Journal of Quality Engineering and Management

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

Abstract 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 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 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 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 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.