Journal of Quality Engineering and Management

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  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 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.

Abstract Reliability of a degrading load sharing k out-of-n:F system is studied in which the total load on the system is shared among all the functioning components. The performance of the system is evaluated based on degradation of its components, and the system lifetime is determined accordingly. It is assumed that degradation of the functioning components follows a Gamma process, such that the shape parameter is a time dependent function and the scale parameter is constant. The effect of load sharing is modeled by considering a power law function as the shape parameter of the Gamma process. A recursive formula is obtained to calculate the reliability function, and its sensitivity is discussed with respect to the model parameters. The estimation problem of the load sharing parameter is also investigated based on degradation data measured at some pre-specified inspection times. Since the obtained formulas are complex, the reliability function is numerically computed. Further, the performance of the proposed estimator is studied using a simulation algorithm.

Abstract Purpose: In this paper, we examine the Bayesian inference of the parameters of the generalized power Lindley distribution in the presence of type two hybrid censored data.
Methodology: To estimate the maximum likelihood of the parameters, given that the estimates cannot be obtained implicitly and do not have a closed-form solution, we employ the EM algorithm and use the Fisher information matrix to construct asymptotic confidence intervals. Additionally, when estimating the parameters of the generalized power Lindley distribution, which we denote EPL throughout the article, we employ two Lindley approximation methods and Markov chain Monte Carlo under the squared-error loss function. We obtain HPD confidence intervals according to Bayesian estimates. Then we compare two Bayesian methods using simulation studies.
Findings: The Monte Carlo method for the three-parameter distribution shows less bias and greater consistency than the Bayesian parameter estimates derived from the Lindley approximation. In high-dimensional distributions, the MCMC method yields more accurate forecasts than the Lindley approximation, and convergence occurs more rapidly. The MSE estimates from the Lindley approximation, as shown in Table 2, are significantly larger than the data dispersion for a similar sample size from the MCMC method, as presented in Table 3. We also provide an example of real data.
Originality/Value: Given that no study has been conducted so far on the generalized Lindley power distribution in the presence of censored hybrid type II, the findings of this study can be used for future studies.

Abstract Purpose: In this paper, a multi-objective mathematical programming model is presented for designing a multi-level, multi-period fruit supply chain, while accounting for sustainable development goals encompassing cost minimization, greenhouse gas emission minimization, and social dimension maximization. In the proposed model, product quality plays a fundamental role in supply chain design, and fruits are graded in distribution centers based on quality and distributed to fruit markets, compost factories, juice factories, concentrate factories, and drug factories.
Methodology: The proposed multi-objective model is solved using fuzzy goal programming. The weights of the objective functions as well as the weights of social dimensions are calculated using the fuzzy best-worst approach.
Findings: In this research, a case study of Fars province, the third-largest apple-producing province in the country, is used to evaluate the performance of the proposed model. The results of the proposed model were compared with three models from economic, environmental, and social perspectives. The research findings show that system sustainability cannot be achieved by separately optimizing models with economic, environmental, and social perspectives. The proposed model achieves an efficient optimal solution across all three dimensions of sustainability, with significant improvements in the environmental and social dimensions and a negligible increase in system costs. In addition, by establishing a proper balance across all three sustainability dimensions, the proposed model leads to a supply chain with a different network structure and a different number of deployed facilities compared to the other three models.
Originality/Value: The added value of this research is to provide a comprehensive model that considers sustainability and quality as the main factors in grading and distributing products across different levels of the supply chain. The findings of this research can help policymakers and operational managers in the fruit industry make strategic and operational decisions in fruit production, processing, and supply-to-sale markets based on sustainability dimensions.

