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Due to the importance of quality to customers, this study considers criteria of quality and profit and optimizes both in a multi-echelon cold chain of perishable agricultural products whose quality immediately begins to deteriorate after harvest. The two objectives of the proposed cold chain are to maximize profit and quality. Since postharvest quality loss in the supply chain depends on various decisions and factors, in addition to strategic decisions, the authors consider the temperature setting in refrigerated facilities and transportation vehicles due to the unfixed shelf life of the products which is related to the temperature found by Arrhenius formula.

The authors use bi-objective mixed-integer nonlinear programming to design a four-echelon supply chain. The authors integrate the supply chain echelons to detect the sources and factors of quality loss. The four echelons include supply, processing, storage and customer. The decisions, including facility location, assigning nodes of each echelon to corresponding nodes from the adjacent echelon, allocation of vehicles to transport the products from farms to wholesalers, processing selection, and temperature setting in refrigerated facilities, are made in an integrated way. Model verification and validation in the case study are done based on three perishable herbal plants.

The model obtains a 29% profit against a total cost of 71 and 93% of original quality of the crops is maintained, indicating a 7% quality loss. The final quality of 93% is the result of making a US$6m investment in the supply chain, including the procurement of high-quality raw materials; facility establishment; high-speed, high-capacity vehicles; location assignment; processing selection and refrigeration equipment in the storage and transportation systems, helping to maximize both the final quality of the products and the total profit.

The proposed supply chain model should help managers with modeling decisions, especially when it comes to cold chains for agricultural products. The model yields these results – optimal location-allocation decisions for the facilities to minimize distances between the network nodes, which save time and maintain the majority of the products’ original quality; choosing the most appropriate processing method, which reduces the perishability rate; providing high-capacity, high-speed vehicles in the logistics system, which minimizes transportation costs and maximizes the quality; and setting the right temperature in the refrigerated facilities, which mitigates the postharvest decay reaction rate of the products.

Comparison of the results of the present research with those of the traditional chain (obtained through experts) shows that since the designed chain increases the profit as well as the final quality, it has benefits for the main chain stakeholders, which are customers of agricultural products. This study model is expected to have a positive impact on the environment by placing strong emphasis on quality and preventing excessive waste generation and air pollution by imposing a financial penalty on extra demand production.

Since profit and quality of the final product are two important factors in all cultures and communities, the proposed supply chain model can be used in any food industry around the world. Applying the proposed model induces growth in local industries and promotes the culture of prioritizing quality in societies.

To the best of the authors’ knowledge, this is the first research on a bi-objective four-echelon (supply, processing, storage and customer) postharvest supply chain for agricultural products including that integrates transportation logistics and considers the deterioration rate of products as a time-dependent variable at different levels of decision-making.

Measuring quality costs has been emphasized as an important part of quality improvement since the early 1950s. A chapter on quality costs seems to be almost compulsory in every book pertaining to total quality management, business process improvement, and similar topics. There is no doubt that measuring quality costs is useful in order to direct improvement efforts; the problem is that the concept is not as valid today as it used to be. While customer requirements and production systems have changed considerably during the last decades, quality cost measurement is advocated in nearly the same way as it was 40 years ago. This work presents a new customer and process focused poor quality cost model that enables the provider of a product or service to focus on elements that really matter to his customers. The input to the model is customer requirements and the output is expected poor quality costs estimated through the Taguchi loss function. Quality function deployment is used to translate the voice of the customer to key process parameters, that is process parameters having a direct influence on the fulfilment of customer requirements. The quality function deployment matrix is also used to estimate intangible costs. Traditional cost categories have been altered, and the expected loss for each cost category is estimated based on actual process performance and stepwise quadratic loss functions with multiple intervals. The intended use of the model is as a top management decision‐making tool able to link quality improvement to customer satisfaction and loyalty.

