Comparer des méthodes

	Contrôle statistique des processus assisté par simulation	Diagramme de contrôle
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1980s–present	1924 (first use); 1931 (seminal book)
Auteur d'origine	Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward	Walter A. Shewhart (Bell Labs)
Type	Hybrid quantitative method	Statistical monitoring and control technique
Source fondatrice	Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley. ISBN: 978-0470169926	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand.
Alias	Simulation-based SPC, Monte Carlo SPC, SA-SPC, Simulation-integrated SPC	Shewhart chart, process-behavior chart, SPC chart, quality control chart
Apparentées	6	6
Résumé	Simulation-assisted statistical process control (SA-SPC) combines computer simulation — typically Monte Carlo or discrete-event simulation — with classical SPC methods to design, test, and calibrate control charts and monitoring schemes before or alongside deployment on a real production process. Rather than relying solely on closed-form analytical assumptions, SA-SPC uses simulated data to evaluate chart performance under realistic, often non-normal process conditions.	A control chart is a time-series graph with statistically derived upper and lower control limits that separates the natural, random variation of a process (common cause) from unusual, assignable variation (special cause). Invented by Walter Shewhart at Bell Labs in 1924, control charts remain the foundational tool of Statistical Process Control and are used across manufacturing, healthcare, software, and service industries to monitor whether a process remains stable and predictable over time.