Journal of Quality Engineering and Management

Abstract Purpose: Calculating the optimal target mean for a process is recognized as an essential research area, with many proposed models in the literature. Previous studies have typically focused on a single market. The main difference in this research lies in the number of markets considered; unlike previous works, this study examines n different markets simultaneously.
Methodology: This study aims to determine the optimal process quality mean for a limited number of markets based on the target values of quality characteristics in each market. We propose a model to calculate this optimal mean across n markets with different price/cost structures. A key innovation of this research is the incorporation of probability distributions that reflect the likelihood of the quality characteristic falling within specific quality ranges in each market.
Findings: The model considers the probability that the quality characteristic falls within each market's defined quality range. To analyze and solve the model, absorbing Markov chains are used. A numerical example is presented in which the model is applied to two markets, and the optimal target mean and corresponding optimal revenue are obtained.
Originality/Value: Based on the results from the numerical example, the optimal target mean and revenue were determined for the two markets. A sensitivity analysis was conducted to assess the influence of various model parameters on these parameters, demonstrating how changes in parameters impact the model's outcomes.

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.