Journal of Quality Engineering and Management

Abstract Abstract: with the growing trend of services in the global economy, services have attracted a lot of attention. In developed markets such as the United States, for example, it is reported that more than 90% of GDP comes from the service industry. Numerous predictions indicate that the global economy will eventually be dominated by services. Services are difficult in terms of abstraction and qualitative and quantitative measurement, and the diversity of service sectors makes it difficult to develop a unique framework in the field of services. Service supply chain management, information management, processes, capacity, service performance, Money, and the forward and reverse flows of tangible goods from the primary supplier to the final customer, including the return of any tangible goods purchased. In order to achieve goals or ensure continuous improvement of quality and productivity in a supply chain including Service supply chain, process performance must be measured. In addition, the process cannot be managed if performance cannot be measured. Therefore, it is important to develop a framework to increase quality and productivity to measure service supply chain performance in order to assess changes and evaluate supply chain performance. Therefore, in this study, an innovative model to evaluate the performance of the service supply chain will be proposed to use it to measure the performance of the service supply chain. At the beginning of the research, after reviewing the literature and previous research, a service-performance evaluation model of serviceproduct type has been developed, which is one of the innovations of the present study and has been confirmed by fuzzy Delphi method by experts. After that, a performance measurement system using neural-fuzzy networks has been developed for this model, which is another innovation of the present study, and then to evaluate the production-service organizations of the home appliance industry in Iran, including the company. LG, Samsung, SNOWA, etc. This model has been used. Finally, the performance of these organizations is reviewed and future research and research suggestions are provided. 

Abstract Redundancy allocation is one of the important approaches to increase reliability used by system designers. In this approach, to improve the reliability of the system, components or subsets (components) are considered in the system, which in case of failure of sensitive components of the system are quickly replaced and intermittently stop the operation of the system.
It is prevented. In this research, the problem of redundancy allocation for a system with one component with defined constraints and considering the cost of lost production is presented. In this article, the goal is to minimize the total cost of the system, which considers two strategies: cold plug-in and inventory. Cold plug-in refers to a situation where the surplus component is not normally under load and the probability of failure before replacing the damaged component is independent of system performance time. The decision variable in this study is the values ​​of the number of redundancy components and the stock of spare components in stock. The difference between the two is that the plug-in component quickly and without delay replaces the defective component in the system, and its presence does not stop during the maintenance of the main component. The mathematical planning model has been developed to achieve the objectives of the problem as a nonlinear complex problem. An example for the system is also given and solved by GAMS optimization software. Finally, the results of solving the model are discussed.

Abstract The purpose of this article is to design a Non-Linear Mixed Integer Multilevel TwoObjective Mathematical Model to minimize the costs and maximize the reliability of blood supply chain. In this research, the reliability is measured according to the conditions and safety of transportation, temperature fluctuations, packaging standards, laboratory equipment and the demand. To test the model, the problem is modeled and solved by different dimensions using real data. In addition, the sensitivity analysis of the outputs is carried out to parameters changes. To solve the proposed mathematical model, Baron Solver of GAMS 24.9 is used. This model determines the product sent from blood center to hospital, the amount of production in blood center, the amount of blood donated from donors, the number of collection centers, the amount of product inventory in each center and hospital to minimize the costs and maximize the reliability. Given the fact that the first objective function is the maximization and the second one the minimization, there is a conflict between these two functions. That is, the costs will be minimized by maximizing the reliability. The model developed in this study determines the variables of decision so that by maximizing the reliability of supply chain, the costs will be well controlled and the waste and lack of blood minimized.  

