Optimal quality investment and specification limits settings with 100% inspection and sampling inspection for quality protection

This study considers the modified Shin et. al.’s [14] model with process control and tolerance. In the first phase, the process parameters setting model considers Taguchi’s quadratic quality loss and process cost of product for obtaining the original values of mean and standard deviation. Then the second phase, i.e., the tolerance setting model considers the process promotion with quality investment. The optimal tolerance can be also determined under the 100% inspection under the specified Cpkm value and sampling inspection with specified Cpkm value and consumer’s risk, respectively. The numerical result are described for explanation.

In this paper, the rectifying sampling inspection plan with quality investment for the product lot is used in the determination of optimal process mean and standard deviation. Consider the quality characteristic of product is normally distributed with both unknown process mean and unknown standard deviation. The product lot is sold to the different markets according to the defective numbers of sample. Assume that the declining exponential reduction of process mean and standard deviation is the function of quality investment. For a given rectifying inspection plan, one can obtain the optimal quality investment and corresponding improved process mean and standard deviation based on the maximum expected total profit of product lot per unit.

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.

Product-harm crises are the nightmare of any firm due to their disastrous effects on sales and image. These crises lead to loss of consumer trust, severe damage to brand reputation, extensive negative media coverage, legal and financial repercussions, decline in market share, negative impact on investor confidence, and increased regulatory scrutiny. Overall, product-harm crises pose significant challenges to companies, emphasizing the critical importance of effective risk management and crisis preparedness. The present paper proposes a new model to compute the optimal investment in quality and advertising in order to reduce the probability of occurrence of a possible product-harm crisis and mitigate its effects. This method uses stochastic control theory and can be used for both tangible products and services. An extension of this method is also proposed in order to take endogenously competition. This extension uses a game theoretical approach.

In this paper we present a mathematical model of a generic manufacturing system. The quality of the manufactured product is captured through one or more of its critical design/process parameters. Managers often face the dilemma of which product/process is to be singled out for improvement in quality with the limited capital outlay on hand. In this paper, we use the critical process parameters along with the standard production variables in a mathematical programming framework, to identify the process to be targeted for improvement. Consistent high quality, results in higher rewards in a perfectly competitive market place and also requires higher amounts of the employed resources, including capital. The optimal investment to be made in achieving higher rewards depends upon the product characteristics. We consider alternative systems, which differ in their costs of quality, since the underlying process parameters follow different probability distributions. Our experiments provide some important insight into the optimal investments in quality, and the accompanying qualityproductivity trade-offs. We demonstrate that 100% process conformance or 100% use of the productive resources do not result in maximum net profits. Our model also reinforces the notion that consistent high quality ultimately translates into a corresponding gain in productivity and higher profits or net revenues.

This article studies a problem of joint pricing and dynamic product quality investment with consumers' reference quality effect under the existence of quality inflation. Optimal control models are constructed to maximize the total profit with a limited quality investment capacity, where the demand is sensitive to historical product quality level. The optimal quality investment strategies for finite and infinite planning horizon are given respectively by solving these optimal control models on the basis of Pontryagin’s maximum principle, which enables the exact trajectory of the optimal quality investment with the reference quality effect over time to be depicted. In addition, an effective algorithm is designed to generate the optimal joint pricing and dynamic quality investment policy for the system. The main difference between the strategy of finite planning horizon and that of infinite planning horizon is that the latter is a constant. Our study indicates that it is never optimal for firm to increase quality investment all the way throughout the planning horizon. The level of quality investment is higher when taking into account the impact of reference quality. Moreover, numerical example is given to illustrate the validness of the theoretical results. Also, sensitivity analysis is carried out to show how system parameters affect the optimal policies, and some managerial suggestions are presented.

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.

