Journal of Quality Engineering and Management

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.

Abstract Purpose: Traditional banking needs new FinTech innovations and technologies to improve its processes and services. FinTech innovations have led to significant changes in the banking system, including advancements in risk management. Therefore, the present study aimed to investigate and analyze the heterogeneity and the mechanism underlying the effect of FinTech innovation on the risk-taking of commercial banks using balanced panel data from 20 banks for the period 2013-2022.
Methodology: Based on web technology, an indicator at the bank level is considered, including the creation, annual number, and frequency of news related to fintech innovation from each bank. This indicator is calculated as the ratio of the value of online shopping and bill payments made through the Internet and mobile devices to GDP. To address potential endogeneity issues, including measurement errors and omitted variables, the methods of Instrumental Variables (IV) and Difference-in-Differences (DID) were employed to test the hypothesis and obtain consistent estimates.
Findings: Showed that improvement in FinTech bank innovation significantly reduces risk-taking. The results of the mechanism analysis indicate that a bank's FinTech innovation reduces its risk-taking through two channels: increasing operating income and enhancing the capital adequacy ratio. The analysis of the heterogeneity of bank size, bank type, and competitiveness shows that larger, public, private, and highly competitive commercial banks have a more pronounced effect on reducing risk-taking in the development of technological innovation. Also, robustness and stability tests, including changing the methods used to construct the FinTech innovation index, replacing risk-taking indicators, and reducing the change in the study sample, showed that the findings remained unchanged.
Originality/Value: The banking system should adopt a development model aligned with the era and utilize FinTech solutions to accelerate its digital transformation. Finally, since the use of FinTech by commercial banks presents certain potential risks, banks should enhance their risk management. Implement applicable supervisory measures, such as information disclosure standards and risk management indicators.

Abstract Purpose: The pharmaceutical services sector is vital in all countries for two reasons. First, it concerns human lives, and in all societies, human capital is one of the most essential assets of a country. Second, it is due to the high financial turnover in this industry. In recent years, many advances have been made in the pharmaceutical industry. Still, the most essential problem is the lack of a clear, logical solution for identifying patients' needs, especially those with rare diseases. This research aims to identify the priorities of medical services for rare diseases in Iran, in comparison with the best in this field worldwide, and to achieve world-class standards and prioritize these needs.
Methodology: This study is an applied research that combines the approaches of quality function deployment and benchmarking under the title of quality benchmarking development. First, quality requirements are collected from patients, doctors, and pharmacists; then, by comparing top pharmaceutical companies, the relationship between requirements and quality elements is analyzed; and finally, the weighted importance of each element for improving pharmaceutical services is determined.
Findings: The results of the study showed that the five factors that have the highest priority in the field of pharmaceutical services in Iran are, respectively: improving the quality of drug production, empowering medical personnel, improving the quality of drug distribution, promoting medical services for rare diseases, and improving supervision of drug production.
Originality/Value:  This study presents a structured approach for identifying and prioritizing the needs of patients with rare diseases within Iran's pharmaceutical service system. The main innovation of this research lies in the simultaneous integration of the voice of the customer (patients, physicians, and pharmacists) with benchmarking against leading global pharmaceutical companies, enabling the identification of performance gaps and the determination of key factors for enhancing the quality of pharmaceutical services.

Abstract  In recent years, many efforts have been made to achieve comprehensive criteria and models for evaluating companies. In this article, the aim is to obtain the efficiency of petrochemical companies based on human resource functions, operations and sales. The efficiency model can show better performance for calculating efficiency by considering human resource functions, operations and sales. 14 active petrochemicals are considered in the stock exchange and the concept model is based on three units of human resources, operations unit and sales unit. Data extracted from Codal site. The efficiency ranking of petrochemical industries is calculated with the same data by three methods. A network model is used to obtain efficiency and the results show the efficiency of petrochemicals 1, 10 and 11 in each model. The implementation of models shows that human resource functions, operations and sales can perform better. Also, the different results of the three models from each other show the importance of choosing a performance appraisal model for organizations. 
  

