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Process capability plots—a quality improvement tool
Author(s) -
Deleryd Mats,
Vännman Kerstin
Publication year - 1999
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/(sici)1099-1638(199905/06)15:3<213::aid-qre245>3.0.co;2-o
Subject(s) - process capability , process capability index , plot (graphics) , process (computing) , computer science , point (geometry) , quality (philosophy) , scatter plot , data mining , work in process , engineering , mathematics , machine learning , statistics , operations management , philosophy , geometry , epistemology , operating system
We introduce the concept of process capability plots, which are powerful tools to monitor and improve the capability of industrial processes. An advantage of using a process capability plot, compared with using a traditional process capability index alone, when deciding whether a process can be considered capable or not, is that we will instantly get information about the location and spread of the studied characteristic. When the process is non‐capable, the plots are helpful when trying to understand if it is the variability, the deviation from target or both that need to be reduced to improve the capability. In this way the proposed graphical methods give a clear direction of quality improvement. We evaluate two different process capability plots, the (δ * , γ * )‐plot and the confidence rectangle plot, from a theoretical as well as a practical point of view. When studying them from a theoretical point of view, among other things, a simulation study is conducted to investigate the ability of each of the two methods to identify that a process is capable when it actually is. The comparison from a practical point of view is made by discussing the advantages and disadvantages of the two methods in different practical situations. Based on the above‐mentioned comparisons, the recommendation is that the practitioner should use the (δ * , γ * )‐plot. Copyright © 1999 John Wiley& Sons, Ltd.