. Second, we develop hybrid control chart methodologies that combine traditional statistical approaches with contemporary machine learning techniques to address the dynamic nature of service processes. Third, we empirically demonstrate the applicability and effectiveness of our framework through multi-industry case studies, providing quantitative evidence of quality improvement across diverse operational environments.