Abstract Despite the important issue of high-reliability organizations, it has not been adequately addressed in our country. Scientific designs that can accurately assess organizations and various assessments and evaluations in the situations of organizations and the possibility of decision-making of organizations and improvement programs are decisive. This aims to provide a comprehensive research need for evaluating high-reliability blocks. In this research, the meta-synthesis method has been used, during which, based on the seventh-stage method of Sandelowski and Barroso, related sources and models have been examined and analyzed, and finally, a five-level model with a comprehensive view has been designed, in which at each level, the characteristics of determining the maturity level have been presented. This model can be used to evaluate and analyze the maturity level of high-reliability organizations, as well as to design an improvement and development program for these organizations.

Abstract The use of Heat Recovery Steam Generators (HRSGs) in combined cycle power plants dramatically increases the efficiency of power generation. One of the most important parts of HRSG is Feed Water System (FWS). This paper describes the process of selecting the optimal configuration for the FWS of Heat Recovery Steam Generator, which has been done in MAPNA Boiler and Equipment Engineering and Manufacturing Company. First, the important equipment of the FWS is identified and is demonstrated by the reliability block diagram. Then the failure rate of each equipment is estimated using the documents of the International Atomic Energy Agency, the OREDA handbook and the IEEE 500 standard, and the system reliability is calculated in the Excel software environment. Finally, the results were analyzed and based on that, the optimal configuration of the FWS is determined. The proposed approach can also be used to design the reliability-based design of other industrial systems. 
 
 

Abstract Hybrid censorship is a combination of the type I and II censoring schemes, which is divided into two types of type I and type II hybrid censoring schemes based on the criteria for ending the experiment. One of the issues in the censoring is choosing the best censoring scheme. Different criteria can be considered to determine the optimal censoring scheme that each of them may lead to a different design. One of the most important criteria is the cost of testing. In this paper, considering the cost of testing as an optimization criterion in type I hybrid censoring, the optimal censoring scheme is determined, when the lifetime of data is Burr type XII distribution. Numerical calculations as well as an example are presented to evaluate the results of the paper. Finally, a summary of the results of the article is given.

Abstract Iran's steel industry chain is very important as one of the key industries which is the driving force of many other industries. The purpose of this research is to prepare the companies of this industrial chain for the optimal establishment of the organizational performance management system (PMS) by identifying the barriers investigating the relationship between them and also determining the priorities for removing the barriers in this chain. In this regard, barriers to the optimal implementation of PMS were extracted from the literature review. Validation of identified barriers to PMS optimization was conducted through semi-structured interviews with industries experts. BY Using IMS method, the relationship between barriers and the priority of removing the final barriers was identified. The results show that the government's involvement in policy-making is the main barrier. The existence of many resources in the country (Iran) and the costs of performance control are also among the root factors. Therefore, it seems that removing these three barriers are the first priority to optimize the performance management system.

Abstract Purpose: With the rapid growth of the financial technology (FinTech) industry, digital advertising has become one of the key tools for attracting new customers and increasing the loyalty of existing ones. In environments where uncertainty and decision-making complexity play significant roles, the use of metaheuristic algorithms can help optimize digital advertising efforts.
Methodology: This study proposes a three-level model in an intuitionistic fuzzy environment and utilizes the Stackelberg game to examine the impact of advertising on performance, customer acquisition, and customer loyalty. In this study, the advertising process of FinTech companies is modeled as a three-level decision-making process encompassing customer acquisition, advertising performance, and customer loyalty. To solve this model, Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are employed to optimize advertising strategies.
Findings: The results indicated that the proposed model accurately predicted customer loyalty and that metaheuristic algorithms effectively optimized advertising parameters. The analysis of the results showed that conversion rate and purchase amount are the most influential factors affecting customer loyalty. Furthermore, the findings revealed that using hybrid algorithms can reduce advertising costs and increase Return on Investment (ROI). Comparing the proposed algorithms showed that the hybrid approach, combining genetic algorithms and particle swarm optimization, outperformed the individual methods in predicting customer behavior.
Originality/Value: Based on the findings, it is recommended that FinTech companies adopt metaheuristic algorithms to optimize digital advertising and achieve precise customer targeting. These approaches can enhance advertising effectiveness, reduce marketing costs, and improve customer loyalty within the FinTech industry.

