Journal of Quality Engineering and Management

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.‎

Abstract Purpose: This study aims to evaluate and rank collaboration models in the Iranian rubber industry supply chain from the perspective of digitalization quality. Digitalization quality refers to the effective use of Industry 4.0 technologies to improve transparency, integration, agility, resilience, and sustainability. The rubber industry was selected due to its operational complexity and urgent need for digital transformation.
Methodology: A multi-criteria decision-making approach was adopted, combining the Fuzzy Best-Worst Method (BWM) for weighting the evaluation criteria and TOPSIS for ranking the collaboration models. A sensitivity analysis was also conducted to assess the robustness of the results across varying criterion weights.
Findings: The digital supply chain model ranks highest in digitalization quality, with "technology integration" as the most critical criterion. The sensitivity analysis confirms the rankings' robustness and stability across different weight scenarios.
Originality/Value: This research uniquely addresses the comparative assessment of collaboration models in the rubber industry based on digitalization quality. The use of a Fuzzy BWM-TOPSIS hybrid method and comprehensive sensitivity analysis provides a novel, practical framework for strategic decision-making in digital supply chain transformation.

Abstract
 The purpose of this article is to examine the quality of goods in the process of transfer between members of the supply chain. For this purpose, a suitable mathematical model has been designed to manage the supply channel route of the supply chain problem and the problem has been solved using a genetic algorithm. In the present study, real-world conditions such as vehicle traffic constraints as well as product quality are considered by considering returned items, and also the Markov chain is used to investigate the possibility of transfer between members of the supply chain. The innovation of this research is the introduction of the channel selection system for transportation planning in the supply chain. The results show that the method used in this study has a good performance and the optimal way of product flow in a distribution network using genetic algorithm is presented. 

Abstract Today, the increasing progress in technology, the expansion and development of human needs for sustainable technologies have caused attention to electric energy to be in the center of attention more than in the past. Therefore, increasing the reliability of electrical systems in the power industry is very important. The purpose of this research is to provide a mathematical model to calculate and increase reliability in the power distribution network. This research is practical in terms of its purpose and results, and in terms of the method and nature of implementation, it is based on operational research that was conducted using mathematical modeling and using Python software based on data from 1398 to 1402. The findings show that parameters such as generators, high pressure and low pressure busbars, 20 kV to 400 V power transformers, communication cables, capacitors, generators and UPS are more important in calculating the reliability of this network. Therefore, according to the purpose and the corresponding limitations, a suitable mathematical model has been presented for each parameter. The results show that after 50 repetitions and simulations, the ultra-emergency line has a higher importance and rank in reliability among the four output feeders. Also, based on the presented model, it has been observed that the entire 20 kV line under investigation has 0.67 degrees of reliability. The results of this research can be considered as a suitable basis for the implementation of research and operational projects in wide radial networks in the electricity industry.

Abstract Purpose: This study aims to investigate the effect of total quality management on performance by focusing on the mediating role of management accountants and the management accounting system. With an empirical approach, the study aims to provide a deeper understanding of the mechanisms underlying total quality management's impact on performance and, in particular, to assess management accountants' participation and the role of accounting information tools in this process.
Methodology: Standard questionnaires were used to achieve the research objective. The study's statistical sample consisted of 97 middle-level managers from manufacturing companies in the west of Mazandaran province in 2024. The questionnaire response rate was 80.1%. SmartPLS3 software and structural equation modeling were used to analyze the hypotheses.
Findings: The results indicate a positive, significant relationship between total quality management and performance. They also indicate that management accountants have a positive role in implementing total quality management in performance. However, the management accounting system does not mediate between total quality management and performance.
Originality/Value: The research examines the relationship between total quality management and performance by reviewing two mediating variables, namely management accountants and the management accounting system; a topic considered separately or incompletely in the previous literature. This study, using real data from manufacturing companies and structural equation modeling, provides new and practical evidence on the strategic roles of management accountants in achieving organizations' quality-oriented goals and partially fills the existing research gap regarding the interaction between total quality management and management information systems.

Abstract Supply chain flexibility is a factor in gaining a competitive advantage in today's changing world, which helps to make effective supply chain management decisions. Therefore, designing an integrated approach to the values resulting from supply chain flexibility can help sustainable development opportunities in companies and control threats in a competitive environment. The Purpose of this research is Interpretive Ranking Process (IRP) Integrating Value Based on Supply Chain Flexibility. Since supply chain value integration can be different, in this study, value integration was divided into two parts: market process pluralism and production process pluralism, based on two interpretive ranking analyzes and a comprehensive structural interpretive model. Select the most effective value integration propositions based on supply chain flexibility. In this research, in order to identify the components and propositions of the research, the analysis was done with the participation of 15 academic experts and in the quantitative part, the components and propositions identified in the form of matrix questionnaires by 23 managers with experience petrochemical companies were evaluated for interpretive analysis. The results showed that the two components of financial resource flexibility (T6) and information system flexibility (T7) have the highest level of priority in supply chain resilience dimensions. And the two propositions of creating innovative values and demand-driven management that are effective in promoting the level of pluralism in market processes of value integration and the dynamics of communication with suppliers of raw materials were identified as the most motivating factor in the multiplicity of production processes in order to create value integration. This research as a decision basis to understand the supply chain cycle to make the best decision can help improve the level of development of petrochemical companies in a competitive environment. 

