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	Bayesowska Analiza Rodzajów i Skutków Możliwych Błędów (Bayesian FMEA)	Statystyczna Kontrola Procesu
Dziedzina	Planowanie eksperymentów	Planowanie eksperymentów
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1990s–2000s	1924–1931
Twórca	Extension of classical FMEA (MIL-STD-1629, 1974) with Bayesian inference formalised in reliability literature from the 1990s onward	Walter A. Shewhart
Typ	Probabilistic reliability and risk analysis	Process monitoring and quality control method
Źródło pierwotne	Bowles, J. B., & Peláez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering and System Safety, 50(2), 203–213.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Inne nazwy	Bayesian FMEA, probabilistic FMEA, B-FMEA, Bayesian risk priority analysis	SPC, statistical quality control, process control charting, Shewhart control
Pokrewne	5	6
Podsumowanie	Bayesian FMEA extends the classical Failure Mode and Effects Analysis framework by replacing fixed point-estimate risk scores with probability distributions, allowing prior engineering knowledge and observed failure data to be formally combined through Bayes' theorem. The result is a probabilistic Risk Priority Number (RPN) that reflects uncertainty in severity, occurrence, and detectability ratings rather than masking it with single consensus values.	Statistical Process Control (SPC) is a data-driven quality method that uses statistical techniques — primarily control charts — to monitor a manufacturing or service process over time. By distinguishing natural process variation (common cause) from unusual, actionable variation (special cause), SPC enables practitioners to maintain processes in a stable, predictable state and to detect problems early, before defective output reaches customers.