On designing efficient memory-type charts using multiple auxiliary-information

This article intends to investigate new progressive mean (MEP) charts using a single auxiliary characteristic (AMEP) and two auxiliary characteristics (TAMEP) to trace small shifts in the process mean effectively. The effectiveness of the proposed TAMEP scheme is evaluated under the absence and presence of multicollinearity among the two auxiliary variables. The run-length profile of the proposed designs has been computed using statistical metrics: average run length (ARL). Numerical comparison study reveals that the proposed structures prove highly sensitive as compared to counterparts, particularly for the detection of small shifts. The estimation effect of the process parameters on the in-control characteristics of the proposed AMEP chart is also part of this study. An illustrative application related to the fiber tube manufacturing dataset is also provided in this study for the demonstration of the proposed designs.