Journal of Quality Engineering and Management

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 Couplings are widely used in the industry and this equipment are always subject to defects and failures due to continuous rotation. Vibration analysis is a suitable technique for failure analysis and failure detection of rotating equipment. The purpose of this research is to analyze the failures that occurred in a coupling, whose data was collected in normal state and three failure states with four sensors connected to the coupling. For this purpose, two different types of feature extraction have been used, and seven machine learning algorithms and one deep learning algorithm have been used to classify situations. In this research, the performance of each of the implemented algorithms and the importance of extracted features have been investigated, and the role of sensors and their importance to reduce the number of sensors have been investigated. From the results of this research, we can point out the high importance of the features of the frequency domain in the accuracy of the implemented models, as well as the high efficiency of two sensors for classification.

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.

Abstract Knowing the real costs of a project, correctly estimating them and identifying the risks involved in resource allocation for projects such as the construction of a petrochemical unit whose resources are expensive or difficult to access, is of particular importance. By using costing methods in conditions of risk and uncertainty, more reliable cost information can be obtained. By examining past studies and researches in the field of costing, it was found that their focus was mostly on carrying out activities, and less attention was paid to the risk factor. In this article, the use of the ABC approach, which focuses on identifying activities and cost centers, has been discussed in the form of a mathematical model whose purpose is to minimize the risk of resource allocation to activity centers based on the desired limitations in costs and resources. Due to the fact that the numerical values of the parameters in the general model, such as cost and time, have uncertainty, risk-based resource allocation modeling has been considered. To test the proposed model, the main activities of the construction and installation stage of a petrochemical unit were considered, and after collecting the necessary data, the steps of the compiled model for resource allocation and the steps of the activity-based costing approach have been implemented. The results show that there is a difference between the costs calculated based on the allocation of resources of the proposed model for the activity centers and the costs estimated according to the past data.

Abstract In time series analysis, ignoring outliers leads to misidentification of the model, biased estimation of parameters, and poor predictions. One of the reliable non-parametric methods in predicting and improving the quality of multivariate time series modeling is the multivariate Singular Spectrum Analysis (MSSA) technique, which does not require any initial assumptions. The presence of outliers affects the Frobenius norm of matrix and reduces the efficiency of the MSSA method. In this research, a new version of MSSA based on the L₁- norm is proposed. Then the performance of this method is compared with basic MSSA using simulation studies and real data.

Abstract Determining the Factors Influencing the Prediction of Helicopter Rotor Failures

Purpose: The purpose of this paper is to investigate and identify the variables that influence the occurrence of helicopter accidents caused by different types of rotor failures. These crucial factors include flight conditions, maintenance conditions, and helicopter configuration. With this approach, accidents can be investigated more effectively and flight safety can be significantly improved.

Methodology: By analyzing 135 rotor faults accident from a comprehensive dataset containing 5652 helicopter-related accidents, eight classes of rotor faults were identified. Based on expert surveys and a review of studies in the field of helicopter accidents, nine features were proposed as crucial factors to such accidents. The significance of these factors was assessed using five feature selection methods. The input features included maximum takeoff weight, flight hours since the last inspection, type of last inspection, engine power, flight hours, altitude, wind speed, wind direction, and flight phase. Five well-known feature selection techniques—Correlation Matrix, Extreme Gradient Boosting (XGBoost), Mutual Information, Deep Learning, and Neural Network—were employed to identify the most essential factors.

Findings: "Maximum weight", "helicopter engine power", "flight phase" and "flight hours" were identified as variables with the highest degree of importance in predicting faults class of helicopter rotor, which also have a strong and acceptable justification in flight mechanics.

Originality/Value: The distinction of the present study from similar works lies in the inclusion of a broader range of variables, such as flight conditions and helicopter configuration, in contrast to previous studies that considered only a limited set of variables. By prioritizing these variables, the findings pave the way for proactive measures to prevent rotor faults, aiming to enhance prediction accuracy, reliability, and flight safety.

Abstract Purpose: The vaccine cold supply chain, as a quality-sensitive service–operational system, plays a critical role in ensuring timely delivery, maintaining vaccine efficacy, and minimizing wastage. The occurrence of operational and disruption risks intensifies process variability, undermines system reliability, and degrades service quality. The objective of this study is to develop a quality-oriented framework for the modeling and optimization of the COVID-19 vaccine cold supply chain, with a particular emphasis on quality-related performance indicators under conditions of uncertainty.

Methodology: In this study, a novel multi-period and multi-product simulation–optimization framework is developed to support decision-making in vaccine inventory management, allocation, and distribution under operational and disruption risks. The proposed approach integrates agent-based simulation with optimization techniques. The simulations are configured based on scenarios involving transportation disruptions and vaccine supply disruptions and are benchmarked against a disruption-free case.

Findings: The results are evaluated using several key performance indicators, including the expected vaccine delivery time, service level, vaccine wastage due to vehicle failures, and financial metrics. The simulation results indicate that the disruption-free scenario achieves the highest service level (0.82) and greatest degree of performance robustness, whereas transportation disruptions result in the spoilage of 8.9 million vaccine doses, and vaccine supply disruptions lead to the lowest service level (0.76). Statistical validation using the paired sign test further confirms the significance of these differences at the 95% confidence level.

Originality/Value: The present study adopts a quality engineering perspective to analyze the vaccine supply chain as a service system sensitive to process variability and proposes a quality-driven risk management framework. The findings provide practical insights for policymakers to enhance system reliability, reduce quality-related costs, and improve the resilience of vaccine distribution systems under crisis conditions.