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| Anàlisi de sensibilitat amb gràfic de control× | Gràfic de Control× | |
|---|---|---|
| Camp | Disseny experimental | Disseny experimental |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen≠ | Integration practice documented from the 1990s onward | 1924 (first use); 1931 (seminal book) |
| Autor original≠ | Rooted in Shewhart (control charts, 1920s) and Saltelli et al. (global sensitivity analysis, 1990s–2000s); integration practice developed in quality engineering literature | Walter A. Shewhart (Bell Labs) |
| Tipus≠ | Hybrid analytical framework | Statistical monitoring and control technique |
| Font seminal≠ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975 | Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. link ↗ |
| Àlies | SA-SPC integration, control chart sensitivity analysis, SPC sensitivity assessment, sensitivity-enhanced control charting | Shewhart chart, process-behavior chart, SPC chart, quality control chart |
| Relacionats | 6 | 6 |
| Resum≠ | Sensitivity analysis integrated with control charting evaluates how uncertain or varying inputs — such as sample size, subgroup frequency, distribution assumptions, or measurement error — affect the detection performance of a statistical process control chart. By quantifying which parameters most strongly influence chart metrics such as the average run length (ARL) or false alarm rate, engineers can design more robust monitoring schemes and understand where control chart conclusions are fragile. | 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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