Statistical Process Control in Industrial Engineering

Abstract

Statistical Process Control (SPC) stands as a pillar of quality operations and the optimization of the processes in the field of industrial engineering. SPC is a set of statistical techniques used to monitor, control, and implement improvements on the production process through the identification of variability, reduction of defects, and improvement of the working process. The research studies the SPC methods including control charts, process capability study and Six Sigma strategies with emphasis on their use in manufacturing systems, service systems and production systems. The paper combines the theoretical background and applied implementation policies with a focus on real-time monitoring and early detection of deviations, as well as continuous improvement. Results have shown that effective utilization of SPC can greatly result in stability of a process, lower expenditure, and quality of the product. The most important difficulties, such as human issues, accuracy of data collection, and compatibility with Industry 4.0 technologies, are also addressed. The study adds value to the overall idea of the importance of SPC in industrial engineering and offers practical recommendations to those practitioners and researchers interested in the field of optimizing production systems.