Journal of Quality Engineering and Management

Abstract Nowadays due to diversity of customer demand and short time for product evolution cycle in market, manufacturing strategy is tended to short run processes characterized by high diversity and low volume. Hence, statistical process control for such processes because of inspection restrictions in a short period is a special and significant practice. In such circumstances, control charts in Phase I cannot be performed and also correct estimations are not available for appraising process parameters. Therefore, it is essential to design new control charts and to utilize them instead of traditional control charts for monitoring such processes. On the other hand, sometimes quality characteristics are described by a relationship between a response variable and one or more explanatory variables, referred to as profile in the literature. In this paper for monitoring quality characteristics delineated by simple linear profiles in short run processes, three control charts are designed to monitor profile parameters (intercept, slope and standard deviation).These control charts have a capability to update the parameter estimations along with new observations and concurrent checks of the out-of-control conditions. The Performance of the proposed method has been compared with competitor control chart by using simulation studies and average run length criterion. The results show that proposed method in some parameters has better performance compared to the competitor control chart in detecting moderate and large shifts.              

Abstract In many statistical process control applications, the relationship between a response variable and one or more control variables is evaluated by a function called a profile. Profiles are divided into different types according to the nature of the response variable, such as linear and nonlinear profiles. In this research, a new control diagram based on the generalized linear test approach and fractional regression is presented to monitor multiple linear profiles with interactions in phase 2. The simulation results of the proposed control diagram show its much better performance than the control diagram based on the least squares error method.

Abstract In the statistical control of multivariate processes, two or more quality characteristics must be controlled simultaneously. In controlling such processes, two main goals must be achieved. The first goal is to detect out-of-control conditions and the second goal is to identify the quality characteristics that cause the deviation when an out-of-control condition occurs. In this research, ways to achieve the first goal are investigated and methods for monitoring the multivariate variance-covariance matrix in phase 2 are presented. The main goal of phase 2 is to quickly detect shifts. In this paper, two methods for monitoring the multivariate variance-covariance matrix in phase 2 are presented and the shift in one of the quality characteristics of case 1 (average trail length) and the detection of ARL are investigated. Simulation results show that the proposed methods reduce the out-of-control condition more quickly.

Abstract Nowadays, many products are the outputs of multi-stage processes. In such processes, the stages are often interdependent, meaning that the quality of the product in a particular stage depends not only on the quality in the current stage but also on the quality of the product in the previous stages. This phenomenon is referred to as the cascading property of multi-stage processes. The existence of this property and the lack of attention to it can lead to errors in interpreting the control charts used in the stages. Therefore, in the literature on multi-stage process monitoring, methods have been proposed to reduce or eliminate this problem. On the other hand, in some cases, the quality of a product is described by the relationship between a response variable and one or more independent variables, known as a profile, which can be the result and output of a multi-stage process. Since fewer studies have been conducted on monitoring profiles resulting from multi-stage processes, this paper investigates the effect of the cascading property on the monitoring of simple linear profiles in a two-stage process, based on the average run length criterion using simulation, and also examines the effect of inter-stage dependency on the estimation of the profile parameters of the second stage.