Abstract The Purpose of this research is Interpretive Prioritization of Sustainable Outsourcing Risk Control Based on Capacity Chain Capabilities Based on Analysis Interpretive Ranking Process (IRP). In this study, in order to identify the components (supply chain capabilities) and research propositions (sustainable supply chain outsourcing risks), a combined analysis was performed with the participation of 14 industrial management experts at the university level, and in a small part, components and propositions. Identified in the form of matrix questionnaires were evaluated by 25 managers with experience in the National Company for Southern Oilfields. The results showed that the knowledge management capabilities component has the highest level of priority in the supply chain capabilities to control the risks of sustainable supply chain outsourcing and two propositions of increasing environmental pollution and lack of investment in waste recycling are the most likely risks of sustainable supply chain outsourcing. This research as a decision-making basis to understand the supply chain cycle to make the best decision can help improve the level of development of the National Company of Southern Oilfields in a competitive market environment. 

Abstract Abstract: One of the most widely used tools in the rapid detection of assignable causes is control charts. Given the importance of economic costs, Duncan proposed the first economic model in the presence of multiple assignable causes in order to reduce the economic costs of the quality cycle. In his model and all the economic designs derived from that, assumed that after the occurrence of an assignable  cause, process is free from the occurrence of other assignable causes. In this paper, after criticizing previous models for incorrect and unrealistic use of this assumption to calculate the average cost per unit of quality cycle time, a realistic economic-statistical design in the presence of multiple assignable causes for economic-statistical design of  X-bar control chart is presented. The numerical results of our model show well that in the previous models; the average cost per unit time of the quality cycle is severely underestimated compared to the actual value and with increasing the Weibull distribution shape parameter, the probability of this assumption is greatly reduced. Therefore, it is suggested that in order to eliminate the shortcomings of the economic design of various types of control charts with multiple assignable causes in future research, they should be redesigned based on our model.

Abstract Purpose: This research aims to present a model for creating shared value in startups based on new technologies.
Methodology: To achieve this goal, a mixed-methods approach was employed, consisting of two phases: qualitative and quantitative. In the first phase, a review of the theoretical and empirical foundations of the topic was conducted, and to enrich the results, insights from a selected group of experts were utilized. This group consisted of 9 experts specializing in new technologies and startups, selected through purposive sampling based on theoretical saturation.
Findings: Based on the results, 17 main categories, 161 sub-components, and 1346 concepts were identified in this research. In the quantitative section, the model's dimensions and components were prioritized using pairwise comparison questionnaires and fuzzy hierarchical analysis.
Originality/Value: According to the findings, customers and services are the central focus of activities, and startups, by deeply understanding customer needs and desires, provide products and services aligned with their values. Collaboration and networking with customers and business partners facilitate knowledge exchange and innovation, which is a fundamental element that enables startups to address customer problems using new technologies. Additionally, attention to product and service quality, attracting and retaining top talent, and securing financial resources are other important aspects of this model that lead to increased customer satisfaction and trust.

Abstract In this study, the usual kriging method as a linear statistical estimator and two intelligent methods of artificial neural network ANN and adaptive neural fuzzy inference system ANFIS were evaluated in predicting the amount of electric conductivity and nitrate in groundwater. In order to conduct studies, nitrate concentration in 40 wells in Lanjanat plain of Isfahan was measured by spectrophotometer and electrical conductivity. The input data of the artificial neural model, including the length and width of the geographies, the nitrate concentration, and the electrical conductivity value were determined as the output of the model. In order to investigate the performance and efficiency of artificial intelligence models in predicting qualitative information, qualitative information of 50% of the wells was used for calibration and 50% of the wells were used for validating the models. Finally, the output of the models was compared with the value measured in the observation wells based on the mutual error evaluation criteria. The results showed that the ANFIS model performed better than the other two interpolation models in predicting the value of electrical conductivity and nitrate, respectively, with the root mean square error (RMSE) and (mg/l) of 5.362, with the mean bias error (MBE) 2.365 with a correlation coefficient (R) of 0.767. Also, the ANN model had far better results than the usual kriging method. Based on this, ANFIS model is proposed for spatial prediction of electrical conductivity and nitrate in the study area.

