Journal of Quality Engineering and Management

Abstract Purpose: This study was conducted to investigate the relationships between Quality Management Practice (QMP), Industry 4.0 technologies, and organizational performance, and among them, the mediating role of customer satisfaction and technology was considered. The main objective of the study was to explain how the combination of quality and technology affects organizational performance improvement in Iranian manufacturing companies. Methodology: The study used a mixed approach, and the data were analyzed using structural equation modeling (PLS-SEM) and Artificial Neural Network (ANN). The statistical population comprised employees of Iranian manufacturing companies, and the data were collected via a valid questionnaire administered to 205 respondents. The research tool had five main variables, fourteen sub-components, and forty-five indicators. Findings: The results showed that QMP has a direct and significant effect on customer satisfaction and organizational performance. Also, Industry 4.0 technology and customer satisfaction played an effective mediating role in these relationships. Neural network analysis also indicates that customer satisfaction, process management, and data-centricity are most important for predicting organizational performance. The findings have collectively confirmed that combining QMP with new technologies can be an efficient strategy for improving organizational performance. Originality/Value: By combining the two methods, PLS and ANN, this research has presented an innovative approach for simultaneous analysis of causal relationships and nonlinear prediction. Also, by simultaneously examining the two mediating variables of customer satisfaction and technology and conducting the research in the local context of Iranian companies, it has covered the existing research gap and contributed to the development of the literature on quality management and digital transformation.

Abstract Purpose: This research investigates the impact of technical, relational, and business components of e-business processes on value creation in the digital supply chain, emphasizing the role of information sharing using a neural network modeling approach. The main focus is on the mediating role of e-business capabilities in enhancing the impact of these components on supply chain competitive performance.
Methodology: This research is applied and descriptive-correlational. The research population consists of experts, managers, and employees of manufacturing companies operating in the capital's industrial park. Sampling was carried out using a non-probability, available, and contingent method, and data were collected through a standard questionnaire, the validity and reliability of which were confirmed by the indices AVE> 0.5, CR > 0.7, and α > 0.7. To validate the model and test the hypotheses, the variance-based structural equation modeling method in SmartPLS version 4.0 and the artificial neural network module in SPSS 29 were used.
Findings: After fitting the research model with the variance-based structural equation approach and the multilayer perceptron neural network, the research findings showed that in both approaches, the information sharing variable had the highest impact, and both approaches were able to predict the competitive performance of the digital supply chain. To evaluate the models fitted using the two approaches, the root mean square error was used. The root mean square error values for the multilayer perceptron neural network approach and the variance-based structural equation approach are 0.021 and 0.879, respectively. Therefore, the multilayer perceptron neural network method can accurately predict the competitive performance of the digital supply chain with much lower error and can serve as an optimal model.
Originality/Value: This study presents an integrated model to explain the role of e-business process capabilities in enhancing the competitive performance of the supply chain. The findings offer practical guidance for strategic decision-making and planning in manufacturing firms, particularly within dynamic business environments.

Abstract Purpose: One of the most important competitive challenges for insurance companies these days is to provide services that can increase productivity with better quality. This research aimed to investigate the impact of productivity quality management indicators and artificial intelligence on increasing the productivity of service production in Pasargad Insurance.
Methodology: The statistical population of the quantitative part of this research includes all personnel working in the central building of Pasargad Insurance.  Due to the large size of the statistical population, a classified questionnaire based on the results of the qualitative phase of the research was prepared and distributed among the personnel to increase the generalizability of the results. The sample size of this study was initially estimated to be 1,300 people using the Cochran formula, and after final calculations, the final sample size was determined to be 297 people. Since this research was conducted using a survey method, the data were analyzed using descriptive and inferential statistical methods. Then, in the inferential statistics section, after determining the distribution of variables in the population, more advanced analyses were performed. For this purpose, structural equation modeling was used with Smart PLS software, as well as descriptive statistical tests to examine demographic data and analyze research variables in SPSS software.
Findings: According to the findings of this study, it can be concluded that combining productivity quality management indicators with modern artificial intelligence technologies plays a significant role in improving performance and increasing service productivity in the insurance industry, especially in companies such as Pasargad Insurance.
Originality/Value: Therefore, the productivity quality management model and artificial intelligence on increasing the productivity of service production in Pasargad Insurance presented in this research is a scientific and practical step towards moving the insurance industry towards technological transformation, organizational agility, and long-term competitiveness.

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 Purpose: Today, supply chain management and accurate demand forecasting are considered key factors in improving productivity, reducing operational costs, and enhancing flexibility across various industries. The printing and publishing industry, as one of the sectors highly influenced by demand fluctuations, requires efficient strategies for supply chain management and optimal resource allocation. The objective of this study is to examine the impact of supply chain management on demand forecasting in this industry and to analyze the differences between the current state and the desired conditions.
Methodology: This study adopts a quantitative approach and utilizes real sales and book demand data for analysis. Machine learning techniques (particularly feedforward artificial neural networks with the backpropagation learning algorithm) are used to model and forecast demand. The performance of these models is compared with that of traditional approaches, such as time-series models, to assess improvements in forecasting accuracy.
Findings:  The results reveal that machine learning models, especially feedforward neural networks, achieve higher accuracy in demand forecasting compared to traditional methods. Moreover, the application of these models reduces the bullwhip effect in the supply chain and enhances coordination among its members.
Originality/Value: By presenting a hybrid model integrating supply chain management and demand forecasting based on neural networks, this research introduces an innovative approach to optimizing decision-making in the publishing industry. The integration of machine learning techniques with supply chain analysis can serve as a foundation for developing intelligent solutions in inventory management and production planning across similar industries.