Abstract Positioning the car in the parking lot is an important factor to make the parking lots smarter, as a result of which it is possible to steer the car, which will be a factor to increase the quality of service in the parking lot. Due to the closed environment of the parking lot, locating objects in it, including locating objects in indoor environments. The use of radio waves and related methods to locate indoors is one of the solutions presented in this field. In some other methods in this field, the location of the object in the indoor space is calculated only by using the equipment available in the environment (similar to location radars). Disadvantages of both methods include the need for additional equipment at high prices, extreme sensitivity to environmental conditions and existing noise. In this research, an attempt has been made to perform the location process by using monopole antennas and scattering matrix. For this purpose, first, the parking environment is simulated with a plate containing several monopole antennas, and using finite element-based software, the scatter matrix is ​​obtained for the absence and presence of the object in different environmental conditions that have been simulated. After calculating the scattering matrices, the required data are selected and each of these values ​​is assigned to an object position using a neural network. In the next phase, in exchange for placing the object in the new position, the corresponding scattering matrix is ​​obtained and the object is calculated by comparing it with the information collected in the previous step. This process is similar to the fingerprint algorithm, except that instead of using the values ​​of signal strength, the matrix is ​​scattered. The advantages of this method include no need to calibrate and accurately measure the position of the antennas, scalability and provide a new solution to reduce costs and increase the accuracy of calculating the position of the object.

Abstract Purpose: This research investigates the impact of technical, relational, and business components of e-business processes on value creation in the digital supply chain, emphasizing the role of information sharing using a neural network modeling approach. The main focus is on the mediating role of e-business capabilities in enhancing the impact of these components on supply chain competitive performance.
Methodology: This research is applied and descriptive-correlational. The research population consists of experts, managers, and employees of manufacturing companies operating in the capital's industrial park. Sampling was carried out using a non-probability, available, and contingent method, and data were collected through a standard questionnaire, the validity and reliability of which were confirmed by the indices AVE> 0.5, CR > 0.7, and α > 0.7. To validate the model and test the hypotheses, the variance-based structural equation modeling method in SmartPLS version 4.0 and the artificial neural network module in SPSS 29 were used.
Findings: After fitting the research model with the variance-based structural equation approach and the multilayer perceptron neural network, the research findings showed that in both approaches, the information sharing variable had the highest impact, and both approaches were able to predict the competitive performance of the digital supply chain. To evaluate the models fitted using the two approaches, the root mean square error was used. The root mean square error values for the multilayer perceptron neural network approach and the variance-based structural equation approach are 0.021 and 0.879, respectively. Therefore, the multilayer perceptron neural network method can accurately predict the competitive performance of the digital supply chain with much lower error and can serve as an optimal model.
Originality/Value: This study presents an integrated model to explain the role of e-business process capabilities in enhancing the competitive performance of the supply chain. The findings offer practical guidance for strategic decision-making and planning in manufacturing firms, particularly within dynamic business environments.

Abstract Health index is a tool to evaluate the functional condition of an equipment with the aim of improving its operational performance. In this research, the types of models available in the field of asset health index estimation along with their challenges and limitations were examined, based on a developed model. The proposed model was implemented in five different types of wind tunnel equipment fans, due to their significant maintenance and repair costs. The model proposed in this research in order to estimate the health index includes steps such as: 1) selecting the equipment and defining its class, 2) evaluating the effect of loading factors and location, 3) calculating the aging rate, 4) achieving the initial health index at age t and 5) evaluation of load effect, modifiers of health index and reliability, and calculation of current health index. Examining the values obtained from the fan health index, in the form of a graphic design, showed that it is possible to determine the speed of failure rate and the functional life of the equipment. The results of this research can be used in choosing the appropriate strategy for maintenance and repairs with the aim of improving the operational performance of other equipment.

