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In 1996, Pulak and Al-Sultan provided a rectifying sampling inspection plan for determining the optimal process mean, by assuming that the quality characteristic is larger-the-better and is normally distributed. In their inspection plan, the 100% rectifying inspection is executed when the quality of the lot is rejected. Pulak and Al-Sultan obtained the optimal process mean by maximizing the expected profit per item. Quality investment setting, e.g., new equipment, new software system, new material and parts, etc., is usually considered a long-term method for reducing the bias and variability of the product characteristic when the process improvement is required. The major objective of the present paper is to involve the effect of quality investment on the expected profit per item, where the original Pulak and Al-Sultan's model is modified. Numerical results show that our modified model yields the higher expected profit per item than the original model. [ABSTRACT FROM AUTHOR]

This study proposes a modified economic manufacturing quantity (EMQ) model with deteriorating production process. Assume that the quality characteristic of the product is normally distributed. Consider the unstable production process with positive shift and an age-dependent positive drift while process variance increases. The target value of product characteristic is considered to be equal to its specification center and Taguchi’s symmetric quadratic quality loss function is applied to evaluate the product quality. The specification limits, process mean, and EMQ are jointly determined by minimizing the total expected cost of product per unit time under the specified process capability indexCpmvalue. A solution procedure is devised to find the optimal solution for modified EMQ model and the sensitivity analysis of key parameters is conducted to investigate the effect on the optimal solution. Numerical results show that the expected total cost of product per unit time is significantly influenced by the process capability index, the quality loss coefficient, the production rate, the demand rate, and process standard deviation. [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]

In 2000, Roan et al. developed an imperfect production system including raw material procurement cost and finished product cost for determining the optimal process mean, production run size, and raw material order quantity, in which the quality cost of conforming products was not included. Taguchi proposed the quadratic quality loss function for measuring the product quality and redefined the product quality as the loss of society when the product is shipped and sold to the customer. In the present paper, the model developed by Roan et al. is modified with quality loss for obtaining the optimal parameters associated with raw material and production process. Numerical results show that our modified model leads to larger process mean, larger raw material order quantity, higher expected total relevant cost of products, smaller production run size, and smaller number of order quantity of the raw material satisfying each production run than those from the original model, which indicates that the original model given in Roan et al. may underestimate the expected total relevant cost of products due to ignoring the quality loss of conforming products. [ABSTRACT FROM PUBLISHER]

In the present paper, we propose a modified Kouikoglou and Phillis's produce-to-stock system with a the specified process capability index Cpm value under the partly loss sales or complete backlogging. The economic specification limits, specified process capability value, output quantity, and incoming order are jointly determined by maximizing the expected total profit of product. Hundred percent inspection is executed before the products are shipped to the customer. The finite output buffer (production quantity) and rejection of incoming order are also considered in our model. Assuming that the target value of product characteristic is equal to its specification center, the Taguchi's asymmetric quadratic quality loss function is applied to evaluate the product quality. From the above numerical examples and sensitivity analysis of parameters, the results suggest that one can adopt the modified Kouikoglou and Phillis's produce-to-stock system under the complete backlogging. If the produce-to-stock system under the partly loss sales, then one should use the original Kouikoglou and Phillis's model for obtaining the larger expected total profit of product than that of modified one. [ABSTRACT FROM AUTHOR]

In this paper, the author proposes a modified Kapur and Wang's model with unequal target value and specification center. The economic specification limits and process mean are determined by minimizing the expected total quality loss per unit product under the specified process capability index value. The process capability indices Cpm and Cpmk correlated with Taguchi's quadratic quality loss function are considered. The 100% inspection is executed before the product shipped to the customer. Taguchi's asymmetric quadratic quality loss function is applied in evaluating the product quality. The numerical results show that the modified model with specified Cpm value has smaller specification tolerance, larger process mean, and smaller expected total cost per unit product than those of the modified model with specified Cpmk value. [ABSTRACT FROM AUTHOR]

In the present paper, a new economic design with variable single sampling inspection plan for determining the optimal process mean is presented. The quality characteristic with the one-sided lower specification limit is assumed to be normally distributed with unknown mean and known standard deviation. The 100% rectifying inspection plan is adopted when the product lot is rejected. The optimum process mean and the parameters of sampling inspection plan are simultaneously determined by maximizing the expected total profit per unit and meeting the requirement of the specified consumer's risk. Two numerical examples and sensitivity analyzes of parameters are provided for illustration. The numerical results show that, among the model parameters, the price of product for accepted lot, the processing cost per unit, and the lower specification limit significantly affect the expected total profit per unit. [ABSTRACT FROM AUTHOR]

Design of sampling plan always plays a key role in statistical quality control. Dodge-Romig provided the rectifying single sampling plans (SSP) for attributing with the protection of lot tolerance percent defective (LTPD) or the average outgoing quality limit. The on-line 100% rectifying inspection can be applied as a short-term method for controlling the product quality, while the quality investment is an available method for improving the process parameters in the long run. In the present paper, the economic selection of quality investment for designing a Dodge-Romig SSP with LTPD protection is proposed. Both the mean and standard deviation of process characteristic are measured as the exponential function of quality investment. The optimal sampling plan and quality investment level are jointly determined by minimizing the expected total cost of product for a specified consumer's risk. The Dodge-Romig LTPD SSP with quality investment under fuzzy environment that satisfies the consumer's risk is also developed. Numerical examples are provided for illustration. Based on the results of analysis, it can be seen that the quality investment level and the expected total cost of product are significantly influenced by the known process standard deviation and the exponential reduction coefficient of process standard deviation for quality improvement. [ABSTRACT FROM AUTHOR]