Statistical Process Control Techniques in Manufacturing Systems
Summary
Statistical process control (SPC) represents a suite of methodologies aimed at monitoring and maintaining the quality of manufacturing processes through the systematic collection and analysis of data. Central to SPC are control charts, which enable practitioners to distinguish between common-cause variation inherent in stable processes and special-cause variation indicative of assignable factors requiring intervention. Traditional Shewhart charts provide a rapid means of detecting large shifts in process means or variability, while more sophisticated schemes such as the cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) charts offer enhanced sensitivity to smaller, gradual shifts. Recent advances have further augmented these techniques through the incorporation of auxiliary information, non-parametric approaches and robust sampling schemes. Together, these developments underpin a global shift towards more intelligent, data-driven quality control frameworks, yielding tangible benefits in diverse manufacturing contexts, from precision machining to semiconductor fabrication.
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Statistical Process Control Techniques in Manufacturing Systems publication trend
The graph below shows the total number of articles in statistical process control techniques in manufacturing systems across all publications each year (not limited to Nature Index journals).
Technical terms
Statistical Process Control (SPC): A methodology for monitoring and controlling manufacturing processes through statistical analysis of measured performance.
Control Chart: A graphical tool displaying process measurements over time with control limits to identify assignable variation.
Shewhart Chart: A basic type of control chart for detecting large shifts in process mean or variability using fixed-sample statistics.
EWMA Chart: An exponentially weighted moving average chart that emphasises recent observations to enhance detection of small or gradual shifts.
Average Run Length (ARL): The expected number of samples or subgroups taken before an alarm is signalled on a control chart.
Non-Parametric Chart: A control chart that does not assume a specific underlying distribution for the quality characteristic.
References
- Efficient Homogeneously Weighted Moving Average Chart for Monitoring Process Mean Using an Auxiliary Variable. IEEE Access (2019).
- Design of a Control Chart Using Extended EWMA Statistic. Technologies (2018).
- On Designing Non-Parametric EWMA Sign Chart under Ranked Set Sampling Scheme with Application to Industrial Process. Mathematics (2020).
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