เปรียบเทียบวิธี

	แผนภูมิควบคุมที่ใช้การจำลอง	การควบคุมกระบวนการทางสถิติโดยใช้การจำลอง
สาขาวิชา	การออกแบบการทดลอง	การออกแบบการทดลอง
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1920s (control charts); simulation integration from 1980s–1990s	1980s–present
ผู้ริเริ่ม	Walter A. Shewhart (control charts); simulation integration developed through work of W.H. Woodall, D.C. Montgomery and collaborators	Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward
ประเภท	Hybrid quality monitoring method	Hybrid quantitative method
แหล่งต้นตำรับ	Woodall, W. H., & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31(4), 376–386.	Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley. ISBN: 978-0470169926
ชื่อเรียกอื่น	simulation-based SPC, Monte Carlo control chart design, simulation-enhanced SPC, virtual control chart	Simulation-based SPC, Monte Carlo SPC, SA-SPC, Simulation-integrated SPC
ที่เกี่ยวข้อง	6	6
สรุป	Simulation-assisted control chart integrates Monte Carlo or discrete-event simulation with traditional Shewhart-type control charting to design, validate, and optimize chart parameters before deployment on a real process. Rather than relying solely on assumed distributional forms, the practitioner builds a simulation model of the process, generates virtual data under in-control and out-of-control scenarios, and uses these runs to calibrate control limits, estimate average run length (ARL), and stress-test chart sensitivity — all without interrupting production.	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.