Advances in Statistical Process Control and Process Monitoring

Dear Colleagues,

Against the backdrop of the intelligent upgrading of manufacturing and the refined development of quality control, practices in global industrial and academic circles are breaking the originally relatively fragmented research pattern between statistical process control and process monitoring. In response, new interdisciplinary and practical research has gradually emerged around core concepts such as "statistical process control (SPC)", "process monitoring", "intelligent process management and control", "quality anomaly diagnosis", and "data-driven process optimization."

We aim to deeply integrate statistics, data science, intelligent algorithms and engineering practices, integrate the classic theories of statistical process control with advanced modern process monitoring technologies, and study their practical applications in various industrial processes, production systems and quality control. Through this, we can achieve accurate identification, real-time early warning and efficient diagnosis of process anomalies, thereby improving the stability of production quality, reducing operational costs and optimizing production efficiency. The stability and controllability of processes are the core premise for the high-quality development of industries. Therefore, in-depth exploration of the theoretical innovation and practical application of statistical process control and process monitoring is crucial for promoting the transformation and upgrading of manufacturing, ensuring product quality and enhancing the core competitiveness of industries.

This emerging research field focuses on the theoretical methods, technological innovations, engineering applications, and their related standards and policy aspects of statistical process control and process monitoring, covering various scenarios such as discrete manufacturing, continuous production, and intelligent manufacturing. It provides theoretical support and technical guarantee for the precise management and control of production processes, anomaly tracing, continuous optimization, and the improvement of quality systems. We look forward to research that integrates "statistical theoretical methods" and "engineering practice needs", promotes the transformation of theoretical achievements into practical applications, and solves the pain points and difficulties in industrial process management and control.

Examples of research include: process anomaly detection methods based on statistical modeling (such as control chart improvement, Bayesian statistical monitoring, multivariate statistical process control), integrated applications of intelligent algorithms and statistical process control (such as machine learning-assisted process monitoring, deep learning-driven anomaly diagnosis), real-time monitoring and dynamic regulation of complex industrial processes, construction of statistical process control systems for intelligent manufacturing, mining and application of process monitoring data, full-process statistical process management and control in the supply chain, and process monitoring and quality optimization practices in various industries (such as machinery manufacturing, electronic appliances, biomedicine, and food processing).

This Special Issue aims to collect review, expository and original papers related to the above-mentioned statistical process control and process monitoring. We welcome submissions of theoretical research, method innovation and empirical application papers to promote academic exchanges and industrial applications in the field of statistical process control and process monitoring.

Dr. Luping Zhao
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• statistical process control
• process monitoring
• process anomaly detection
• intelligent algorithms
• intelligent process management and control
• quality anomaly diagnosis
• data-driven process optimization
• industrial processes