Investigation the stability of the relationship between a response variable and one or more independent variables over time is termed as profile monitoring. In the area of profile monitoring, fewer researches can be found to the profiles with the non-normal response variable denoted by generalized linear models (GLM). Logistic profiles as one of common the GLM profiles, are the most common types of profiles in which we take advantage of logistic regression to model the relationship. In logistic regression, the response variable is assumed to be non-quantitative and binary. Also, to increase the efficiency of the charts and faster detection of the shifts, we can consider control charts’ parameters such as sample size, sampling intervals and etc., with an adaptive approach, which means that considering the process conditions, parameter values can be unfixed and variable. In this study, we investigate logistic profiles monitoring using three existing control charts entailing T2 control charts, multivariate exponentially weighted moving average (MEWMA), deviance control chart and two proposed control chart including generalized likelihood ratio (GLR) and support vector regression (SVR) methods by considering sampling interval as a variable, denoted with variable sample interval (VSI) approach. Simulation results indicate the much better performance of control charts by adding the VSI than the traditional fixed sampling interval in terms of average time to signal (ATS). Eventually, to clarify how to implement the VSI charts, a numerical example is provided.

Keywords