Abstract Purpose: This paper aims to accurately estimate the parameters of the fractional Ornstein-Uhlenbeck model using the Bayesian method and the SIR simulation algorithm and to compare its performance with the Maximum Likelihood Estimation (MLE) method in the context of stochastic differential models with long-memory properties. The paper also seeks to evaluate the efficiency of the Bayesian approach in similar models, particularly in analyzing financial data with long-term dependencies.
Methodology: In this study, the parameters of the fractional Ornstein-Uhlenbeck model are estimated for the first time using the Bayesian method, with appropriate prior distributions and the SIR algorithm employed for simulation. The efficiency of the Bayesian estimator is compared with that of the MLE estimator using RMSE and variance indices.
Findings: The results demonstrate that the Bayesian estimator provides more accurate parameter estimates than the Maximum Likelihood method. Moreover, as the degree of long-term data dependence increases, the accuracy of estimates improves under both methods; however, the Bayesian approach consistently outperforms the MLE. Additionally, the parameter σ is estimated with higher precision compared to the parameters k and μ.
Originality/Value: The originality of this paper lies in the application of the SIR algorithm to estimate the parameters of the fractional Ornstein-Uhlenbeck model. This approach has not been previously explored. This innovation represents a significant contribution to the application of Bayesian methods for parameter estimation in stochastic differential models with long-memory properties, and it opens new avenues for applying similar techniques to models such as the Heston model in future research.

Abstract This paper for the first time in the statistical design of adaptive control charts, deals with the statistical design of univariate control chart X ̅ under the Weibull shock model. practically, the use of shock models with more flexible risk rate functions in the statistical design of adaptive control charts is closer to reality. This study shows that diagram X ̅-VRS under Weibull shock model with non-uniform sampling intervals and variable sample size, compared to diagram X ̅ under Weibull shock model with non-uniform sampling intervals and fixed sample size (FRS), Detecting average changes is faster and performs better.
In this model, with increasing changes in the mean, the rate of detection of changes in the mean increases and the value of h_1 increases and the ANF decreases. Also relatively large changes (𝛅≥2), lead to a relatively small sample size (n_2≤ 10) and smaller changes (2 ≤ 𝛅 <0.25) lead to a larger optimal sample size (〖"11≤n" 〗_2 "≤14" ).

Abstract Emphasizing the importance of competition and cooperation in supply chains caused a resurgence of game theory ‎as a tool for the analysis of interactions in a supply chain. The development of the business and the ‎complexity of retailing requires a change in the advertising approaches. Affiliate advertising is one of the ways that ‎manufacturers and distributors can jointly participate in advertising programs. Therefore, a variable is defined as ‎the participation rate, which is a percentage of local advertising costs that the producer agrees to pay. In this ‎research, we consider cooperation in advertising during two different advertising function models along with pricing ‎decisions, warranty period and quality level, in a two-level supply chain. Therefore, demand will be affected by ‎price, advertising, warranty period and quality level. Manufacturer and retailer can have cooperative advertising. ‎The theory of non-cooperative and cooperative games is the tool used to solve this problem. Due to the complexity ‎of the model, a meta-heuristic algorithm, the optics inspired optimization, which is a population-based search ‎algorithm, is used to solve the problem. In this research, it was observed that the sum of the profits claimed by both ‎parties in the non-cooperative game is smaller than the profit of the whole system in the cooperative game, and the ‎profit of each individual is also lower in the non-cooperative state than in the cooperative state. Therefore, ‎cooperation between two players increases their profits. Also, sensitivity analysis has been done on some ‎parameters, including parameters related to product quality.‎