Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Statistické řízení procesů založené na riziku× | Regulační diagram× | |
|---|---|---|
| Obor | Plánování experimentů | Plánování experimentů |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 1920s (SPC foundations); risk-based integration formalized in 2000s–2010s | 1924 (first use); 1931 (seminal book) |
| Tvůrce≠ | Integrated from SPC (Shewhart, 1920s; Deming, 1950s) and risk analysis frameworks (FDA ICH Q10, ISO 31000) | Walter A. Shewhart (Bell Labs) |
| Typ≠ | Hybrid quality-risk engineering method | Statistical monitoring and control technique |
| Původní zdroj≠ | Montgomery, D. C. (2020). Introduction to Statistical Quality Control (8th ed.). Wiley. ISBN: 978-1119399308 | Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. link ↗ |
| Další názvy | Risk-based SPC, RBSPC, risk-prioritized SPC, risk-informed process monitoring | Shewhart chart, process-behavior chart, SPC chart, quality control chart |
| Příbuzné | 6 | 6 |
| Shrnutí≠ | Risk-based statistical process control (Risk-based SPC) is an engineering quality method that integrates formal risk analysis — typically FMEA or a risk matrix — with statistical process monitoring to focus control chart resources on the process parameters that pose the greatest risk to product quality or system safety. Rather than applying control charts uniformly across all variables, risk-based SPC directs tighter monitoring toward high-risk, high-impact process characteristics identified through structured hazard prioritization. | 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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