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| Wykres kontrolny wspomagany symulacją× | Wykres Kontrolny× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1920s (control charts); simulation integration from 1980s–1990s | 1924 (first use); 1931 (seminal book) |
| Twórca≠ | Walter A. Shewhart (control charts); simulation integration developed through work of W.H. Woodall, D.C. Montgomery and collaborators | Walter A. Shewhart (Bell Labs) |
| Typ≠ | Hybrid quality monitoring method | Statistical monitoring and control technique |
| Źródło pierwotne≠ | Woodall, W. H., & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31(4), 376–386. DOI ↗ | Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. link ↗ |
| Inne nazwy | simulation-based SPC, Monte Carlo control chart design, simulation-enhanced SPC, virtual control chart | Shewhart chart, process-behavior chart, SPC chart, quality control chart |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | 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. | 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. |
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