Abstract In most cases, the alert received from a control chart does not indicate the actual time of the process change due to the delay between the actual change time and the time of receiving the alert from the control chart. As a result, it is necessary to examine the real time of change, which is referred to as the "point of change". By reviewing the literature on identifying real-time process changes, it can be concluded that most research in this field focuses on univariate processes and little research is devoted to multivariate processes. In addition, most research in the field of estimating change time in multivariate processes has focused on changes in the mean process vector, and only one research has been done on the covariance matrix. In this paper, a model based on artificial neural network is proposed to estimate the point of change in the covariance matrix of multivariate normal processes. The method presented in phase 2 is control diagrams and the type of change that occurred in the variance of qualitative characteristics is assumed to be the type of step changes. The performance of the proposed method in estimating the change point is evaluated based on two criteria of experimental distribution of estimates as well as the mean and standard deviation of the change point estimator for different step shifts in the variance of process variables in a simulation study. Finally, in order to further explain the proposed method, a numerical example is provided. The results show the proper performance of the proposed method in estimating the change point in the covariance matrix of multivariate normal processes.

Abstract Abstract: The chart is commonly used for monitoring processes where each produced items is characterized by its number of nonconformities. Recent studies have approved the advantages of using adaptive schemes rather than static one in monitoring such processes. In this paper, we develop a full adaptive model for control charts in which all design parameters (sample size, sampling interval, control limit) switch between two values, according to the most recent process information. The proposed scheme is investigated from the economic statistical viewpoint. The cost model is developed by using the Markov chain approach. Using numerical examples, we illustrate the performance of the proposed models and compare their efficiency with the other schemes. A sensitivity analysis is also carried out to investigate the effects of model parameters on the solution of the economic statistical design by using the design of experiments and regression analysis techniques.

Abstract Due to the lack of water in the current era and the support of governments in replacing worn-out water transmission lines, the polyethylene pipe industry has become one of the attractions of interest for investors. But one of the concerns of the community is the quality of these pipes. Therefore, in the present study, the effect of quality management system on the quality of polyethylene pipes is investigated using system dynamics. The case study of this research is one of the companies in Kerman Pipe Manufacturing Industry. In this article, using the opinion of industry elites, the necessary variables were identified and causal diagrams and state and flow diagrams were drawn using Wensim software. The results of the analysis showed that when implementing the quality management system, all organizational units should be coordinated with each other and the role of management in it is very key so that any management negligence causes poor product quality, reduced customer satisfaction and consequently reduced The amount of product sales and waste increases and eventually the organization is in a state of liquidation, bankruptcy and doubling.

Abstract Purpose: In recent years, environmental management, with a focus on environmental protection, has become one of the main priorities of governments and organizations. One key area in this regard is the design of supply chain networks with environmental approaches, which helps integrate production processes with sustainability goals.
Methodology: This study aims to investigate the feasibility of using palm tree pruning waste in the production of Medium-Density Fiberboard (MDF). As one of the richest plant resources in the country, the palm tree has high potential for application in the wood industry and for reducing environmental waste.
Findings: First, the biological and structural characteristics of palm trees are examined. Then, using this raw material, a supply chain network design model for MDF production is developed. The model's performance is evaluated and compared under different operational and environmental conditions.
Originality/Value: The findings indicate that using palm waste in MDF production not only helps reduce environmental waste but also contributes to the design of an efficient and green supply chain. Moreover, the study highlights a lack of prior research on this specific topic, underscoring the innovative aspect of the work.

Abstract Abstract Quality control tools are widely used in monitoring production processes. Quality monitoring of various production processes, from one-step processes to complex multi-stage processes in the first and second phases of control has been the focus of researchers. In recent decades, the use of quality monitoring tools in health care processes has also increased significantly. In contrast to the many efforts that have been made to monitor the quality of single-stage surgeries, researchers have not paid much attention to multi-stage surgeries. In this study, we have tried to enter the medical services space, while using the logistics model to mitigate the risk, monitor the two-stage process of thyroid cancer surgery for a set of 94 data and present our predictive model.

