Journal of Quality Engineering and Management

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  
 If the identification of small changes in the production process is intended, the Moving Averaging Control Chart (EWMA) is a good alternative to the X control chart. In situations where a large sample of the population cannot be extracted due to economic constraints, the Simple Random Sampling Scheme (SRS) may not be accurate enough, in which case the RSS can be used. Appeared. In this paper, for the first time, the economic and statistical-economic design of the EWMA control chart under the RSS design is reviewed. By presenting numerical results, the advantages of statistical-economic design over economic design are shown. The results show that costs in statistical-economic design have increased slightly compared to economic design, but due to the low false alarm rate are in line with statistical quality control objectives and at the same time reduce costs. , Controls the quality of the product at the desired level of error and high power. Keywords: Statistical-economic design, ranking set sampling, shock model. 

Abstract One of the most important tools for statistical process control is the control chart. The construction of control diagrams is achieved by determining the design parameters of the sample size, sampling distance and coefficient of control limits. Statistical-economic design is the best way to determine these parameters by considering statistical properties and cost. Statistical design - Economics Control charts require a distribution for the process failure mechanism. The most popular distributions in the analysis of the failure mechanism of the exponential distribution process are gamma and Weibull. Recently, a new distribution called generalized exponential distribution has been added to conventional distributions in data life analysis and process failure mechanisms. If the failure mechanism data follows this distribution, the use of other distributions will give users misleading and inaccurate results. On the other hand, a fundamental assumption in most control charts is the independence of process-related observations; But in practice we have situations with correlated data. As a result, it is of particular importance to identify control charts that can be used to control such data.In this paper, statistical-economic design of control charts with correlated data under generalized shockwave and Weibull models and by providing a numerical example , We show their application. We have applied a sensitivity analysis to investigate the effect of changing the controlled properties of the first type error rate, power and correlation coefficient. The results show that this type of control charts have undesirable statistical properties and the limitation on the first type of error has a significant effect on determining the average cost per unit time and design parameters. Also, the correlation coefficient is indirectly related to the sample size and sampling distance and is directly related to the average cost per unit time.

Abstract Applying preventive maintenance strategies in control chart design, which is one of the main tools in statistical process quality control, not only increases the reliability of the system and reduces its wear and tear, but also reduces the cost of chart design. However, the cost of quality in the classical approach to control chart design, derived from the Crossby football gate philosophy, depends only on whether the quality characteristic is within or outside the control limits. In contrast, using the loss function approach in in-production monitoring activities such as control charts, where quality cost based on Taguchi's concept of social loss quality depends on the amount of quality characteristic deviation from the target value, is a more comprehensive assessment of the process and therefore decisions. It is better guided in planning and management. This paper presents an integrated model of Taguchi loss function, preventive maintenance strategies including incomplete maintenance and early replacement, and economic control diagram design in which the shock model or process failure mechanism has an incremental failure rate. In addition, due to the fact that the data obtained from the measurements related to the output of the production process may not follow the normal distribution or the assumptions of the central limit theorem may not be true for them, it is necessary to study the integrated model in these situations. Normal distribution is necessary. To illustrate the issue, numerical examples based on non-uniform sampling design and Weibull shock model are provided. The adjustment parameters obtained from the integrated model (sample size, sampling intervals, coefficient width of control limits, early replacement time, and cost level and incomplete maintenance time) in addition to significant differences in normal and abnormal conditions, show that with increasing preventive maintenance level, The average design cost will be reduced in both normal and abnormal modes.