Statistical Process Control and Reliability Engineering: Contemporary Practices and Emerging Trends
Keywords:
Statistical Process Control, Reliability Engineering, Control Charts, Predictive Maintenance, Quality Control, Industrial EngineeringAbstract
Statistical Process Control (SPC) and Reliability Engineering are foundational elements of quality engineering, playing a crucial role in ensuring the efficiency, consistency, and robustness of industrial systems. SPC utilizes statistical methods to monitor, analyze, and control manufacturing processes, helping to detect variations and maintain product quality. Reliability engineering, on the other hand, focuses on the ability of systems and components to perform their intended functions over time, considering factors such as failure rates, maintenance, and life-cycle performance.
This review provides a comprehensive analysis of both traditional and contemporary practices in SPC and reliability engineering. Key topics include control charts, process capability analysis, reliability modeling, failure prediction, preventive and predictive maintenance, and the integration of intelligent systems for real-time monitoring. The paper also explores emerging trends such as the adoption of Industry 4.0 technologies, machine learning algorithms, digital twins, and advanced analytics, which are transforming how industrial processes are monitored and maintained.
How to cite this artcle:
Gupta S, Statistical Process Control and Reliability Engineering: Contemporary Practices and Emerging Trends. J Adv Res Prod Ind Engg 2026; 13(1): 21-25.
References
Montgomery, D. C. (2019). Introduction to Statistical Quality Control. Wiley.
Psarakis, S., & Castagliola, P. (2017). SPC in Industry 4.0: A review. Quality and Reliability Engineering International.
Ebeling, C. E. (2010). An Introduction to Reliability and Maintainability Engineering. Waveland Press.