Abstract Nowadays, the integration of the supply chain has changed into an obligation due to the global competition and the necessity of quick response. Therefore, identifying and ranking the vulnerabilities of the integrated supply chain is of great importance and ignoring and even incomplete execution of this process may do irreparable damage to different sections of the chain. The present research aims to analyze the interaction among the vulnerabilities of the supply chain and setting their priorities. F.DEMATEL is used to examine the factors and F.ANP is used to rank the factors. The results show that the environmental and communication factors have the greatest effect on the vulnerability of the integrated supply chain. The production factor has the greatest rank among the factors to be affected. Also, the results showed that it is possible to reduce the vulnerability of the supply chain considerably and provide the causes of the improvement in production through creating communication networks over the chain and enhancing the indices such as improvement in dispersion and the weak harmony of the supply chain partners and transparency of the information over the supply chain. The results of the present research help the managers considerably to identify the difference of the vulnerable internal and external vulnerabilities of the supply chain and to identify the improvement priorities.

Abstract Purpose: The main goal of this study is to propose a realistic and practical model for the economic-statistical design of control charts in the presence of multiple independent assignable causes under the Burr Type XII shock model. The model aims to minimize the underestimation of the actual cost per unit time of the quality cycle.
Methodology: This research utilizes the Burr Type XII distribution as a shock model to develop the RED model for optimal design of control charts. The Lorenz and Van cost function is also extended to account for multiple assignable causes, and a numerical example is provided to demonstrate the solution approach.
Findings: Numerical results reveal that the proposed model outperforms existing models in accurately estimating the real cost per unit time of the quality cycle. Furthermore, an increase in the shock probability leads to a non-decreasing trend in the average cost, underscoring the importance of accounting for this probability in E(A) calculations.
Originality/Value: This is the first study to employ the Burr Type XII distribution as a shock model in the economic-statistical design of control charts. By extending existing cost models, the paper introduces a novel and realistic approach to designing control charts in the presence of multiple independent shocks.

Abstract Statistical quality control methods are the basis for measuring process performance and productivity. Control charts are one of the most widely used statistical control tools and play an important role in improving the quality of processes and products. The main purpose of their implementation is to identify the deviation (s) and take corrective measures to eliminate the root of the deviation. The technical applications of control charts are to determine the sample size, sampling frequency and control limits, which is called control chart design. In most nP chart design research, only one reason deviation is considered as the cause of the chart getting out of control, while several industry deviations can play a role. For this purpose, in this research, the design and statistical-economic development of nP control diagram, in order to reduce costs and average detection time and increase the power of the diagram by using gray values ​​and considering deviations for various reasons. In this regard, in order to achieve the optimal values ​​of these parameters, a multi-objective problem with three constraints has been defined, in which we have considered each possible combination of parameters as a decision unit. Then, using data envelopment analysis and ranking methods, the most efficient design for the decision maker is determined. In the continuation of the research, sensitivity analysis has been performed on some parameters of the model and the effect of these parameters on the optimal values ​​has been analyzed.

Abstract The purpose of this study is to identify and prioritize the demands and needs of customers in the cellulose and packaging industries and in the field of complete baby diaper products in Bushehr Polymer Industrial Group using Kano model and determining technical characteristics with design priority to improve product quality. In this study, according to the opinion of the interviewees and the use of verbal variables as well as the opinion of experts in weighting the importance of needs, the problem has become fuzzy and the Likert scale is used to rank customer needs and determine the current performance of the organization. has taken. Next, we convert fuzzy values ​​to non-fuzzy state and by determining the technical elements based on the relevant national and international standards, we have formed the quality house communication matrix in non-fuzzy form. Findings indicate that in the design of technical elements, the elements "dispersion of absorbent powder" and "width of the top and bottom cover" are the most important in designing the quality pattern of diaper product and can be an important factor in preventing physiological sensitivity of the child.

Abstract Today, the figures obtained from measurements are used under different headings and much more than in the past. Also, using the statistics obtained from the production process, one can comment on the quality of the products. Therefore, making the right decision in such cases depends on the quality of the measurement. Linearity factors also determine whether a measuring instrument works the same in all of its measurable ranges. The purpose of this study is to develop a fuzzy model to investigate the linearity relationship for a tool whose data are fuzzy triangular and trapezoidal. Finally, the data of automotive industry suppliers were used and the proposed method using coding in software. MATLAB software is solved. Then we compare the results with the classical mode. The results show that the fuzzy mode has more sensitivity and flexibility than the classical mode. 