Abstract Abstract: The production of low quality drugs in addition to wasting resources, leads to risks to public health. Quality risk management can ensure the high quality of the drug for the patient by controlling quality risks during the product manufacturing process. The present study aimed to identify and analyze the particle contamination risk of the vial product at Daana Pharmaceutical Company by implementing quality risk management process based on the ICH Q9 guideline. For this purpose, in order to identify the risk, the failure modes and effects analysis (FMEA) method and to identify the root causes of failure, the "Why" root cause analysis (Why RCA) technique and to analyze the risk, a combination of the failure modes, effects and criticality analysis (FMECA) and Bayesian Belief Network (BBN) analysis methods were used. Comparison of the results of the FMECA and BBN-FMECA approaches showed that at Daana Company, risk analysis with both methods gives similar results. Also quality of raw material powder and primary packaging materials, personnel performance and air conditioning monitoring system are identified as the most important causes of product contamination risk

Abstract In this article, a queuing system with finite capacity, referred to as M/M/m/K, is analyzed for m ≥ 2, where K represents the system's capacity and m indicates the number of servers. Initially, a function known as the system cost function is introduced. This function is based on the number of customers present in the queue and the number of servers available. The main objective is to identify the optimal number of servers, termed mOpt, that minimizes the system cost function. This optimal configuration, denoted as M/M/mOpt/K, is termed the optimal system. To illustrate the concept, a numerical example is provided, showcasing various values of K to determine the optimal systems. The analysis covers key performance metrics such as the average number of customers in the queue and the entire system, the average waiting time of the customers both in the queue and the system, and a metric referred to as the average degree of customer satisfaction within these queuing systems. Through this comprehensive approach, the study aims to provide valuable insights into optimizing queuing systems for better efficiency and customer satisfaction.

Abstract Any statistical method designed to detect process changes over time is within the scope of statistical process control. Among the most widely used tools for statistical process control, the control chart is the most important and powerful tool for statistical process control. Here it is important to understand that statistical process control charts are not complete in relation to process control in two respects. They do not have non-random causes and their elimination. In this paper, we introduce robust control diagrams for time-series data to detect reasoned deviations. We collect daily diagrams and draw solid control charts and standard control charts for time series data. Statistical analysis of this design was performed using SPSS16 software and finally by comparing Solid control diagram With standard control diagram for time series data, we conclude that the stable control diagram has a better performance than the standard control diagram. 

Abstract Purpose: This study aims to determine the optimal model within the family of queueing models, where interarrival times follow an exponential distribution and service times follow an Erlang distribution, under a finite stopping time TTT. The significance of this research lies in its application to optimizing the performance of service systems using queueing theory.
Methodology: To select the optimal model, a cost function and a performance metric, namely the average customer satisfaction level, are first defined. Subsequently, a new index, named ORS, is introduced based on the cost function, average customer satisfaction, and the system's stability probability. The optimal model is identified as the one with the highest ORS value. Numerical analysis is employed to demonstrate the procedure for determining the optimal model.
Findings: The numerical results indicate that the ORS index is an effective criterion for evaluating and comparing different queueing models, enabling optimal model selection by incorporating multiple performance aspects.
Originality/Value: The main contribution of this research is the introduction of the ORS index as a novel and comprehensive measure for optimal model selection in queueing systems. This approach can enhance service system design and improve customer satisfaction levels in practical applications.

