Journal of Quality Engineering and Management

Abstract In this article, we introduce three new distribution-free Shewhart-type control charts that exploit run and Wilcoxon-type rank-sum statistics to detect possible shifts of a monitored process is introduced. Exact formulae for the alarm rate, the run length distribution, and the average run length (ARL) are all derived. A key advantage of these charts is that, due to their nonparametric nature, the false alarm rate (FAR) and in-control run length distribution is the same for all continuous process distributions. Tables are provided for the implementation of the charts for some typical FAR values. Furthermore, a numerical study carrited out reveals that the new charts are quite flexible and efficient in detecting shifts to Lehmann-type out-of-control situations.
Statistical quality control charts were introduced in the early work of Shewhart (1926) and since then several variations of them have been proposed for monitoring continuous characteristics. Most of the control charts are distribution-based procedures in the sense that the process output is assumed to follow a specified probability distribution (usually normal); see, for example, Albers et al. (2004).

Abstract Any statistical method designed to detect process changes over time is within the scope of statistical process control. Among the most widely used tools for statistical process control, the control chart is the most important and powerful tool for statistical process control. Here it is important to understand that statistical process control charts are not complete in relation to process control in two respects. They do not have non-random causes and their elimination. In this paper, we introduce robust control diagrams for time-series data to detect reasoned deviations. We collect daily diagrams and draw solid control charts and standard control charts for time series data. Statistical analysis of this design was performed using SPSS16 software and finally by comparing Solid control diagram With standard control diagram for time series data, we conclude that the stable control diagram has a better performance than the standard control diagram. 

Abstract  Different types of process control methods exist for jointly monitoring of several quality characteristic. Recent studies have shown that multi-stage sampling control charts perform better in detecting shifts in process. Therefore, because of the efficiency of the joint mean and standard deviation charts with multi-stage sampling in detecting shifts in mean and standard deviation of the process, discussion and consideration of these charts is necessary. In this paper, the joint Double Sampling and Triple Sampling mean and standard deviation charts are introduced. The joint Statistical Design of these charts is formulated as an optimization problem and solved using a genetic algorithm. 
 
 

Abstract Identifying significant changes in key indicators of financial processes is one of the points of interest in recent years and after the financial crisis. Control charts are one of the most powerful tools used in this field. A noteworthy point in the practical use of control charts is the estimation of process parameters based on available data. Despite extensive research into the effect of parameter estimation on the performance of control charts, little research has been done on processes with time-dependent observations. Due to the importance and widespread use of the Auto Regressive Conditional Heteroskedasticity (ARCH) time series model in monitoring financial processes, in this paper, the effect of prototype size for estimating model parameters on the performance of phase 2 control charts is investigated. The performance of each control diagram is evaluated using simulation studies based on AARL and SDARL criteria and the results are described.

Abstract Traditional control charts are based on the basic assumption that process data are sequentially independent of each other and have a normal distribution. However, in many real-world cases, including chemical and continuous processes, this basic assumption does not exist and there is a kind of autocorrelation between the data collected from the process. The use of traditional control charts in autocorrelated processes is unreliable and increases false alarms. One of the methods developed to control autocorrelated processes is to identify the structure of the process time series and use the residual values ​​to control the process. In this paper, a model based on neuro-fuzzy adaptive systems is designed to identify the structure of the time series and use the prediction. Finally, it is AR(2). Also, residual control charts based on this system for second-order autoregressive data using simulated data, the efficiency of the proposed method in the weighted moving average chart and for different degrees of correlation are evaluated, and it is shown that the proposed method has very good efficiency for data with high correlation.

Abstract The importance of software is increasing day by day, and with this growing importance, continuous efforts are being made to develop technologies that lead to the creation of high-quality software. Software metrics are essential tools for project and quality management. In addition to selecting appropriate metrics for monitoring, detecting meaningful behaviors and changes or deviations in the process, analyzing them, and determining whether process shifts are statistically significant are also crucial.
Since statistical quality control is one of the well-established approaches for addressing these issues, this paper aims, for the first time, to identify and categorize all techniques used in the existing literature related to Statistical Process Control (SPC) in software processes. This classification helps researchers recognize practical topics and research directions and supports them in conducting useful and statistically sound studies based on the compiled material.