Journal of Quality Engineering and Management

Abstract  In this research, in order to allocate reliability to the subsystems of a cube satellite in the conceptual design phase-with the objective of achieving a 73% reliability-we have developed. The target feasibility method as our research fundamental procedure. In no research study in the area of reliability allocation conducted so far have we found a method which has developed the technology factor or considered the relationships between suppliers and the issue of reliability allocation.  Therefore, the current research study focuses on the exact calculations of the technology factor. In order to estimate the letter factor, a concept designated as technology preparation assessment and another one as level of suppliers technological capacities are being discussed. In this regard, a model has been presented which is a synthesis of the letter two concepts.  The obtained results indicate that the reliability allocation through by adopting this methodology is a great help toward identifying critical subsystems and heir improvement at the design stage . 
 
  

Abstract Abstract: One of the basic issues in Ultrahigh-dimensional data analysis is fitting the optimal model and estimating its unknown quality parameters in such a way that it can correctly interpret the structure of the investigated data. In this article, we compare two non-local hyper priors: hyper product moment and hyper product inverse moment priors in determining the optimal model at the same time as estimating the parameters in variable selection using Bayesian Shrinkage in ultrahigh-dimensional generalized linear models. In order to compute the posterior probabilities, the Laplace approximation method was used, and to select the optimal model in the model space of posterior probabilities, Simplified shotgun stochastic search algorithm with screening (S5) for GLMs was used along with screening. Finally, through the study of simulation and real data analysis, the effectiveness of the above Bayesian Shrinkage methods has been evaluated with the ISIS-LASSO and ISIS-SCAD method. The advantage of the model is shown.

Abstract Given the dynamics of the competitive market and changing customer needs, quality plays a key role in meeting customer requirements, and delivering high-quality products is essential for the survival and growth of manufacturing organizations. In this paper, process planning and advanced scheduling are considered, where scheduling is performed based on the orders received by the system. The production system has flexible machines and operators, and orders must be scheduled according to the current status of the machines and available operators so that, by assigning appropriate machines and operators, quality-related costs are minimized. To achieve the desired quality, the Taguchi quality loss function is developed by linking the quality characteristics of operations to the assigned operator and machine. Since the orders have precedence-required operations and, in one aspect, are similar to multiple traveling salesman problems, providing an exact mathematical model for these problems is often complex. Moreover, when dealing with flexible machines and operators, this complexity increases. In most works related to integrated process planning and scheduling, the mathematical models presented (because they define, for operations, a set of binary ordered pairs indicating precedence or non-precedence between operations) lack efficiency for solving with optimization software. Therefore, in this study, the problem is modeled with a new approach, and the problem is solved using GAMS software. For larger-scale problems, given the existing complexities, a genetic algorithm is used.

Abstract Precision machining of complex industrial parts requires special machines, one of the best of which is the CNC milling machine. Malfunction of machines can cause the production line to stop, while repairs of these machines when they break down without prior possibility and preparation of spare parts with Paying attention to the need to import them is time consuming and costly. In this article, the failure of the device on an annual basis is extracted from the failures recorded in the repair and control sheets of the device, which have been prepared and maintained by the personnel. Then, using Markov chain, to estimate the reliability, maintenance and repair and availability of four-axis CNC milling machine in 2011 was used. The results of implementation in 2011-2018, were compared with the indicators of 2011. The evaluation results show that the availability of the device has increased from 96.3% in 2011 to 99.28% in 2018.

