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	Analiza Zdolności Procesu metodą Bayesa	Wykres Kontrolny
Dziedzina	Planowanie eksperymentów	Planowanie eksperymentów
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	Classical PCA: 1986; Bayesian extensions: 1990s–2000s	1924 (first use); 1931 (seminal book)
Twórca	Bayesian extensions developed by multiple authors including Bernardo, Smith, and Vannman; classical PCA by Juran and Kane (1986)	Walter A. Shewhart (Bell Labs)
Typ	Bayesian statistical quality method	Statistical monitoring and control technique
Źródło pierwotne	Kotz, S., & Johnson, N. L. (2002). Process Capability Indices — A Review, 1992–2000. Journal of Quality Technology, 34(1), 2–19.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand.
Inne nazwy	Bayesian PCA, Bayesian capability indices, Bayesian Cp/Cpk estimation, Bayesian process performance analysis	Shewhart chart, process-behavior chart, SPC chart, quality control chart
Pokrewne	5	6
Podsumowanie	Bayesian Process Capability Analysis integrates Bayesian inference with classical capability indices (Cp, Cpk, Cpm) to estimate how well a production process meets specification limits. Rather than relying solely on observed sample data, it incorporates prior knowledge about process parameters — yielding more stable and credible estimates of process capability, especially under small sample sizes common in manufacturing and quality engineering.	A control chart is a time-series graph with statistically derived upper and lower control limits that separates the natural, random variation of a process (common cause) from unusual, assignable variation (special cause). Invented by Walter Shewhart at Bell Labs in 1924, control charts remain the foundational tool of Statistical Process Control and are used across manufacturing, healthcare, software, and service industries to monitor whether a process remains stable and predictable over time.