Abstract Purpose: This study aims to propose a fair fixed cost allocation scheme among a set of Decision-Making Units (DMUs), such as banks or factories, in an uncertain environment. The allocation is designed so as not to reduce DMU efficiency and may even lead to efficiency improvements.
Methodology: To achieve this goal, a model is developed based on Data Envelopment Analysis (DEA) integrated with robust optimization. The inputs and outputs of the DMUs are treated as fuzzy random variables to reflect environmental uncertainty. The model is linearized and converted into a deterministic programming model using principles from stochastic programming. Furthermore, a common set of weights is used to ensure fairness in the allocation process.
Findings: The results indicate that, under the proposed fixed-cost allocation plan, the DMUs' (banks') efficiency scores are not only maintained but, in many cases, improved, confirming the model's effectiveness in preserving and enhancing performance under uncertain conditions.
Originality/Value: The novelty of this research lies in integrating DEA and robust optimization in uncertain environments to design a cost allocation model that ensures non-decreasing efficiency. Using a common set of weights enhances the approach's fairness. Additionally, applying the model to the Iranian banking sector highlights its practical relevance and managerial value.

Abstract In this article, we introduce three new distribution-free Shewhart-type control charts that exploit run and Wilcoxon-type rank-sum statistics to detect possible shifts of a monitored process is introduced. Exact formulae for the alarm rate, the run length distribution, and the average run length (ARL) are all derived. A key advantage of these charts is that, due to their nonparametric nature, the false alarm rate (FAR) and in-control run length distribution is the same for all continuous process distributions. Tables are provided for the implementation of the charts for some typical FAR values. Furthermore, a numerical study carrited out reveals that the new charts are quite flexible and efficient in detecting shifts to Lehmann-type out-of-control situations.
Statistical quality control charts were introduced in the early work of Shewhart (1926) and since then several variations of them have been proposed for monitoring continuous characteristics. Most of the control charts are distribution-based procedures in the sense that the process output is assumed to follow a specified probability distribution (usually normal); see, for example, Albers et al. (2004).

Abstract In usual quality control methods, the quality of a process or product is evaluated by monitoring one or more quality characteristics using the corresponding distribution of one or more variables. Recently, in many cases, quality is defined through the relationship called profile between one or more response and independent variables as a supplanting way. In order to monitor linear profiles in this study, the EWMAR control chart and its normal distribution are used to detect various changes in the intercept, slope and standard deviation. To improve the performance of the chart, run rules are used here. Then, in order to validate the proposed control chart, its performance is examined based on the average length of the sequence (ARL) criteria in the out-of-control for increasing changes, decreasing changes and simultaneous changes in intercept and slope and standard deviation. The results show that the proposed method has a much better performance in incremental and decreasing shifts than other charts. Also, the proposed scheme performs better than other run rules. 
 
 

Abstract : Mineral industries are one of the important sectors of industry in Iran, therefore, it is necessary to improve the quality of mineral products. One of these products is foundry silica sand. The aim of this study was to create a complete model using this type of silica sand. A comprehensive analysis was done on ten mines and seven mines were selected to perform the quality improvement stage. A total of 1400 tests were conducted to achieve the main goal of the research, which was to increase the quality of silica sand parameters. It was also found that the seven basic characteristics of silica sand have a significant effect on the quality of the final products. The quality of silica sands is influenced by elements such as calcium, sodium, potassium and magnesium, which are alkaline elements of the soil. A higher percentage of silica in a mineral is usually associated with increased quality, as it ensures the achievement of ideal properties and performance in silica sands. Factors affecting the quality of silica sand were prioritized by experts using the fuzzy Delphi technique and hierarchical analysis. These factors have an effect on the chemical composition, purity, reactivity and performance of silica sands. Also, a data mining model was designed to predict the quality of these sands. The findings of this study show that the presence of calcium, sodium, potassium, magnesium, silica content, ADV (sand alkalinity or acidity) and pH affect the quality of silica sands. It is concluded that this model provides an efficient attitude and prediction to increase product quality.

