Statistical Process Control


Statistical Process Control (SPC) uses time-ordered data to distinguish routine variation from signals indicating meaningful process change. Rather than relying on isolated inspection or targets, SPC supports evidence-based decisions by focusing on patterns over time and understanding causes of variation (Oakland and Oakland, 2007; Mohammed, 2024).

Technique Overview

Statistical Process Control

Statistical Process Control Definition

Statistical Process Control (SPC) is the application of statistical techniques, primarily control charts and process capability analysis, to monitor process performance over time. Its purpose is to identify special-cause variation, maintain stable operations, and support continuous improvement by linking signals in data to structured investigation and corrective action (Oakland and Oakland, 2007; Mohammed, 2024).

Statistical Process Control Description *

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Business Evidence

Strengths, weaknesses and examples of Statistical Process Control *

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Business Application

Implementation, success factors and measures of Statistical Process Control *

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Professional Tools

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Further Reading

Statistical Process Control web and print resources *

Statistical Process Control references (4 of up to 20) *

  • Gejdoš, P., 2015. Continuous quality improvement by statistical process control. Procedia Economics and Finance, 34, pp.565-572. Available at: https://doi.org/10.1016/S2212-5671(15)01669-X
  • Leitner, P.A.P.D., 2005. The lean journey at the Boeing Company. In ASQ World Conference on Quality and Improvement Proceedings (Vol. 59, pp. 263-271). American Society for Quality.
  • MacGregor, J.F. and Kourti, T., 1995. Statistical process control of multivariate processes. Control Engineering Practice, 3(3), pp.403–414. Available at: https://doi.org/10.1016/0967-0661(95)00014-L
  • Middleton, P. and Joyce, D., 2012. Lean software management: BBC Worldwide case study. IEEE Transactions on Engineering Management, 59(1), pp.20–32. https://doi.org/10.1109/TEM.2010.2081675

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Related Concept: Collating and Formatting Data

Before any analysis can happen, data needs to be collated from correct sources and formatted so it follows clear organisational standards. Research shows that inconsistent structures and formats make data harder to combine, process and trust (Jagadish et al., 2014). Collating and formatting ensure the dataset is clean, consistent and ready for use.