Abstract Purpose: The existing supervisory system in many countries, especially in Iran, mainly uses general and integrated models in which the structural, mission, and operational differences between banks and financial institutions have not been properly taken into account. This uniform approach has led to reduced risk identification accuracy, a lack of adaptation to each financial institution's specific needs, and reduced effectiveness of supervisory measures. The main objective of the present study is to design a cause-and-effect model to improve the quality of supervision based on the mission type of banks and financial institutions, using a mixed approach (Meta-Synthesis-Fuzzy DEMATEL).
Methodology: The present research was conducted using a mixed method (Qualitative-quantitative) and exploratory approach. Then, using a survey, 25 banking industry experts with at least 10 years of executive experience in finance and banking and master's and doctoral degrees were recruited to examine the validity and reliability of the proposed model. Also, paired-comparison questionnaires were distributed to the experts, and the intensity of impact and effectiveness between the research dimensions were examined using the fuzzy multi-criteria decision-making technique, DEMATEL.
Findings: The research findings show that using the meta-synthesis approach, 9 dimensions and 40 components were selected. They were selected from the dimensions. Also, the results of fuzzy DEMATL analysis show that the most influential dimension of the present study regarding the supervision of banks and credit institutions based on the value of (D+R) is the legal and regulatory supervision dimension among the independent dimensions and the cause with the highest value and the most influential variable. Also, among the dependent and affected dimensions based on the lowest value (D-R), the environmental and social performance dimension was recognized as the most influential variable in improving the quality of supervision of banks and credit institutions.
Originality/Value: Using a meta-combination approach in the analysis of previous research, which can provide new horizons for designing effective models based on improving the quality of banking system supervision. Therefore, the present study is of high scientific and practical importance for advancing methodology, responding to the current needs of the country's financial system, and improving the effectiveness of supervision of monetary institutions. This research has also led to recognition of the intensity of the relationships among the dimensions of quality improvement in banking industry supervision and has drawn more attention to these components.

Abstract Control charts are one of the most effective tools used in quality control to monitor various quality characteristics in a process with the aim to improve quality of the product. Usually, in Shewhart control charts, the normality assumption met for the data, but sometimes there is lack of information regarding the statistical distribution of the observations. For this reason, non-parametric control charts are used in this situation. In this research, non-parametric sign charts are introduced to deal with the lack of information regarding observations’ statistical distribution. Nonparametric Generalized Weighted Moving Average Sign Control Chart (NS GWMA) designed using statistical design and average run length (ARL) and its statistical performance was studied. But statistical design is not enough to ensure the performance of a control chart, so in the next steps, economic design (ED) and economic-statistical design (ESD) were applied using cost model of Lorenzen and Vance, in order to optimize both statistical and economical characteristics of the control chart.

Abstract Workforce health is one of the most important fundamental prerequisites for productivity and plays a crucial role in achieving sustainable socio-economic development. Therefore, preventing accidents in work environments, as a public necessity, has always attracted the attention of specialists. Given the key role of unsafe behaviors in the occurrence of accidents, some researchers have focused on monitoring unsafe behaviors using statistical process control (SPC) charts. However, human behavior is influenced by various factors such as environmental conditions and individual characteristics. These factors increase process variance and are often referred to as common causes of variation. In such processes, identifying the most influential factors contributing to variance can significantly improve process performance. Nevertheless, such sources of variability have not been adequately considered in previous related studies. The main objective of this paper is to model and monitor different levels contributing to variance in individuals’ unsafe behaviors. The proposed model was implemented in a manufacturing company. After determining the variance associated with each factor, the variability of each component was analyzed separately. The proposed approach, which demonstrates a high capability in reducing unsafe behaviors, can be used as an effective tool for safety management in organizations.

Abstract According to previous research reviews, most studies on the evaluation of sustainable supply chain capabilities are limited to statistical variables. Based on previous research reviews in this paper we prepare a list of capabilities of sustainable supply chain management, in cooperation with an expert team we short listed this capability. Data collection tools, questionnaires and interviews, and data analysis methods including fuzzy Delphi method, fuzzy expert system were used. Also, we applied some data from formal sites in data gathering steps. In order to clustering the variables, in next step we defined limitations of decision variables. Then by utilization of quantitative and fuzzy technique we presented an analytical approach. Simulink tool was used to simulate and integrate the designed fuzzy systems. Levels for sustainable supply chain capabilities are measured and at the end results delivered. The results shows that there are four competencies with high priority, including competitiveness, operational, technology and resilience. 
 