Abstract Purpose: In the complex and volatile conditions of the Iranian financial markets, the need to utilize data-driven decision-making frameworks to enhance the quality of strategic decisions is increasingly felt. However, a review of previous studies indicates that most research has examined big data solely from a technical perspective and in stable environments of developed countries, paying limited attention to the role of managers' behavioral and cognitive factors in the data-to-decision transformation chain. Therefore, the present study, aiming to fill this gap, examined the direct and indirect effects of Big Data Utilization (BDU) on the Strategic Decisions Quality (SDQ) through the variables of Data Quality (DQ), Information Quality (IQ), and Information Adoption (IA).
Methodology: This study pursued an applied purpose and employed a descriptive survey method. The statistical population included 697 financial institutions active in Iran's capital market, and the sample size was determined to be 244 companies using G-Power 3. Data were collected via a standardized online questionnaire, using simple random sampling, and analyzed using structural equation modeling with the partial least squares method in SmartPLS 3.
Findings: The effects of BDU on DQ and IQ were confirmed with path coefficients of 0.405 and 0.210, respectively, at a 99% confidence level, while its direct effect on SDQ was not supported (0.083). DQ positively affected IQ, IA, and SDQ (0.381, 0.353, and 0.296), and IQ influenced IA and SDQ (0.674 and 0.493). Finally, IA positively impacted SDQ (0.286), all at a 99% confidence level.
Originality/Value: This study, for the first time, employed an experimental approach to demonstrate that information adoption by managers influences the improvement of strategic decision quality, and that DQ and IQ alone are not sufficient. Optimal decision-making requires the synergy between technological capabilities and managers' behavioral–cognitive capacities. The proposed conceptual model integrates the relationships among BD, DQ, IQ, IA, and SD, providing both theoretical enrichment and a practical framework for companies and financial institutions operating in the Iranian capital market.

Abstract Extensive research has been done in the field of statistical process control. Also recently, control charts based on the Bayesian predictive distribution idea have been proposed in statistical process control texts. This idea was first proposed by Menzefricke and its parameters were considered unknown. In this paper, for the first time, statistical-economic design of control charts for monitoring the process mean with known standard deviation based on Bayesian predictive distribution is presented. According to the results presented in the article, regarding the superiority of statistical-economic design over economic design and also the superiority of Bayesian approach over the classical approach in designing control charts, it is suggested to use Bayesian control chart with statistical-economic design in controlling the process mean. 

Abstract This article deals with modelling and analysis of the competing risks for a one-shot device under a constant stress accelerated life test. In a reliability analysis of a device, it is important to be able to identify the main causes of failure. Therefore, a competing risk model is generally used. We consider this model in two modes: observed and masked causes of failure. The data obtained from one-shot device testing are missing in fact. For this reason, the EM algorithm along with the Fisher scoring method are used to estimate the model parameters. An accelerated life test is also used to shorten the time and cost. In addition, in order to accurately estimate the product reliability, the test design is finally optimized. Based on the simulated study, it is concluded that the EM algorithm and the bootstrap confidence interval are more accurate than the other methods. Also, shortening the test length leads to achieve an optimal test design.

Abstract Purpose: The purpose of this study is to develop a model for identifying and prioritizing optimal quality management strategies in small and medium-sized enterprises operating in Iran's ceramic and tile industry, with a focus on enhancing competitive advantage in export markets.
Methodology: This research employs an integrated multi-phase fuzzy approach. First, 29 quality management strategies were extracted from the literature and screened using the Fuzzy Delphi Method. Next, evaluation criteria were weighted using the Fuzzy SWARA (FSWARA) method. Finally, the selected strategies were prioritized using the Fuzzy COPRAS (FCOPRAS) method.
Findings: The results indicated that the key criteria for quality management include continuous process improvement, responsiveness to customer needs, and process innovation. In addition, strategies grounded in internal organizational factors, such as leadership styles, teamwork, and process orientation, proved more effective than those grounded in external factors, such as supplier management.
Originality/Value: By integrating three fuzzy decision-making methods under uncertainty, this study offers a comprehensive and precise model for identifying and ranking quality management strategies. Its emphasis on SMEs in the Iranian ceramic and tile industry, along with its methodological integration, distinguishes it from previous studies.