Abstract In many statistical process control applications, the relationship between a response variable and one or more control variables is evaluated by a function called a profile. Profiles are divided into different types according to the nature of the response variable, such as linear and nonlinear profiles. In this research, a new control diagram based on the generalized linear test approach and fractional regression is presented to monitor multiple linear profiles with interactions in phase 2. The simulation results of the proposed control diagram show its much better performance than the control diagram based on the least squares error method.

Abstract The use of a Functional Block Diagram is usually one of the most commonly used methods to estimate the reliability of products. This method is not responsive in many missionoriented complex systems. Because at each stage of each mission, different sections and subsystems work and then stop at different times. This is precisely the problem of this research, which is a mission-centered submarine for rescue.  For this purpose, in this paper, a method for calculating the reliability of this submarine, which has four 9-step subsystems, has been designed and presented. At first, the functions of each stage are extracted from the subsystems (electricity, secondary, radio-electronics and navigation) during the meetings with experts, then a Functional Block Diagram is drawn for each step. In the following, the potential failure states for each function are determined in the form of a Failure Mode and Effect Analysis tool. Also, for risk analysis, the severity-probability matrix and the average RPN number have been used and good suggestions for improving the design presented. Further, calculating the failure rate for each step by Kim formula, we calculated the reliability of each stage of each subsystem. Then, the reliability of each subsystem is computed. Finally, in order to calculate the reliability of the entire submarine, first, the reliability of each step is obtained by multiplying the reliability values of each of the four subsystems in each step, eventually multiplied by the successive steps. According to the calculations, the total submarine reliability in the design stage is approximately 0.6, which is considered by the experts to be reasonable. 
       

Abstract Control charts are one of the most important tools for evaluating process performance and monitoring. In the classic design of a control chart, the cost of quality depends on whether the quality characteristic is inside or outside the control. The use of loss function in the design of control charts, as an estimator of the cost of production of defective products, contributes to a more comprehensive assessment and better management decisions. Therefore, in this article, the combination of loss function and economic statistical design of control charts. The loss functions used so far in this field have been symmetric functions, but in many cases overestimating or underestimating the ideal value for a quality characteristic does not produce the same losses. Therefore, for the first time in the literature on the design of control charts, this paper uses the asymmetric loss function of Linux. Using a practical example, the performance of quadratic, linear, exponential and linear loss functions are compared. The result of these comparisons showed that the Linex loss function has the lowest cost in statistical-economic design of the control chart compared to other loss functions.

Abstract In this article, three monitoring approaches using cumulative sum (CUSUM) control charts in phase two for zero inflated poisson-based processes are presented. The first approach is based on the zero inflated poisson distribution, the second on, a proportional hazard regression model, and the third integrates a proportional hazard (PH) regression model and a frailty model to consider both measurable and unmeasurable covariates. The performance of all three control charts was evaluated separately and simultaneously by applying shifts to both parameters of the zero inflated poisson distribution. Extensive simulation studies were conducted to evaluate the performance of these monitoring methods in terms of the average run length (ARL) of the control charts. The proposed cumulative sum control chart with simultaneous consideration of measurable and unmeasurable variables showed superior performance. Finally, a real case study in a label printing factory has been provided to show the effectiveness of the proposed control chart.

Abstract Cumulative cumulative control charts are presented in most quality control books regardless of the statistical formulas behind the V-mask. In this paper, after introducing and reviewing the cumulative sum control chart, the decision rule is examined and its relationship with sequence probability ratio tests. Many references and quality control books consider the V mask method to be equivalent to a cumulative test with inverted data. Although the two methods have many similarities, their decision-making rules are not the same. In order to further clarify, in this article, the similarities and differences between the two approaches are examined and compared.