Abstract In today’s competitive markets, quality is a fundamental pillar for organizational survival. One important dimension of quality is reliability; therefore, improving reliability is considered essential. To enhance system reliability, approaches such as adding redundant and standby components can be employed, and an optimal design can be developed using available system data. In some cases, system data lack sufficient accuracy, and fuzzy logic is used to enable more precise analysis. In many practical applications, in addition to improving system reliability, other objectives such as minimizing system weight, volume, or cost are also considered in optimal design. Since these objectives are conflicting, the system design problem becomes a multi-objective optimization problem. In this paper, a mathematical model is proposed with the objectives of maximizing reliability and minimizing cost for multi-stage series–parallel, standby, and k-out-of-n systems, subject to system volume and weight constraints. The decision variables in this problem are the optimal number of redundant components allocated to each stage. Finally, a numerical example is presented in which system component reliability over the life cycle is considered, and the cost, volume, weight, and failure rate parameters of components are modeled as triangular and trapezoidal fuzzy numbers. The resulting model is solved using the metaheuristic simulated annealing algorithm, and a set of Pareto-efficient solutions is obtained. The results indicate that standby systems achieve higher reliability compared to the other system configurations.

Abstract Dependency in systems is one of the problems that reliability faces. Dependence increases the probability of failure and can affect the performance of a system; Therefore, it is very important to study and understand the consequences when designing, operating and maintaining the system. Regardless of the dependency, reliability is optimistically estimated and the system fails sooner than expected. In the rail industry, subsystems show a high level of dependence due to their high dynamics and complexity. Failure of any of the subsystems can affect the performance of the entire network and sometimes have irreparable consequences. Identifying dependencies and dependent failures based on the block diagram of reliability and the structure of the rail system can affect the more accurate prediction of reliability and reduce the subsequent consequences. This paper presents a new mathematical model for predicting reliability in the rail industry by considering common cause failure. Various methods have been introduced to estimate the common cause failure coefficient. The results show an increase in accuracy in predicting reliability.

Abstract The economic design of control charts depends on the process shock model distribution and due to difficulties from both theoretical and practical aspects. This paper pursues to develop the economic design of X ̅ control chart for monitoring continuous flow processes under Bur XII shock model. The Burr XII distribution is flexible and its hazard rate can take many forms like as fixed, increasing, decreasing, and single mode or even U-shaped. Continuous flow processes are used in batch processes such as those meet to filtering, chemical processing and etc. A sensitivity analysis is executed and numeric examples are given to illustrate the effects of changing the parameters of the shock model distribution on the optimum values of the economic design models as discussed in this paper. In addition, we use uniform and non-uniform sampling schema and comparison between them. This comparison show that for non-constant hazard rate, non-uniform sampling scheme is better.

Abstract This research focuses on improving productivity in the home appliance industry through ‎the Kaizen approach. In order to respond to competitive challenges, various ‎organizations are pursuing the implementation of lean production today. The use of lean ‎techniques in manufacturing and even service industries can be seen in writings and ‎articles. From a functional and operational point of view, lean production includes the ‎implementation of a set of tools and techniques that try to reduce waste in the company ‎and the value chain.‎
The main issue includes identifying and reducing waste and mods related to processes in ‎the production system. The main questions in this research focus on the definition and ‎corrective measures to improve productivity and achieve the goals of kaizen in ‎eliminating or reducing waste such as excess production, transportation, etc. Various ‎methods such as case studies and field analyze are used as well as value flow mapping ‎to identify trends and implement corrective measures. ‎

Abstract In this paper, an economic statistical design of control chart is presented for individual qualitative characteristics that have an exponential distribution. For this purpose, we first convert the exponential distribution of individual observations to the normal distribution using the approximation proposed by Nelson. Then, using Costa and Rahim economic model, we have obtained an economic statistical design for the transformed observations. In order to optimize the model parameters that are calculated based on the design parameters, we have obtained the optimal values of the design parameters using the honey bee algorithm. Finally, the results of the economic statistical design of the control chart are compared with the economic design and the positive performance of the economic statistical design compared to the economic design is shown. Also, by simulated data from the exponential distribution, the efficiency and performance of the introduced control chart in comparison with the case that the distribution of the qualitative characteristics is normal, has been investigated. 
 
