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| Flerrespons-kontrollschema× | Kontrollkort – Statistisk processövervakning med Shewhart-diagram× | |
|---|---|---|
| Ämnesområde | Försöksplanering | Försöksplanering |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 1947 (Hotelling T²); 1980s–1990s (MEWMA, MCUSUM extensions) | 1924 (first use); 1931 (seminal book) |
| Upphovsperson≠ | Harold Hotelling (multivariate foundation); extended by Lowry, Woodall, and others | Walter A. Shewhart (Bell Labs) |
| Typ≠ | Multivariate statistical process monitoring | Statistical monitoring and control technique |
| Ursprungskälla≠ | Hotelling, H. (1947). Multivariate quality control illustrated by the air testing of sample bombsights. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques of Statistical Analysis (pp. 111–184). McGraw-Hill. link ↗ | Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. link ↗ |
| Alias | multivariate control chart, multi-response SPC, MRCC, multiple-response monitoring chart | Shewhart chart, process-behavior chart, SPC chart, quality control chart |
| Närliggande | 6 | 6 |
| Sammanfattning≠ | A multi-response control chart simultaneously monitors two or more correlated quality characteristics on a single chart, preserving the correlation structure that univariate charts ignore. Built on Hotelling's T² statistic and its time-weighted extensions (MEWMA, MCUSUM), it detects process shifts that would be missed if each response were charted independently. It is the standard tool in manufacturing and service quality when product performance depends on multiple interrelated outputs. | 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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