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: This paper aims to estimate the parameters of the Weibull distribution using a genetic algorithm and compare its performance with traditional estimation methods.
Methodology: A simulation study was conducted under different sample sizes and censoring levels. The genetic algorithm was applied to maximize the likelihood function.
Findings: The results show that the genetic algorithm provides more accurate and stable parameter estimates compared to the maximum likelihood method, especially in the presence of censored data.
Originality/Value: This study presents a novel application of genetic algorithms in reliability analysis, demonstrating their effectiveness in parameter estimation for censored datasets.

Abstract Purpose: This paper aims to compare and examine the statistical properties of four common estimators of process standard deviation for grouped data in statistical quality control.
Methodology: To achieve the research objectives, the bias and the Mean Squared Error (MSE) of the estimators will first be presented. Then, the estimators will be compared based on their MSEs.
Findings: It is shown that two estimators out of four estimators belong to two different classes of linear unbiased estimators with the minimum variance. Furthermore, numerical calculations show that the estimator based on the arithmetic mean of the group standard deviations is more efficient than the other estimators.
Originality/Value: Based on the results obtained in this study, it is suggested that for estimating the standard deviation of the process in grouped data, an estimator based on the arithmetic mean of the standard deviations of the groups should be used instead of estimators that are based on the arithmetic mean of the ranges of the groups.

Abstract Purpose: This paper examines how blockchain technology can improve reliability and operational performance in hospital pharmaceutical supply chains with a focus on inventory variability and responsiveness to demand.
Methodology: A system dynamics model of a three-echelon chain (manufacturer–distributor–hospital) is developed. Two information-sharing scenarios are compared: a traditional setting with centralized, delayed information and a blockchain setting with real-time, decentralized data sharing.
Findings: Results indicate that blockchain adoption enhances behavioral stability, reduces the persistence of hospital backlog, and shortens mean delivery lead time. Specifically, mean lead time decreases by ~15.1% and mean hospital backlog decreases by ~15.8% (both statistically significant). However, the difference in mean hospital inventory is not significant; stability improves, with inventory SD decreasing by ~21.5% and lead-time SD decreasing by ~10%. Taken together, these effects strengthen service reliability and overall supply-chain performance.
Originality/Value: By integrating blockchain-based decentralized data sharing with system dynamics modeling in the hospital pharmaceutical context, this study provides quantitative evidence of how transparency supports quality-oriented supply chain management.