Comparer des méthodes

	Analyse de Capabilité des Processus Basée sur le Risque	Maîtrise Statistique des Procédés
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s (formal integration with risk analysis)	1924–1931
Auteur d'origine	Evolved from classical capability indices (Juran, Kane) integrated with risk frameworks (FMEA, ISO 9001)	Walter A. Shewhart
Type	Quantitative quality engineering method	Process monitoring and quality control method
Source fondatrice	Montgomery, D. C. (2020). Introduction to Statistical Quality Control (8th ed.). Wiley. ISBN: 978-1119399308	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Alias	RBPCA, risk-adjusted capability analysis, capability-risk integration, risk-informed SPC	SPC, statistical quality control, process control charting, Shewhart control
Apparentées	4	6
Résumé	Risk-based Process Capability Analysis (RBPCA) combines classical process capability indices (Cp, Cpk, Pp, Ppk) with structured risk assessment tools — such as FMEA risk priority numbers — to prioritise improvement actions not merely by how capable a process is, but by the potential harm its failures can cause. The approach is widely used in automotive, aerospace, medical device, and pharmaceutical manufacturing to align quality engineering decisions with risk management requirements.	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.