Quality improvement decisions are the catalyst for substantial technological improvements being made in the manufacturing sector. The new technology, however, has developed faster than techniques for evaluating capital investments in such improvements. This is largely because the benefits of quality improvement technology are difficult to quantify. The Taguchi loss function is incorporated into a net present value capital budgeting technique to provide an estimate of these benefits. Describes the loss function in relation to key quality costs: appraisal and prevention costs, and internal and external failure costs. External failure cost savings are generated by reducing variability in the manufacturing process. These savings are then compared with the cost of the quality improving technology. Results indicate that these savings can be substantial, depending on the achieved reduction in the process variability, the cost of capital, and on the estimate of the cost of processing a customer’s return of the product.

The purpose of this paper is to explore the system requirements model. According to the concept of loss costs of Type I and Type II errors, it can define the optimal decision line, and reduce overall loss costs. Moreover, it can decrease the probability of Type I and Type II error by the systems thinking, and it can effectively reduce overall loss costs.

The paper proposed a system demand model and constructed a decision‐making system thinking model as well as a decision‐making performance management model using the principle of system demand. Types of decision‐making errors were analyzed to set judgments on the error risk and establish a model of improvement evaluation key factors, in order to reduce decision‐making error risk and enhance decision quality. It also constructed the improved decision‐making to assess the key factors, to reduce the risk of making errors in order to improve the quality of decision‐making.

Optimistic decision‐makers (risk takers) tend to make Type II errors, whereas pessimistic decision makers (conservatives) tend to make Type I errors. Financial depressions are the time for optimistic decision makers (risk takers) and boom periods are the time for pessimistic decision makers (conservatives).

The concept of the loss cost of two decision‐making errors and related cost function models were proposed. Decision makers could make decisions with a more stable model, taking into consideration false alarms and the cost function of errors in order to determine the position of the decision‐making line. It could effectively reduce decision‐making error costs and increase the precision of decision‐making.

The purpose of this paper is to develop an extensive economic production quantity (EPQ) model on the basis of previous research. Considering an imperfect three‐state production process, this paper makes contributions to an integrated model combining conceptions of quality loss and design of control chart based on EPQ model. The objective is to minimize the total production cost with the determination of EPQ and design parameters of control chart subjected to quality loss and other process costs.

In this paper, imperfect process is defined as a three‐state process, and the quality cost corresponding to each state contributes to the eventual total expected cost formulation. Control chart is used to monitor the shift from the target value within whole process and its control limits are set to be related to the quality cost.

The proposed integrated model conforms more closely to the real situation of production process considering the process shift as a random variable.

Numerical computation and sensitivity analysis through a case study are presented to demonstrate the applications of the model.

Few research efforts investigate an integrated model considering EPQ, control chart and quality loss simultaneously. In particular, compared with the former researches, the process shift, due to which the quality cost incurs, is considered as a random variable in this paper.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

The purpose of this paper is to provide an optimal tolerance allocation model for assemblies with consideration of the manufacturing cost, the quality loss, the design reliability index with various distributions to enhance existing models. Results of two case studies are presented.

The paper develops a model with consideration of the manufacturing cost, the Taguchi's asymmetric quadratic quality loss and the design reliability index for the optimal tolerance allocation of assemblies. The dimensional variables in normal distributions are initially used as testing and compared with the data from the prior researches. Then, the dimensional variables in lognormal distributions with the mean shift and the correlation are applied and investigated.

The results obtained based on a lognormal distribution and a normal distribution of the dimension are similar, but the tolerance with a lognormal distribution is little smaller than that with a normal distribution. The result of the reliability with the lognormal distribution obtained by the Monte‐Carlo is higher than that with a normal distribution. This paper shows that effects of the mean shift, the correlation coefficient and the replacement cost on the cost are significant and designers should pay attention to them during the tolerance optimization. The optimum tolerances of components of a compressor are recommended.

The model is limited to the dimensions of components with the normal distribution and lognormal distributions. The implication should be enhanced with more data of dimension distributions and cost of assembly components.

Two case studies are presented. One is an assembly of two pieces and another is a compressor with many components.

This model provides an optimal tolerance allocation method for assemblies with the lowest manufacturing cost, the minimum quality loss, and the required reliability index for the normal distribution and lognormal distribution.

