Comparer des méthodes

	Analyse des causes profondes 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 (risk-informed extension of classical RCA)	1924–1931
Auteur d'origine	Developed within safety and quality engineering communities; risk integration formalized through CCPS and ISO 31000 frameworks	Walter A. Shewhart
Type	Hybrid risk-analytic investigation method	Process monitoring and quality control method
Source fondatrice	Latino, R. J., & Latino, K. C. (2006). Root Cause Analysis: Improving Performance for Bottom-Line Results (3rd ed.). CRC Press. ISBN: 978-0849380815	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Alias	Risk-based RCA, RBRCA, Risk-weighted root cause analysis, Risk-informed failure investigation	SPC, statistical quality control, process control charting, Shewhart control
Apparentées	6	6
Résumé	Risk-based Root Cause Analysis (RBRCA) integrates classical root cause investigation with quantitative or semi-quantitative risk assessment to ensure that corrective actions are directed first at the causes that carry the highest probability and consequence of recurrence. Unlike standard RCA, which identifies root causes without systematically ranking their hazard potential, RBRCA assigns risk scores to each identified cause, allowing organizations to allocate limited remediation resources where they can reduce overall risk most efficiently.	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.