Comparer des méthodes

	Analyse de Capabilité Assistée par Simulation	Maîtrise Statistique des Procédés
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1980s–1990s (mature practice by mid-1990s)	1924–1931
Auteur d'origine	Developed through integration of Monte Carlo simulation with classical capability indices (Juran, Kane, Kotz and colleagues)	Walter A. Shewhart
Type	Quantitative engineering quality method	Process monitoring and quality control method
Source fondatrice	Kotz, S., & Lovelace, C. R. (1998). Process Capability Indices in Theory and Practice. Arnold. ISBN: 978-0340691281	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Alias	Monte Carlo process capability, simulation-based Cpk analysis, stochastic capability analysis, virtual process capability study	SPC, statistical quality control, process control charting, Shewhart control
Apparentées	6	6
Résumé	Simulation-assisted process capability analysis combines Monte Carlo simulation with classical capability indices (Cp, Cpk, Cpm) to evaluate whether a process can consistently meet specification limits when direct measurement is costly, dangerous, or impractical. By propagating input distributions through a process model, the analyst obtains a simulated output distribution and derives capability metrics without waiting for physical production runs. The approach is especially valuable during product design, process scale-up, and tolerance stack-up studies.	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.