Journal of Quality Technology · 2009 · 92 citations · 32 references
ManagementQuality ManagementProcess ControlQuality AssuranceQuality ControlControl ChartsMonte Carlo MethodsMonte Carlo SimulationProcess RobustnessStatistical Process ControlProcess ParametersRank-based EstimationReference Sample DevelopmentReference Sample SelectionRobust Control ChartsRobust Statistical Process ControlStatistical Quality AssessmentStatistical Quality Control
Statistical quality control relies on Phase I studies, yet most Phase I control charts assume normality, which is often inappropriate when little process information is available; existing robust or distribution‑free charts mainly address Phase II limits rather than Phase I. We propose a distribution‑free method for defining the in‑control state of a process and identifying an in‑control reference sample. The method uses a rank‑based approach to construct a reference sample that can estimate process parameters for any chosen Phase II procedure, and is compared with the traditional X chart via Monte Carlo simulation. The rank‑based method performs as well as the X chart under normality and outperforms it in many skewed or heavy‑tailed scenarios.
Much of the work in statistical quality control is dependent on the proper completion of a Phase I study. Many Phase I control charts are based on an implicit assumption of normally distributed process observations. In the beginning stages of process control, little information is available about the process and the normality assumption may not be reasonable. Existing robust and distribution-free control charts are concerned with the establishment of Phase II control limits that are robust to nonnormality or outliers from the Phase I sample. Our literature review revealed no purely distribution-free Phase I control-chart methods. We propose a distribution-free method for defining the in-control state of a process and identifying an in-control reference sample. The resultant reference sample can be used to estimate the process parameters for the Phase II procedure of choice. The proposed rank-based method is compared with the traditional X chart using Monte Carlo simulation. The rank-based method compares favorably to the X chart when the process is normally distributed and performs better than the X chart in many situations when the process distribution is skewed or heavy tailed.
32