Comparer des méthodes

	Robust Six Sigma DMAIC	Maîtrise Statistique des Procédés
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s (integration period)	1924–1931
Auteur d'origine	Motorola (Six Sigma, 1986); Taguchi robust design integrated into DMAIC by quality engineering practitioners in the 1990s–2000s	Walter A. Shewhart
Type	Hybrid process improvement and robust engineering methodology	Process monitoring and quality control method
Source fondatrice	Antony, J. (2006). Six Sigma for service processes. Business Process Management Journal, 12(2), 234–248.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Alias	Robust DMAIC, Six Sigma with Robust Design, Taguchi-integrated DMAIC, R-DMAIC	SPC, statistical quality control, process control charting, Shewhart control
Apparentées	4	6
Résumé	Robust Six Sigma DMAIC embeds Taguchi's robust design philosophy within the classic Define-Measure-Analyze-Improve-Control framework. Rather than optimizing a process only for average performance, this hybrid approach simultaneously minimizes process variation caused by noise factors — environmental shifts, material lot differences, operator variability — so that the outcome remains near target even when uncontrollable conditions change. The result is a process that is both capable and insensitive to real-world disturbances.	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.