Making decisions on preventive maintenance (PM) policy and buffer sizing, as is often studied, may not result in overall optimization. The purpose of this paper is to propose a joint model that integrates PM and buffer sizing with consideration of quality loss for a degenerating system, which aims to minimize the average operation cost for a finite horizon. The opportunistic maintenance (OM) policy which could increase the output and decrease the cost of the system is also explored.

A joint PM and buffer size model considering quality loss is proposed. In this model, the time-based PM and the condition-based PM are taken on the upstream and the downstream machine, respectively. Further, the OM policy based on the theory of constraints (TOC) is also considered. An iterative search algorithm with Monte Carlo is developed to solve the non-linear model. A case study is conducted to illustrate the performance of the proposed PM policies.

The superiority of the proposed integrated policies compared with the separate PM policy is demonstrated. Effects of the policies are testified. The advantages of the proposed TOC-based OM policy is highlighted in terms of low-cost and high-output.

Few studies have been carried out to integrate decisions on PM and buffer size when taking the quality loss into consideration for degenerating systems. Most PM models treat machines equally ignoring the various roles of them. A more comprehensive and integrated model based on TOC is proposed, accompanied by an iterative search algorithm with Monte Carlo for solving it. An OM policy to further improve the performance of system is also presented.

The purpose of this paper is to apply the statistical tolerancing technique to analyze the dual responses of APFA arm height deviation with respect to next stage of disk assembly process and simultaneously optimize and allocate the required tolerance of the responses onto its components at minimum cost of manufacturing and the quality loss.

The relationships between the dual responses of APFA heights and the geometric dimensions and tolerances of APFA components, and orientation of the assembled part with respect to disk assembly were first defined. The effects of the APFA orientation, and the component tolerances on the distributions and variations of the responses were derived and investigated in terms of resultant product/process performance, quality loss, and the cost of assembly. The tolerance cost-based objective function is then formulated as the combined manufacturing/assembly cost and the quality loss. Direct search method was used to find the best feasible tolerance solutions satisfying the required product performance at minimum cost.

The constructed relationship or transfer functions of the dual responses were probabilistic depending on the orientation of part with respect to the next assembly process. The Monte Carlo simulation is empirically suitable for the computation of the conditional distributions of the responses against the first-order linear approximation of component variances. The proposed solution of tolerance control plan increases the product performances, C _pm , from 0.6 to be at least 1. The proposed tolerance allocation plans will reduce the amount of rework currently as high as 5 percent to at most 0.01 percent with minimally increased assembly cost.

This proposed methodology to design and allocate component tolerances is suitable and applicable to the APFA assembly process. The derived assembly functions of probabilistic type relating the responses to the process and component characteristics can represent the actual dynamic of assembled part better than a traditional single deterministic function developed under static concept. This presented methodology can be applied to other assembly cases where quality characteristic depends on the part dynamic.

This research simultaneously optimized the dual APFA height deviation responses with minimum cost of tolerance and quality loss using two different conditional distributions and transfer functions of the resultant deviations generated from dynamic of APFA with respect to disk.

The purpose of this paper is to study the interaction of economics of production with process quality, when multiple key quality characteristics are present. Specifically, the paper aims to analyse the possibility of investing in a production process to reduce its variances and the impact on a multivariate quality loss function.

A bivariate inventory‐planning model is developed, in which the optimal investment for reducing process variances and the optimal lot size are jointly determined. A case study with industrial data is presented to illustrate the possible solution procedures and the potential advantages of the proposed model.

It is found that by using the previous approaches to analyse the interaction between the economics of production and process quality, a company will underestimate the cost of quality, especially the expected external failure cost (quality loss), and ultimately invest less into the prevention activities to improve the process.

The proposed model can help managers to compare different production processes and also guide the managers towards better choices for process improvement. To the best of our knowledge, this paper is the first to integrate the economic production quantity (EPQ) problem with the process quality consideration for products with multiple quality characteristics.