Comparar métodos

	Análise Bayesiana de Capacidade de Processo	Controle Estatístico de Processo Bayesiano
Área	Delineamento experimental	Delineamento experimental
Família	Process / pipeline	Process / pipeline
Ano de origem	Classical PCA: 1986; Bayesian extensions: 1990s–2000s	1950s (foundations); formalized 1990s–2000s
Autor original	Bayesian extensions developed by multiple authors including Bernardo, Smith, and Vannman; classical PCA by Juran and Kane (1986)	Various (Girshick & Rubin 1952 early signal detection; Menzefricke 2002 Bayesian control chart framework)
Tipo	Bayesian statistical quality method	Bayesian process monitoring technique
Fonte seminal	Kotz, S., & Johnson, N. L. (2002). Process Capability Indices — A Review, 1992–2000. Journal of Quality Technology, 34(1), 2–19.	Menzefricke, U. (2002). On the evaluation of control chart factors for monitoring the process mean and variance. Journal of Quality Technology, 34(2), 167–178.
Outros nomes	Bayesian PCA, Bayesian capability indices, Bayesian Cp/Cpk estimation, Bayesian process performance analysis	Bayesian SPC, Bayesian process monitoring, B-SPC, Bayesian control charting
Relacionados	5	5
Resumo	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.	Bayesian Statistical Process Control (Bayesian SPC) extends classical SPC by replacing fixed, frequentist control limits with a probabilistic framework that incorporates prior knowledge about the process. Rather than waiting for a run of points to exceed a pre-set 3-sigma boundary, Bayesian SPC continuously updates the probability that the process has shifted given the incoming data, enabling earlier and more informed detection of out-of-control states while formally accounting for uncertainty in process parameters.