Comparar métodos

	Gráfico de Control Bayesiano	Six Sigma DMAIC
Campo	Diseño experimental	Gestión de la calidad
Familia	Process / pipeline	Process / pipeline
Año de origen	Formally developed in the 1990s–2000s; roots in Shewhart (1924)	2014
Autor original	Ulrich Menzefricke and others building on Shewhart (1924) and Bayesian inference (Bayes, 1763)	Motorola; Pyzdek & Keller
Tipo	Statistical process monitoring / quality control	Structured process improvement methodology
Fuente seminal	Menzefricke, U. (2002). On the evaluation of control chart limits based on predictive distributions. Communications in Statistics — Theory and Methods, 31(8), 1423–1440.	Pyzdek, T., & Keller, P. (2014). The Six Sigma Handbook (4th ed.). McGraw-Hill. ISBN: 978-0-07-184053-9
Alias	Bayesian SPC chart, Bayesian monitoring chart, posterior control chart, Bayesian Shewhart chart	DMAIC Framework, Six Sigma Process Improvement Cycle, Define-Measure-Analyze-Improve-Control, Altı Sigma DMAIC
Relacionados	6	3
Resumen	A Bayesian control chart integrates prior knowledge about a process — such as historical mean and variance — with incoming measurement data to produce dynamically updated control limits. Unlike classical Shewhart charts that fix limits from a Phase-I baseline, Bayesian charts update the posterior distribution of process parameters after each sample, yielding limits that adapt to accumulated evidence and are better calibrated under small sample sizes or non-stationary processes.	Six Sigma DMAIC is a data-driven, five-phase process improvement methodology — Define, Measure, Analyze, Improve, and Control — used to reduce defects and process variation to fewer than 3.4 defects per million opportunities. Originating at Motorola in the 1980s and systematized by practitioners including Pyzdek and Keller, it is widely adopted in manufacturing, healthcare, finance, and service industries seeking sustained quality gains.