Abstract Expanding product variety helps customers to find products that perfectly fit their individual needs. Therefore, companies are looking for ways to better manage and variety procedures in the product and a design that is compatible with variety can be considered a competitive advantage for the organization. The present study aims to evaluate engineering designs with a variety approach, has presented an optimization model that examines engineering designs in terms of two parameters, the amount of changes required by the design to standardize now and future manufacturers' efforts to redesign the components. The parameter of the amount of changes required by the design for standardization now is obtained with the help of the generational variety index and the coupling index and the parameter of future manufacturers' efforts to redesign the components with the help of the commonality index. Applying the model developed in the present study, as well as determining the allowable components in the standardization priority and the amount of effort in the future will lead to the selection of engineering design that can reduce the cost of product development, redesign efforts and market time. The model is developed in the present study has been implemented on one of the fuzzy array radars of Iran's electronics industry and has determined the optimal engineering design of the desired radar.

Abstract Purpose: The most important effects of the Internet of Things in healthcare include the ability to exchange information, reduce hospitalization costs, and improve healthcare costs. The primary challenges of the Internet of Things in healthcare are security and privacy, with image transmission particularly crucial for communication and security. The primary objective of this paper is to design a suitable channel for transmitting medical data via chaotic synchronization that employs fuzzy modeling.
Methodology: This paper presents a new method for transmitting medical images to preserve patient information by synchronizing two fractional-order convolutional neural networks based on polynomial fuzzy modeling. Using chaotic signals as a carrier for medical images and employing a suitable fuzzy controller for synchronization at the receiver enhances security and significantly reduces the likelihood of detection. In this scheme, a suitable fuzzy controller is designed to establish the stability of the closed-loop system. Then, considering the synchronization scheme based on the polynomial fuzzy model and its error detection, a chaotic masking method is proposed to encrypt patient-related images.
Findings: Simulations have been performed on color and black-and-white medical images. Encrypted and recovered images have been obtained using this scheme. The simulation and accuracy of the proposed method's results have been investigated using MATLAB software. To evaluate the performance of the proposed method, various criteria, including image histogram, signal-to-noise ratio, correlation, and information entropy, were assessed. The results demonstrate the effectiveness of the proposed method in image encryption.
Originality/Value: This paper presents a new method for transmitting medical images to preserve patient information by synchronizing two fractional-order multi-convolutional systems based on polynomial fuzzy modeling. Using chaotic signals as a carrier for medical images and employing a suitable fuzzy controller for synchronization at the receiver enhances security and significantly reduces the likelihood of detection. In this project, a suitable fuzzy controller is designed to establish the stability of the closed-loop system. Then, considering the multi-state synchronization scheme based on the polynomial fuzzy model and its error detection, a chaotic masking method is proposed to encrypt patient-related images.

Abstract Purpose: This research aims to empirically analyze the low beta anomaly within the framework of random basket theory using linear and quantile regression. This financial anomaly refers to the higher long-term returns of a portfolio of low-beta stocks than of a portfolio of high-beta stocks. This study examines the excess growth rate generated in a random portfolio based on this financial anomaly. The statistical population of this study consists of 8 stocks from the US stock market during the period 2015 to 2023.
Methodology: To achieve the research objectives, a continuous-time dynamic model with analytical solutions is proposed. To find its optimal weights or strategies, the "functionally generated portfolios" approach and the concept of "generating functions" are used. Finally, a regression analysis of US stock market data is conducted to examine the growth rate generated in this model.
Findings: The results show that investors are always trying to increase their investment returns by adopting an appropriate method. In this regard, higher returns from low-beta investment portfolios have been observed over the past few decades, and the use of random portfolios as a reasonable method to examine the portfolio's excess return in this financial anomaly is thus crucial.
Originality/Value: Given the innovative nature of this research in using stochastic portfolio theory to examine the excess growth rate generated based on the low beta anomaly, the results can help investors construct optimal portfolios with higher long-term returns.

Abstract Considering the high flexibility of type-2 fuzzy sets to represent uncertainty, their applications in different scopes included control charts are extended. In this paper, to control the centrality of non-normal and ambiguous processes, two non parametric control charts included sign and signed-rank charts have been developed using interval type-2 fuzzy sets. In the type-2 fuzzy sign chart and type-2 fuzzy signed-rank chart, the observations of each sample are compared with the centrality of the process in the control state, which for this purpose it used two different methods. To describe the applicability of the proposed charts, they are used in an example with real data and it is showed their correctness performance. Also, to evaluate the performance of type-2 fuzzy sign chart and type-2 fuzzy signed rank chart, simulation programs have been used in which type-2 fuzzy random variables with three different density functions are generated and in each distribution and both methods, the average run length (ARL) of the charts are calculated. The numerical results show the appropriate performance and applicability of the type-2 fuzzy sign chart and type-2 fuzzy signed-rank chart to control the centrality of non-normal fuzzy random variables.

Abstract One of the most practical methods of monitoring and controlling project performance is earned value management. The widespread use of this method in many projects leads to the production of multiple indicators with mutual influence, This important feature makes their individual analysis with errors. In this method, not only the mutual influence of the indicators is not paid attention to, but also the lack of attention to the monitoring of the variability of the indicators has caused this method to monitor the project based on the criterion of indexability. In this research, using multivariable control charts, we have simultaneously checked the performance indicators of a gas supply project based on real data. After analyzing the obtained results, we have compared the performance of Hotelling and (MEWMA) charts with each other. After comparison, it was found that MEWMA chart has more capability and sensitivity than Hotelling chart in identifying changes in multivariate processes.