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Sampling inspection is one of preventive quality methods for decreasing waste of time and cost, but should be along with other quality control approaches to effectively enhance product quality. Determination of optimum process mean tends to reduce the bias of the central tendency of process characteristic from its target, and has been an important topic in quality system design. Pulak and Al-Sultan developed a single sampling rectifying inspection model to determine the optimum process mean, based on maximization of the expected total profit per item. Since setting the economic specification limits for process characteristic is considered a short-term approach for quality assurance and, on the other hand, quality investment is a long-term approach for process improvement, in the present paper, a modified product inspection model, which is modified from Pulak and Al-Sultan's work, is firstly presented to simultaneously determine the optimum process mean and economic specification limits, based on maximization of the expected total profit per item. Then, quality investment is integrated into this modified model to obtain the optimum quality investment level. [ABSTRACT FROM AUTHOR]

In this paper, the author presents a jointly design model with manufacturing quantity, specification limits, process mean and the parameters of X-bar control chart based on the specified process capability index value. The quality loss of a product is set to be related to the limits of the control chart. Assume that the quality characteristic of a product is normally distributed with known standard deviation and adjustable mean. The production process may have a shift in the process mean, while the process standard deviation remains constant. The optimal combination of parameters is determined by minimizing the expected total cost of product under the specified value. This integrated model is a generalization of the economic manufacturing quantity (EMQ) model with statistical quality control chart. Finally, the numerical example and sensitivity analysis of parameters will be provided for illustration. [ABSTRACT FROM AUTHOR]

Purpose The quality level setting problem determines the optimal process mean, standard deviation and specification limits of product/process characteristic to minimize the expected total cost associated with products. Traditionally, it is assumed that the product/process characteristic is normally distributed. However, this may not be true. This paper aims to explore the quality level setting problem when the probability distribution of the process characteristic deviates from normality.Design/methodology/approach Burr developed a density function that can represent a wide range of normal and non-normal distributions. This can be applied to investigate the effect of non-normality on the studies of statistical quality control, for example, designs of control charts and sampling plans. The quality level setting problem is examined by introducing Burr’s density function as the underlying probability distribution of product/process characteristic such that the effect of non-normality to the determination of optimal process mean, standard deviation and specification limits of product/process characteristic can be studied. The expected total cost associated with products includes the quality loss of conforming products, the rework cost of non-conforming products and the scrap cost of non-conforming products.Findings Numerical results show that the expected total cost associated with products is significantly influenced by the parameter of Burr’s density function, the target value of product/process characteristic, quality loss coefficient, unit rework cost and unit scrap cost.Research limitations/implications The major assumption of the proposed model is that the lower specification limit must be positive for practical applications, which definitely affects the space of feasible solution for the different combinations of process mean and standard deviation.Social implications The proposed model can provide industry/business application for promoting the product/service quality assurance for the customer.Originality/value The authors adopt the Burr distribution to determine the optimum process mean, standard deviation and specification limits under non-normality. To the best of their knowledge, this is a new method for determining the optimum process and product policy, and it can be widely applied. [ABSTRACT FROM AUTHOR]

Chung and Hou develop a deteriorating production system with allowable shortage for determining the optimal production run time. The rework cost of the defective products is included in their model. However, they did not consider the quality cost of non-defective products. In this study, the author proposes a modified Chung and Hou’s model for obtaining the optimal production process parameters (process quality level and production run time) under the minimization of the expected total relevant cost of product per unit time. Taguchi’s asymmetric quadratic quality loss function is used for measuring the product quality. [ABSTRACT FROM PUBLISHER]

The exponentially weighted moving average (EWMA) control charts with variable sampling intervals (VSIs) have been shown to be substantially quicker than the fixed sampling intervals (FSI) EWMA control charts in detecting process mean shifts. The usual assumption for designing a control chart is that the data or measurements are normally distributed. However, this assumption may not be true for some processes. In the present paper, the performances of the EWMA and combined X-EWMA control charts with VSIs are evaluated under non-normality. It is shown that adding the VSI feature to the EWMA control charts results in very substantial decreases in the expected time to detect shifts in process mean under both normality and non-normality. However, the combined X-EWMA chart has its false alarm rate and its detection ability is affected if the process data are not normally distributed. [ABSTRACT FROM AUTHOR]

Kapur and Wang [The winter Annual Meeting of the American Society of Mechanical Engineers (1987) p. 23] addressed the problem of product having the specification limits and process being not capable of specifications. They presented that the inspection policy in an online quality control system has a short-term approach to reduce variance of the items shipped to the customers. In this paper, we further propose the modified Kapur and Wang’s model with the linear quality loss of product within specifications for determining a one-sided optimum specification limit. [ABSTRACT FROM AUTHOR]

Chen and Chou presented the economic design of type I continuous sampling plan (CSP-1 plan) under the linear inspection cost. However, it can be argued that the production process is seldom independent. In this paper, we further propose the problem concerning the economic design of CSP-1 plan under the dependent production process and linear inspection cost. A solution procedure is developed to find the unique combination (i*, f*) that will meet the average outgoing quality limit (AOQL) requirement while also minimizing the total expected cost per unit produced during one inspection cycle. [ABSTRACT FROM AUTHOR]

The article explores the problem of an economically based type I continuous sampling plan (CSP-1 plan) under linear inspection cost. By assuming that the per unit inspection cost is linearly proportional to the average number of inspections per inspection cycle, and by solving the modified Cassady et al.'s model, we not only have the required level of product quality but also obtain the minimum total expected cost per unit produced. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present the problem of the design of integrating Dodge-Romig lot tolerance per cent defective (LTPD) single sampling plans (SSP) for variables and specification limits. Considering that the quality characteristic obeys a normal distribution, we propose a modification of Kapur and Wang's model for obtaining the optimal inspection policy of Dodge-Romig LTPD SSP for variables. [ABSTRACT FROM AUTHOR]

This article explores the problem of designing a CSP-1 plan with the specified average outgoing quality limit (AOQL), the acceptable quality level (AQL), and the limiting quality level (LQL) value. By adopting the regret-balanced criterion under the producer's and consumer's interests of quality, we can design the optimal CSP-1 plan. [ABSTRACT FROM AUTHOR]