Sammenlign metoder

	Bayesisk kontrollkart	Six Sigma DMAIC
Fagfelt	Forsøksdesign	Kvalitetsledelse
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	Formally developed in the 1990s–2000s; roots in Shewhart (1924)	2014
Opphavsperson	Ulrich Menzefricke and others building on Shewhart (1924) and Bayesian inference (Bayes, 1763)	Motorola; Pyzdek & Keller
Type	Statistical process monitoring / quality control	Structured process improvement methodology
Opprinnelig kilde	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
Relaterte	6	3
Sammendrag	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.