In this paper, the author proposes the modified Banu and Mondal’s model without product warranty for obtaining the optimal quality investment, retailer’s replenishment cycle time, and customer’s trade credit period. Assume that the product quality is normally distributed with known process mean, known standard deviation, and nominal-the-best characteristic. The declining exponential reduction of process mean and standard deviation is the function of quality investment. Numerical example and sensitivity analysis of some parameters will be provided for illustration.

In this study, we present the joint design for process mean, standard deviation, and tolerance. In the first phase, Taguchi’s quality loss and process adjustment cost are used. In the second phase, the quality investment and sampling inspection are addressed with quality protection under the specified capability value and consumer’s risk.

In 2007, Chen and Liu's (2007) model presented the optimum profit model between the producer and the purchaser for the supply chain system. However, their model with simple manufacturing cost did not consider the used cost of customers in their pure and mixed procurement policies. Hence, the modified Chen and Liu's model should be addressed for determining the optimum product and process parameters. In this study, the authors propose a modified Chen and Liu's model with economic selection of quality improvement. Both mean and standard deviation of process are assumed as a declining exponential function of the quality investment. Taguchi's symmetric quadratic quality loss function will be applied in evaluating the product quality. The optimum purchaser's order quantity, producer's wholesale price, improved process mean, improved process standard deviation and quality improvement will be jointly determined by maximizing the expected total profit between the producer and the purchaser.

Supply chain is the operation of the flow of goods and services, and includes all processes that transform raw materials into final products. It involves the active streamlining of a business's supply-side activities to maximize customer value and gain a competitive advantage in the marketplace. In recent years, many researchers have proposed the integrated supply chain models with production, inspection, maintenance and quality. Chuang and Wu (2019) developed an integrated model to determine the optimum supplier’s process mean and quality investment settings and retailer’s number of shipment, order quantity and maximum backorder quantity with maximization of total profit of supply chain system. In the present paper, Chuang and Wu’s model is modified with the constraint of the specified process capability index Cpm value, where the mean and standard deviation of process characteristic are assumed to be the declining exponential reduction function. The decision variables in this modified model include supplier’s parameters (i.e., quality investment and specification limits) and retailer’s parameters (i.e., order quantity, number of shipments and maximum backorder quantity). A numerical example is provided for illustration. Based on the sensitivity analysis, it may be seen that the supply chain’s total profit is positively influenced by the production rate, the demand rate, the purchasing cost, the selling price and the quality investment, and is negatively affected by the production cost, the specified process capability index, the target value, the maximum mean and both the minimum and the maximum standard deviations of process characteristic.

In many cases consumers cannot observe firms' investment in quality or safety, but have only beliefs on the average quality of the industry. In addition, the outcome of the collective investment game of the firms may be stochastic since firms cannot control perfectly the technology or external factors that may affect production. In such situations, when only consumers' subjective perceptions of the industry level of quality matters, the regulator may make information available to firms or subsidize their information acquisition. Under what conditions is it desirable to make information available? We show how firms' overall level of investment in quality depends upon the parameters of the quality accumulation process, the cost of investment and the number of firms in the industry. We also show the potentially negative effects on the total level of quality from providing information on consumers' actual valuation.

Investments in Quality, Collective Reputation and Information Acquisition

In this work, the authors proposes the modified Banu and Mondal’s model for obtaining the optimal process mean, quality investment, replenishment cycle time, and trade credit period. Assume that the product quality is normally distributed with unknown process mean and known standard deviation. The product is considered under the larger-the-better characteristic. The declining exponential reduction of process standard deviation is the function of quality investment. Numerical results show that (1) the production cost per item and the parameter of quality investment function for the process standard deviation have the effect on the process mean; (2) the production cost per item, the non-conforming cost per item, the parameter of quality investment function for the process standard deviation and the known process standard deviation have the major effect on the quality investment; (3) the production cost per item has the effect on the retailer’s replenishment cycle time; (4) the production cost per item has the major effect on the expected total profit including the manufacturer and the retailer per unit time.

Green investment choice in a duopoly market with quality competition