Abstract Purpose: This study aimed to identify the components of assessing the level of satisfaction with the quality of services provided by airlines and rank 5 airlines (Aseman, Ata, Iran Air, Zagros, and Mahan) based on the level of customer satisfaction with the quality of services provided.
Methodology: The research consists of two phases: first, identifying the components of customer satisfaction with airline service quality through a study of the subject literature and interviews with 15 experts (flight attendants and pilots). Step 2: Measure passenger satisfaction using two questionnaires completed by 30 frequent travelers. The DEMATEL technique was used to determine component weights and identify relationships among components, and the VIKOR technique was used to rank airlines. Data were analyzed using Excel and BT Vikor Solver.
Findings: Among the 20 components identified, the proportionality of the ticket price to the quality of service, the modernity of the aircraft, and the price of the ticket had the highest weight. Zagros, Ata, and Aseman airlines ranked first, Iran Air ranked second, and Mahan ranked third. In examining the relationships between components, aircraft modernity and up-to-date were identified as the most influential criteria, and services for disabled people were identified as the most influential criteria.
Originality/Value: By presenting a hybrid model of DEMATEL and VIKOR to identify and rank the components of airline service satisfaction, this research contributes to the promotion of airline managers' understanding of customers' needs and provides a suitable tool to improve service quality.

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 light of the growing challenges in today's supply chains, including market fluctuations, increasing environmental and social pressures, and the need to enhance resilience against foreseeable crises such as the COVID-19 pandemic and economic disruptions, the strategic importance of selecting suppliers that simultaneously meet sustainability and resilience criteria has become more prominent. Accordingly, the main objective of this study is to present a comprehensive, data-driven, and forward-looking decision-making model for evaluating and selecting suppliers within the supply chain, accounting for multiple dimensions of sustainability and resilience simultaneously.
Methodology: In the proposed model, the weights of the defined criteria and sub-criteria were initially determined using the Stochastic Best-Worst Method (SBWM). Supplier performance was then evaluated using the Stochastic VIKOR Multi-Criteria Decision-Making (MCDM) method. In the final stage, the Random Forest regression algorithm was applied to predict future supplier performance. The model was tested through a case study conducted at SAIPA Kashan Automotive Company using expert input collected via structured questionnaires.
Findings: Sustainability and resilience criteria play a central role in supplier selection in the automotive industry. Among the sub-criteria, "greenhouse gas emissions" and "energy consumption reduction" were most influential due to environmental regulations. At the same time, "cost" and "safety stock level" had the greatest impact due to their direct effect on economic performance and operational continuity. Furthermore, the Random Forest algorithm achieved high predictive accuracy (RMSE = 0.0976), confirming the model's ability to generate reliable, data-driven forecasts.
Originality/Value: Although each of the methods used in this research (Random Best-Worst Method, Random VIKOR, and Random Forest algorithm) has been employed individually in previous studies, the main innovation of this study lies in presenting an integrated framework that combines all three approaches. In fact, this research is the first to merge MCDM methods with a machine learning algorithm, offering a comprehensive, data-driven decision-making model. This model not only assesses the current performance of suppliers but also enables prediction of their future performance. Such a combination has not previously been introduced in the supplier selection literature with a simultaneous focus on supply chain sustainability and resilience in the automotive industry, marking a clear methodological innovation.

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: Multivariate and multi-state processes refer to types of processes that involve a large number of variables at each production stage, which may be interrelated. The objective of this study is to propose a novel approach for selecting, reducing, and defining new control variables in complex manufacturing processes, enabling more effective and efficient quality control.
Methodology: This study employs an applied, descriptive research methodology. Machine learning techniques and dimensionality reduction methods, such as Principal Component Analysis (PCA), are utilized, along with regression and correlation analysis. To evaluate the proposed method, a case study was conducted using real production data from the slab manufacturing process at Mobarakeh Steel Company in Isfahan.
Findings: The slab production process consisted of three main stages: furnace, secondary metallurgy, and casting. In each stage, the proposed method was applied to reduce the number of control variables. For instance, in the furnace unit, nine initial variables were grouped into three clusters, and correlation and PCA were applied within each group. Key variables were extracted, and experts validated the results. The findings indicated that this approach effectively reduces the number of quality-related variables.
Originality/Value: The novelty of this research lies in integrating machine learning and dimensionality reduction techniques to optimize quality control in multistage, multivariate processes. This method provides an effective tool for quality engineers and process analysts, particularly when traditional methods prove ineffective.