Journal of Quality Engineering and Management

Abstract Purpose: In many industrial processes, there are situations where simultaneous monitoring and control of two or more dependent variables are necessary. In such cases, univariate control of quality characteristics can be misleading when considered independently. In the classical approach, when a quality characteristic falls outside the specified technical limits, the quality loss is regarded as a cost. All products within the technical limits of the quality characteristic are assumed to have similar quality, regardless of the deviation of the quality characteristic from its target value. However, it is essential to distinguish between products that fall within the technical limits of the quality characteristic, as any deviation from the target value incurs a proportional loss.
Methodology: This paper introduces, for the first time in the literature, a reflected normal loss function to determine the average cost of producing non-conforming products when two quality characteristics are evaluated. In summary, this study focuses on the statistical-economic design of a multivariate T²-Hotelling control chart with multivariate variable sampling intervals in the presence of a reflected normal multivariate loss function (RNLMVSIT²). Additionally, a sensitivity analysis is conducted to examine the effects of time and cost parameters on the design parameters and the average cost.
Findings: The results demonstrate the satisfactory performance of the proposed models.
Originality/Value: This paper introduces, for the first time in the literature, a reflected normal loss function to determine the average cost of producing non-conforming products when two quality characteristics are evaluated.

Abstract The economic design of control charts depends on the process shock model distribution and due to difficulties from both theoretical and practical aspects. This paper pursues to develop the economic design of X ̅ control chart for monitoring continuous flow processes under Bur XII shock model. The Burr XII distribution is flexible and its hazard rate can take many forms like as fixed, increasing, decreasing, and single mode or even U-shaped. Continuous flow processes are used in batch processes such as those meet to filtering, chemical processing and etc. A sensitivity analysis is executed and numeric examples are given to illustrate the effects of changing the parameters of the shock model distribution on the optimum values of the economic design models as discussed in this paper. In addition, we use uniform and non-uniform sampling schema and comparison between them. This comparison show that for non-constant hazard rate, non-uniform sampling scheme is better.

Abstract Control charts are one of the most important tools for evaluating process performance and monitoring. In the classic design of a control chart, the cost of quality depends on whether the quality characteristic is inside or outside the control. The use of loss function in the design of control charts, as an estimator of the cost of production of defective products, contributes to a more comprehensive assessment and better management decisions. Therefore, in this article, the combination of loss function and economic statistical design of control charts. The loss functions used so far in this field have been symmetric functions, but in many cases overestimating or underestimating the ideal value for a quality characteristic does not produce the same losses. Therefore, for the first time in the literature on the design of control charts, this paper uses the asymmetric loss function of Linux. Using a practical example, the performance of quadratic, linear, exponential and linear loss functions are compared. The result of these comparisons showed that the Linex loss function has the lowest cost in statistical-economic design of the control chart compared to other loss functions.

Abstract Abstract Control charts are used in process monitoring to identify any changes that may affect process quality. In many cases, it is assumed that the process data has a normal distribution, which may not be the case in practice. In this paper, we examine the economic statistical design of the   X ̅ control diagram when the qualitative characteristic distribution is not normal with the Markov chain approach. In this regard, we use the distribution as a model for the variable distribution of process quality. Due to the flexibility of its components, this distribution can model many distributions, including the normal distribution. We also show the design performance by analyzing the sensitivity of process parameters and based on the values ​​of skewness and elongation of the community, using genetic algorithm, for industrial application.