 

Abstract Purpose: This study aims to optimize warranty periods for complex products by examining the role of warranties in customer retention and cost management. The proposed model uses a mixed statistical distribution to simultaneously model minor and major failures, seeking to minimize the product's life-cycle cost while maintaining customer satisfaction.
Methodology: The mathematical model defines life-cycle costs, establishes an objective function to minimize total costs, and determines the optimal warranty period. A numerical example and sensitivity analysis are used for validation, and the model is solved using Maple 2024.
Findings: The optimal warranty period was identified as 3.5-4.5 time units, and the model achieved a 23% cost reduction compared to conventional methods. Sensitivity analysis showed that changes in failure probability and failure rate directly affect the optimal warranty length.
Originality/Value: Using a mixed statistical distribution to model different failure types simultaneously offers an innovative, more realistic approach. This model provides a practical tool for adjusting warranty policies and reducing life-cycle costs, with potential for further development by incorporating dependent failures and real-world data.

Abstract Six Sigma methodology has been established as a process-oriented approach with strong emphasis on results and effectiveness, aiming to improve the quality of products, services, and processes. In our country, in line with global developments, industrial and service organizations have rapidly become aware of the high potential of this methodology through information exchange with international organizations and industrial centers, and have been encouraged to implement it. The main objective of this methodology is the implementation of a measurement-based strategy focused on process improvement and variability reduction. Sarcheshmeh Copper Complex, as one of the major and foundational industries of the country, has defined numerous Six Sigma projects to improve production processes, reduce waste, and increase productivity, achieving extensive benefits such as improvement of production processes, reduction of losses and scrap, and significant financial savings. Since one of the main concerns of managers at the Sarcheshmeh Copper Complex is reducing the amount of cold material produced in the smelting plant and consequently preventing the waste of a considerable portion of incurred costs a Six Sigma project was therefore considered to address this issue. In this research, simulation tools were also utilized to develop an integrated model for reducing cold material in the smelting process. For this purpose, a discrete-event system simulation approach was employed. The design and implementation of the simulation model were carried out using Arena software and Visual Basic.

Abstract  Stroke and heart attack are the most important causes of mortality in the world. Identifying communities at risk for stroke and heart attack is an important step in improving the care systems of these patients, strokes and heart attacks are important to the emergency department, The aim of this study is to improve emergency department performance and improve the quality of emergency and hospitals resource allocation. Therefore, the spatial autocorrelation of stroke and heart attack was investigated using spatial statistics. Distribution of these two types of complications in different parts of Tehran was identified by using hot spots analysis and Moran's local autocorrelation index. Also, considering the spatial autocorrelation, to investigate the factors affecting the occurrence of this event, the relationship between the incidence rate of stroke and heart attack with AQI air pollution index And the level of development in different region of Tehran was investigated using Spearman correlation coefficient. 
 

Abstract Allocation is one of the important activities in the reliability process, if it is not done correctly, it will not be possible to achieve the reliability goals for the whole system. To allocate the reliability, methods have been presented that are not free of defects. In this research, five factors of complexity, new technology, operation time, environment and resilience of subsystems are considered and the goal is to consider a new approach in allocating reliability to the goal feasibility method so that different parts and subsystems can be examined and measured based on the desired reliability and resilience. The obtained results are investigated and measured by Foo method with the mentioned five factors and finally validated.
The following items are suggested to researchers in the field of reliability and complex systems:
• Consider the development of each of the four factors in Fu's method in their research.
• Considering this research as a basis, the presented method should be developed in terms of management as well as providing other combined solutions.
• After the development of other factors of Foo's method, software can be developed to assign reliability.
• Designing a mathematical model for assigning reliability so that it includes more factors.
• Finding a quantitative relationship between the cost of a subsystem and its reliability so that the system cost is minimized under the reliability constraint or the system reliability is maximized under the cost constraint.

