Journal of Quality Engineering and Management

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 Digital transformation, large amount of data, and high speed of production processes are among the most important characteristics of the ecosystem of the fourth industrial revolution. These characteristics have influenced the decision-making processes of business managers. In the fourth industrial revolution, all elements of business and production systems, including decision support systems, will be affected by digitization and the high speed and accuracy of new technologies. One of these important pillars is quality management. Digitized quality management is called the Quality 4. Design of experiments as an active approach in quality management plays an important role in improving the quality of products and even processes. This powerful tool has been influenced by the changes that occurred in the fourth industrial revolution and has tended towards the design of intelligent experiments. This research aims to show with a comprehensive review in the literature how the design of experiments can provide a special role in meeting the requirements of customers in the fourth industrial revolution and the fourth quality. In this research, firstly, the changes made in the methods and design plans of the experiments, then the possible changes in the activities of the basic process of the implementation of the design of the experiments have been redefined according to the effects of new technologies. At the end, suggestions for future research have been proposed.

Abstract Nowadays, statistical analysis and monitoring of networks and early detection of anomalies with a significant growth rate have received more attention than before in recent years. In the real world, there is a wide range of networks analyzed and improved through network monitoring solutions, such as transportation, supply-demand, financial exchanges, health care, as well as the social ones, the analysis of the results can be beneficial to the stakeholders. The basis of the research is on identifying and solving the real problem. In other words, a real problem is identified in the country and a methodology is developed to solve it. The case study is the monitoring of a network of centers that provide emergency services in cities. The nature of this network is dynamic, feature-based, directed and weighted. The results of this study show that by modeling complex systems as a network and its continuous monitoring, abnormal situations can be identified and managed early and crises in cities can be prevented.

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 As time goes on and crises increase in societies, organizations are increasingly exposed to disruption. These crises can be of natural (such as earthquakes, floods, and fires) or human (such as terrorist attacks, infectious diseases, and intentional or inadvertent employee errors). Therefore, organizations need to be resilient to protect themselves from harmful consequences. The basic aspect of resilience involves the ability of an element to return to normal after disruption and resource allocation. Obviously, in any organization, the primary goal is to allocate the least resources to recover operations and to bring activities back to the tolerance threshold so that destructive events do not stop vital activities. In this paper, a quantitative model for resource allocation is presented, which minimizes the lack of resilience. The problem has a basic assumption, that there is a shortage of resources in at least one of the available resources due to excessive demand. After solving the model by numerical experiment, the results of the model were described and it was found that destructive events were retrieved before the tolerance threshold.

Abstract Nowadays one of most important organizations’ goals is to access compatibility to increase their market share and higher profitability. Therefore, it can be claimed that compatibility is considered as one of the most general organizations’ and industries’ outputs. The purpose of writing the current paper is to represent a model to measure carpet industry compatibility emphasizing quality and innovation. Statistical society in qualitative part includes 14 top managers and 582 employees in quantitative part from which 232 ones were selected as statistical sample. First of all, by the research model was obtained with three main dimensions resources and capabilities, quality and innovation with 30 indices through semi-structured and unstructured interviews with experts and during the coding process. Meanwhile resources and capabilities includes spiritual capital, human capital and technological capabilities, quality dimension includes raw materials, pre-production phase and production phase and finally innovation dimension includes product innovation, process innovation and sale innovation.

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: 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.

Abstract In this paper, firstly, a comprehensive and detailed model is presented to define the technical design requirements of engineering systems to be designed in design offices. From a theoretical point of view, it has been tried that the proposed model includes the general categories of technical requirements presented in international and national standards, as well as important references in the field of system engineering. From a practical point of view, the presentation of this model is based on the practical experience of defining the technical requirements for the design of a variety and a large number of complex electrical, electronic, computer, telecommunication, mechanical, and electromechanical systems. Also, as an example in this paper, the results of the definition of technical requirements for a type of Global Maritime Distress and Safety System, GMDSS, as one of the essential telecommunication subsystems of a type of surface vessel according to the proposed model, are briefly presented. Therefore, the proposed model can be used to define the technical requirements of a wide range of engineering systems in design offices.

Abstract Recent years, the insurance industry has been experiencing an increase in equipping insurance companies with fraud detection systems. Furthermore due to the significant cost imposed on the insurance industry by the rise in such claims, the role of data mining techniques in detecting fraudulent claims has become widespread. However an essential issue with such systems is the quality of their outputs. On one hand, supervised algorithms are more accurate comparing to unsupervised counterparts. On the other hand, as data labeled fraud is really limited, the efficiency of supervised algorithms is severely challenged. Within this regard, a novel approach is introduced as “alternative feature” to overcome the challenge. Basically, alternative feature is a variable whose values are available and can be considered a suitable indicator to detect suspicious cases. This approach improves the efficiency of the system and allows experts and insurance companies to investigate suspicious cases with more confidence and less error.

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.