Abstract Abstract: Data envelopment analysis (DEA) is a nonparametric technique based on mathematical programming to evaluate performance of homogenous decision-making units (DMUs). Many DMUs have network structure where the output of one stage is the input to the next stage. A supply chain, which consists of several members such as supplier, manufacturer and customer, has multi-stage process. In this paper we, for the first time, introduce network DEA approaches to obtain the most productivity in a supply chain as a multi-stage DMU. Our models, by looking inside the internal structure of supply chain, consider the intermediate links between the stages. This perspective proposes managerial implications to improve the overall performance of a supply chain and the productivity of each member.

Abstract One of the most widely used fields in production planning is related to determining the amount and sequence of production. The development of production batch sizing models as one of the branches of production planning science has always had a special place among researchers. Determining the size of a production batch means breaking a batch into a number of subcategories, each subcategory of which is completed and transferred to the next machine to continue the operation so that the operations can overlap. The purpose of this study is to determine the size of the production batch by considering the distribution costs and using single loading devices such as pallets and containers. Problems associated with decisions regarding the size of the production batch and the loading of products in the machines were also modeled in this study, in which constraints such as weight constraints, volume constraints, or the amount of load loaded in the boxes were also considered. Finally, MIP models based on the "branching and cutting" method on an optimization package were investigated and the results of this method showed that these methods can be implemented in many different practical situations.

Abstract Purpose: This paper presents an innovative model for the simultaneous optimization of redundancy allocation and condition-based maintenance in series-parallel load-sharing systems. The primary objective of the model is to determine the optimal level of redundancy so that costs are minimized while system reliability constraints are met.
Methodology: In this research, stochastic dependencies between system components are considered using the proportional hazards model and tempered failure rates to assess reliability accurately. Additionally, transition probability matrices are used to determine the optimal maintenance limits for each subsystem, and periodic inspections are performed. The proposed model is solved using MATLAB, and its performance is evaluated under four different scenarios: 1) a baseline model without redundancy or stochastic dependencies, 2) redundancy allocation without stochastic dependencies, 3) stochastic dependencies without redundancy, and 4) the proposed model.
Findings: The results show that the proposed model achieves an optimal balance between cost and reliability, reducing both failure and maintenance costs. Compared to the various scenarios, the proposed model demonstrates superior performance in optimizing costs and enhancing reliability. The findings also emphasize the importance of simultaneously considering stochastic dependencies and redundancy allocation to improve system performance.
Originality/Value: This research introduces a novel approach by simultaneously considering stochastic dependencies and redundancy allocation in series-parallel load-sharing systems. The proposed model significantly improves system performance and reduces failure and maintenance costs. It underscores the importance of integrating these two factors in optimizing complex engineering systems.

Abstract Reliability and safety of any system is the most important quality characteristic of a system. This qualitative characteristic is of special importance in systems whose operation is under various stresses such as: high temperature, high speed, high pressure, etc. A noteworthy point that is often not taken into account in calculating the reliability and safety of systems is the existence of interdependencies between subsystems with each other, which causes different failures in the system. One of the most important of these failures is common cause failure. In this type of failure, several subsystems or all subsystems fail simultaneously or in a short period of time due to a common cause. Failure to consider common cause failures in calculating the reliability of systems leads to an optimistic estimate of the reliability rate of the system and consequently leads to overconfidence in the system. In this paper, first using the product structure separation techniques (PBS), performance flow block diagram (FFBD) to identify and then using the reliability block diagram (RBD) to calculate and allocate the output reliability of a dynamic positioning system that includes hydraulic thrusters And electric for the movements of roll, suge, sui, yaw and hyo, will be discussed.In calculating the reliability of the system with the help of cascade failure probability rules and with the help of beta factor model and PDS method, common cause failures of different subsystems were considered.