A manufacturing process cannot be released to production until it has been proven to be stable. Also, we cannot begin to talk about process capability until we have demonstrated stability in our process. This means that the process variation is the result of random causes only and all assignable or special causes have been removed. In complicated manufacturing processes, such as drilling process, the natural instability of the process impedes the use of any CONTROL CHARTs for the mean and standard deviation. However, a complicated manufacturing process can be capable in spite of this natural instability. In this paper we discuss thee C~pk process capability index. We find the membership function of C~pk based on fuzzy data. Also, by using the definition of classical CONTROL CHARTs and the method of Vannman and Castagliola, we propose new CONTROL CHARTs that are constructed by the a-cut sets of C~pk for the natural instable manufacturing processes with fuzzy normal distributions. The results are concluded for a =0.6, that is chosen arbitrarily.

CONTROL CHARTs and maintenance strategies are essential tools in production management. However, despite their inherent connection, these tools are often studied and applied independently. To better reflect real-world scenarios, this paper focuses on the economic design of an integrated production planning model based on a synthetic adaptive Exponentially Weighted Moving Average (EWMA) CONTROL CHART. To mitigate machine failure rates, two types of maintenance strategies are incorporated: reactive maintenance (RM) and preventive maintenance (PM). The model uses the particle swarm optimization (PSO) metaheuristic algorithm to minimize the total production cycle cost while adhering to statistical quality constraints. A comparative analysis is conducted to evaluate the impact of variable sampling intervals in CONTROL CHARTs on overall costs. Sensitivity analysis is performed to examine how model parameters influence optimization policies. Finally, the results are compared with previous studies to demonstrate the effectiveness of the proposed method.

The exponentially weighted moving average (EWMA) and double EWMA are two basic CONTROL CHARTs in the sensitive manufacturing process such as semiconductor manufacturing. Optimal value for the weight parameters of these CONTROL CHARTs are important for evaluation of their performance. In this paper we have used multi criteria decision making (MCDM) and goal programming (GP) models to reduce bias and standard deviation of statistics of EWMA and double EWMA CONTROL CHARTs based on decision maker openion. The proposed model as a Engineering Process CONTROL (EPC) tools can be used for optimal adjastment of sensitive process.

The mixed EWMA-CUSUM and CUSUM-EWMA CONTROL CHARTs are among the CONTROL CHARTs provided in recent years by combining two Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) CHARTs for efficient monitoring of the process mean. In this paper, these mixed CONTROL CHARTs are extended using new median ranked set sampling and double ranked set sampling procedures. The performance of the proposed mixed CONTROL CHARTs is evaluated through extensive Monte Carlo simulations in terms of average run length criterion. The results demonstrate that the proposed CHARTs outperform similar ones in detecting different shifts in the process mean. Furthermore, a real dataset is also presented for explaining the implementation of the proposed CONTROL CHARTs.