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| 시뮬레이션 지원 공정 능력 분석× | 통계적 공정 관리× | |
|---|---|---|
| 분야 | 실험설계 | 실험설계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1980s–1990s (mature practice by mid-1990s) | 1924–1931 |
| 창시자≠ | Developed through integration of Monte Carlo simulation with classical capability indices (Juran, Kane, Kotz and colleagues) | Walter A. Shewhart |
| 유형≠ | Quantitative engineering quality method | Process monitoring and quality control method |
| 원전≠ | 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 |
| 별칭 | Monte Carlo process capability, simulation-based Cpk analysis, stochastic capability analysis, virtual process capability study | SPC, statistical quality control, process control charting, Shewhart control |
| 관련 | 6 | 6 |
| 요약≠ | 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. |
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