عنوان مقاله [English]
Nowadays, most products are the results of multistage processes with cascade property. In each step of multistage processes, several variables with reliability characteristic may affect the performance. These reliability variables have some unique properties such as censoring and following the family of location-scale and log-location-scale distributions including Weibull, Log-normal and Log-logistic. The purpose of this paper is to monitor left-censored reliability data with the aid of advanced statistical process control (SPC) techniques in line with statistical modeling approaches. To this end, data modeling has been studied using survival analysis regression models. In fact, the accelerated failure time (AFT) regression model has been employed to establish the relationship between quality variables. Then, two monitoring schemes including a cumulative sum (CUSUM) and an exponentially weighted moving average (EWMA) control charts have been proposed by constructing likelihood function and conditional expected values (CEVs) respectively. It should be noted that the transformation of Weibull distribution to standard smallest extreme value distribution has been done to effectively remove the cascade property in the discussed process. Subsequently, the performance of the proposed control schemes has been examined and investigated in terms of average run length (ARL) criterion under various censoring levels of low, medium and high (20%, 50% and 80%). Finally, the performance of the superior (the cumulative sum) control chart has been investigated in a real case study in Taktab Zarif company located in Kashan, Iran. The quality variable of interest is actually the tensile strength of thread which is affected by the proportion (weight) of raw materials. Moreover, the values corresponding to tensile strength of threads are recorded just in case that they are beyond the specified level. In other words, we encounter some data which are left-censored and thus remedial action should be considered to alleviate the mentioned obstacle for optimal multistage process monitoring.