Abstract Purpose: In today's competitive environment, selecting suitable suppliers plays a pivotal role in enhancing the efficiency and sustainability of supply chains. The LARG supply chain model, as a comprehensive approach, integrates various dimensions in supplier management. However, despite its critical influence on interaction success and supplier selection, the cultural dimension has received limited attention. This study aims to evaluate suppliers within the LARG framework while incorporating the cultural dimension to improve supply chain performance and achieve sustainable competitive advantage.
Methodology: This research developed a data-driven and forward-looking decision-making model for supplier evaluation and selection. First, key criteria were identified through literature review and expert consultation, and weighted using the Fuzzy Best–Worst Method (FBWM). Then, suppliers' efficiency was assessed and ranked using Fuzzy Data Envelopment Analysis (FDEA). Subsequently, the Random Forest algorithm was employed to predict future supplier performance, yielding highly accurate results.
Findings: The initial results highlighted the significance of sub-criteria such as greenhouse gas emission reduction, risk management, quality, and delivery speed in supplier evaluation. In the second phase, supplier efficiency was analyzed under various α-cuts and classified into three performance groups: high, medium, and low. The Random Forest model demonstrated high accuracy in forecasting supplier performance. Moreover, the paired t-test results revealed that incorporating the cultural dimension significantly improves the supplier selection process.
Originality/Value: The proposed model contributes to strategic decision-making by identifying key performance factors, enabling predictive evaluation, and employing robust analytical tools. This approach not only reduces risks and costs but also serves as a practical model for improving supply chain performance in similar industries.

Abstract The success of service organizations can be attributed to customer orientation and attention to service quality. Due to the importance of service quality in service industries and its significant impact on customer satisfaction, the question of how service quality can be measured has always been raised. Attention to quality in all organizations seeking excellence in the service sector is considered one of the most important concerns of managers. Moreover, creating quality in an organization requires attention to a set of influential dimensions that must be considered in service delivery. By reviewing the research literature, the dimensions and components for measuring service quality in the banking industry were identified. After refining the dimensions, they were categorized and then incorporated into a questionnaire distributed among bank employees and customers. The results of this study indicate that among the identified dimensions, courtesy and politeness received the highest scores from both customers and bank employees, reflecting its importance from the perspectives of both groups. Among the quality dimensions, tangibles received the lowest weight in both respondent groups compared to the other dimensions, indicating their relatively lower importance from the viewpoints of customers and employees.

Abstract In this article with title "Economic-statistical design of Bayesian control chart based on the predictive distribution for individual observations with an exponential qualitative characteristic distribution" the economic-statistical design of the Bayesian control chart based on the predictive distribution for individual observations of the exponential qualitative characteristic distribution is presented. In doing this, two types of the conjugate prior distribution and Jeffrey’s distribution are considered, and based on the distribution of observations in phase I, the predictive distribution is determined. Then, using the economic model of Lorenzen and Vance, an economic-statistical design was obtained for the data. Optimal design parameters (sampling distance, sample size, and control limits) were determined using a genetic algorithm and sensitivity analysis was performed for different values of model parameters. The results of this approach have been compared with the results of the classical model. The results show that this method is more effective than the classical method

Abstract In this study, the design of the Response Surface Method (RSM) of the Box-Behnken model (BBD) was used by MiniTab-19 software to determine the optimal conditions for the removal of AB-74 dye by nanozeolite in dimensions smaller than 100 nm. Effective parameters such as pH, temperature, dye concentration, stirrer speed and adsorbent dose were investigated in an ANOVA statistical analysis system to study the dye adsorption process. The results showed that with decreasing pH and temperature and concentration, the dye had the highest dye absorption. To remove anionic contaminants and increase the negative ion exchange capacity by nanozeolite, the nanozeolite surface was modified using tween-60 surfactant. BET and DLS tests were performed to ensure the level of correction and study of structure features. Experimental results showed that by adding the tween-60 surfactant with a concentration of 15% to the nanozeolite due to surface modification, the amount of dye adsorption increased. The kinetic model and isotherm model of the adsorption process were also studied and the results showed that the reaction kinetics follows the pseudo-second-order model and adsorption process for nanozeolite and modified nanozeolite follows the Freundlich model.

