Phase II control charts for monitoring dispersion when parameters are estimated. Issue Volume 29:Issues 4(2017) (2nd October 2017)

• Record Type: Journal Article
• Authors: Diko, M. D.; Goedhart, R.; Chakraborti, S.; Does, R. J. M. M.; Epprecht, E. K.
• Is Part Of: Quality engineering. Volume 29:Issues 4(2017)
• Page Start: 605
• Page End: 622
• Publication Date: 2017-10-02
• Subjects: average run length (ARL) -- control charts -- control limits -- Phase II analysis -- process dispersion -- statistical process control; Quality control -- Periodicals; Production management -- Quality control -- Periodicals; 658.5
• DOI: 10.1080/08982112.2017.1288915
• Languages: English
• ISSNs: 0898-2112

Abstract

ABSTRACT: Shewhart control charts are among the most popular control charts used to monitor process dispersion. To base these control charts on the assumption of known in-control process parameters is often unrealistic. In practice, estimates are used to construct the control charts and this has substantial consequences for the in-control and out-of-control chart performance. The effects are especially severe when the number of Phase I subgroups used to estimate the unknown process dispersion is small. Typically, recommendations are to use around 30 subgroups of size 5 each. We derive and tabulate new corrected charting constants that should be used to construct the estimated probability limits of the Phase II Shewhart dispersion (e.g., range and standard deviation) control charts for a given number of Phase I subgroups, subgroup size and nominal in-control average run-length (ICARL). These control limits account for the effects of parameter estimation. Two approaches are used to find the new charting constants, a numerical and an analytic approach, which give similar results. It is seen that the corrected probability limits based charts achieve the desired nominal ICARL performance, but the out-of-control average run-length performance deteriorate when both the size of the shift and the number of Phase I subgroups are small. This is the price one must pay while accounting for the effects of parameter estimation so that the in-control performance is as advertised. An illustration using real-life data is provided along with a summary and recommendations.