Application of X-bar R Control Charts for Process Efficiency Monitoring : a Data-Driven Approach in Quality Management

Abstract

Ensuring process efficiency and product quality remains a critical challenge in modern manufacturing, necessitating the implementation of robust methodologies for process monitoring and optimisation. Lean Six Sigma (LSS), which integrates Lean Manufacturing and Six Sigma principles, is widely adopted to enhance productivity while minimising process variability and waste. A key component of LSS is Statistical Process Control (SPC), which employs control charts to assess process stability and compliance in real time. Despite extensive research on SPC applications, existing studies often fail to systematically differentiate common cause variation from special cause variation and to identify their critical sources in industrial processes. Addressing this gap, the present study evaluates the effectiveness of X-bar R control charts as a data-driven methodology for identifying process inefficiencies. Using Minitab Statistical Software, the study analyses the adhesion parameter of Thermoplastic Polyurethane (TPU) film, a material widely used for electronic screen protection. The methodology involves constructing X-bar R control charts to monitor variability patterns, establish stability thresholds, and pinpoint critical sources of process deviations. The findings demonstrate that X-bar R control charts provide a robust framework for differentiating process variations, facilitating targeted corrective actions to enhance process stability. This research highlights the importance of statistical modelling in industrial decision-making, particularly within automated manufacturing environments. A key contribution of the study lies in its demonstration of the practical applicability of control charts in quality management and the integration of data-driven techniques for process control. Future research should investigate advanced machine learning-based SPC approaches to refine real-time decision-making and expand the applicability of control charts to dynamic and complex production systems. By reinforcing the role of statistical tools in quality engineering and operational excellence, this study contributes to the broader discourse on digital transformation in industrial process optimisation.