Journal of Quality Engineering and Management

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.

Abstract Purpose:  This research aims to identify and examine the relationship between the barriers to implementing Quality 4.0 in the Yazd tile and ceramic sector.
Methodology: A systematic review was employed to identify and categorize barriers, while the combined Fuzzy DEMATEL-ANP (FDANP) method was used in the quantitative phase to elucidate the relationships among these barriers and to prioritize them.
Findings: The qualitative analysis revealed 18 barriers across four categories. The Quantitative findings indicated that "High Cost of Implementation and lack of transparency in Return on Investment" emerged as the most causal barrier, while "failure to consider Quality 4.0 as a strategic issue and source of competitive advantage" was identified as the most consequential barrier to implementing Quality 4.0 in the Yazd tile and ceramic industry. Also, the key obstacles to the implementation of Quality 4.0 in the Yazd ceramic and tile industry include the lack of quantitative metrics for assessing the impact of Industry 4.0 on quality, the failure to consider Quality 4.0 as a strategic issue and a source of competitive advantage, the absence of advanced training programs for personnel, insufficient financial resources for implementing Quality 4.0, and the lack of stakeholder involvement in Quality 4.0 initiatives and projects.
Originality/Value: This study employs a hybrid Fuzzy DANP approach to support managers and decision-makers in strategizing for overcoming obstacles to the adoption of Quality 4.0. It achieves this by identifying barriers, determining their interrelationships, and prioritizing them.

Abstract Purpose: This study aims to explore and assess situations in international markets. In this context, the primary objective is to identify the factors influencing the gas market and analyze the internal dynamics of each factor. Subsequently, based on the recognized elements' factor-driven model, the interplay and behavior of these elements as parts of the model are investigated. Methodology: This research employs the problem structuring approach, also known as soft operational research, specifically the Graph Model for Conflict Resolution (GMCR) method. Additionally, in the quantitative part of this research, factor-based modeling is utilized. In this research, an attempt is first made to obtain a clear understanding of the gas market, and then a model is built based on this understanding. By identifying the key and leverage components of the factor-based model and simulating it over the long term, we generate possible scenarios or states. Findings: In the first step, key players in the gas market, including the United States, the European Union, Russia, China, India, Iran, Qatar, and the Renewable Energy Group, were identified, and the conflicts between them were modeled. The GMCR analysis identified 28 equilibrium points, which were subsequently clustered into five distinct scenarios. In the next step, an agent-based simulation model was developed based on these scenarios. Originality/Value: By integrating GMCR and an Agent-Based Model (ABM), this study has successfully addressed aspects of strategic conflict, market dynamics, and participants' gradual learning, which were previously overlooked in earlier research.

Abstract Purpose: This study aims to determine the optimal stock portfolio using a combination of the Markowitz model with Support Vector Machine (SVM), Data Envelopment Analysis (DEA), and the DBSCAN clustering algorithm. The statistical population consists of companies listed on the Tehran Stock Exchange from 2012 to 2022. Methodology: To achieve the research objectives and form an optimal stock portfolio, dimensionality reduction approaches, DEA, SVM, and the DBSCAN clustering algorithm were employed. Financial ratios derived from balance sheets, income statements, and cash flow statements, as well as composite financial ratios and risk-return analysis based on the hybrid Markowitz model, were used as inputs to construct four portfolios. Findings: The SVM method and the fourth approach, which includes the hybrid model, exhibited superior performance in optimizing the stock portfolio. Originality/Value: Given the innovation of this research in applying the hybrid Markowitz model, the results can assist investors and stock analysts in managing the quality of an optimal stock portfolio.

Abstract Purpose: This work addresses the automotive industry with two important, evolving concepts: sustainability and resiliency. The proposed model is designed to balance economic, environmental, and social objectives while maintaining adaptability to potential supply chain changes and disruptions. A real automotive company is then investigated as the case study to assess the applicability, validity, and performance of the developed model and, eventually, render useful managerial and decision aids. Methodology: To achieve this objective, a comprehensive decision-making model has been developed. In the first stage, supplier evaluation is conducted based on sustainability and resilience criteria. This assessment employs two innovative decision-making approaches: the stochastic fuzzy Best-Worst Method (BWM) and stochastic VIKOR. In the subsequent stage, a multi-objective mathematical model is formulated by incorporating stochastic-fuzzy uncertainty. To solve the model, a fuzzy robust optimization approach combined with a modified multi-choice goal programming method based on a utility function is applied. Findings: In this study, the supply chain of SAIPA Kashan Automotive Company is analyzed across three key dimensions: general criteria, sustainability, and resilience. Indicators such as cost, quality, and reductions in energy consumption were identified as the most critical evaluation factors. Supplier evaluation and ranking were carried out using the fuzzy VIKOR method. The results indicate that, among the main suppliers, the second and fifth options, and among the backup suppliers, the second option, received the highest scores. Furthermore, to assess the robustness and validity of the proposed approach, the results were compared with those obtained using conventional methods. Originality/Value: The value of this research lies in presenting a comprehensive decision-making model under uncertainty and in improving supply chain performance across economic, environmental, and social perspectives. The findings can significantly assist managers and policymakers in the automotive industry in addressing complex supply chain challenges.

Abstract Purpose: Sustainable economic growth is a key national priority, with bank loans serving as a critical driver by financing production units. However, rising Non-Performing Loans (NPLs) jeopardize economic stability and could trigger recessions. This study proposes an optimized loan allocation model for private banks, aiming to minimize NPLs while enhancing resource efficiency.
Methodology: Using statistical techniques, including stepwise multiple regression, panel data analysis, and logistic regression, the study examines loan disbursement data, NPL ratios, and their determinants across three dimensions: bank-specific, firm-level, and macroeconomic factors.
Findings: The capital surplus-to-assets ratio, capital adequacy, financial soundness, and equity ratios significantly reduce NPLs and enhance allocation efficiency. At the firm level, industry sector, credit history, loan purpose, and banking relationship history all directly shape default risk, with industry type and credit history being the most critical factors in determining credit risk. Macroeconomic variables, including government debt, unemployment, economic growth, and the share of loans in investments, also systematically influence NPL trends and banks' capacity to allocate resources.
Originality/Value: This research presents a comprehensive and actionable model for Iran's private banks, integrating multi-level indicators to optimize lending decisions and enhance credit risk management. The model equips bank managers with a strategic tool to improve operational efficiency and support economic stability.