Abstract In this paper, the Bayesian estimation of the reliability parameter, R = P (X <Y), in the two-parameter Riley distribution, is investigated under increasingly censored bond samples. This issue is studied in three different ways. In the first case, assuming that the variables stress, X, and resistance, Y, both have a common location parameter and non-common scale parameters, and all of these parameters are unknown, the Bayesian estimate R is examined. Since in this case Bayesian estimation does not have a closed form, it is approximated by both Lindley and MCMC methods. In the second case, assuming that the stress and resistance variables have a known common place parameter and the common and unknown scale parameters, the exact Bayesian estimate for R is calculated. In the third case, assuming that all parameters are different and unknown, the Bayesian R estimate is calculated using the MCMC approximate method. Bayesian belief intervals are also obtained in all methods. Finally, using Monte Carlo simulations, the performance of different estimators is compared. 
 

Abstract Suppose that the lifetime distribution of a random sample of n experimental units is exponential with hazard rate θ and due to time constraints and cost reduction, interval censoring data schemes is used by the experimenter. In this research, some of the existing parameter estimators of the exponential lifetimes under interval censoring are considered. In addition, three new weighted estimators are introduced. we compare the performance of our three methods with some of the existing parameter estimators through simulation studies. For a given sample size n and k inspection times, t1, t2, . . . , tk from the exponential distribution and for each scenario, bias and mean of square error (MSE) are calculated. In addition, the relative efficiency of the proposed estimators are taken into consideration. Moreover, based on the simulation study, their performances are compared to the existing methods. Finally, the asymptotic distribution of one investigated method, which has the best performance, is obtained.

Abstract design to cost (DTC) utilized at concept phase design is branch of design for Xwhich by exploiting different techniques and procedures, estimates the life cycle costs of different design for product before its manufacturing: thus, enabling producer-by comparing costs revenues, and profits-to select the most optimal design for manufacturing a product. The present research sets out to execute and deploy DTC method for specific electro-optical product. Unlike other research works, the present study has availed itself of the axiomatic design method to analyze customers' needs and converting them to estimating life cycle costs-by examining different models in light of available costs and information. Structure in the industry under investigation –we have developed an estimation model base and developing mathematical formulas, we have embarked on calculating life cycle costs of two design A and B .the obtained results indicate that in the care of product under study, design A – as regards cost effectiveness in production and life cycle costs in economic terms- can be selected as e premium concepts design. 
 
  

Abstract Abstract: Since the development of a strong platform architecture is considered a competitive advantage for companies and is effective in improving the future generations of the product, therefore there is a need for a kind of diversified product design that simultaneously manages the costs and supply chain process and thus help to develop the architecture of the product platform. In this research, the design for vareity (DFV) method and two generational variety index (GVI) and coupling index (CI) are used to measure a product architecture and by using the quality improvement function (QFD) and the design structure matrix ( DSM), design indicators for variety are identified and ranked. Also, the DFV approach is simultaneously modeled with the categories of design for cost (DFC) and design for supply chain (DFSC) and a mathematical model applied to the development of the product platform architecture is obtained, which seeks to diversify the product and reduce costs and Supply chain process management. The case study of the current research is the phased array antenna system, in which the problem is solved using one of the new optimization techniques (LP metric) and GAMS software. After the implementation of the model, its validation was carried out and considering three objectives including the total cost and the evaluation score (competence) of the suppliers and the objective of variety and the seven main parameters of the model, sensitivity analysis and other comparisons and results. A review is provided. Regarding the comparison of the goals with each other, the findings show the inverse relationship of the total cost goal with the variety and the evaluation score goals and the direct relationship between the two variety and the evaluation score goals. Also, the results of the sensitivity analysis showed the higher effectiveness of the goal of variety among the investigated parameters, and the evaluation score (competence) of suppliers and the total cost were ranked next.