Journal of Quality Engineering and Management

Abstract Purpose:  Given the increasing complexity and uncertainty in today's organizational environments, making informed, timely, and flexible decisions is of paramount importance for ensuring the sustainability and long-term growth of organizations. Accordingly, the present study aims to identify and prioritize individual, group, organizational, technological, and environmental factors that influence the quality of managerial decision-making, thereby providing a practical framework for enhancing decision-making capacity and increasing organizational resilience. Methodology:  This study is applied in nature and adopts a descriptive-analytical approach. Initially, a comprehensive set of factors was extracted through a systematic literature review. Then, a five-member expert panel was formed to identify the evaluation criteria, and the required data were collected using intuitionistic fuzzy numbers. The relative weights of the criteria were calculated using the FUCOM method, and the alternatives were finally prioritized using an aggregated decision matrix based on the multi-criteria WASPAS method. Findings: Specialized knowledge is the most significant factor influencing the quality of managerial decision-making. It was followed by participative decision-making and data accuracy and reliability, which ranked second and third, respectively. In contrast, factors such as organizational structure and the legal, cultural, and social environment had the least impact. Overall, these results highlight the importance of focusing on internal and controllable factors to enhance organizational sustainability in dynamic environments. Originality/Value: This research is among the first to provide a comprehensive prioritization of individual-to-environmental factors affecting the quality of managers' decisions by integrating a systematic literature review, intuitionistic fuzzy numbers, the FUCOM method, and the WASPAS method. The resulting framework not only enriches theoretical understanding but also serves as a practical guide for policymakers and managers in optimal resource allocation.

Abstract Purpose: In statistical data analysis and modeling, assessing the similarity or divergence between two probability distributions is of great importance. One of the most widely used metrics for this purpose is the Kullback-Leibler (KL) divergence, which quantifies the informational distance between distributions. This study aims to analyze the KL divergence between two normal distributions with equal variance and to compare the performance of different estimation methods for this measure. Methodology: In this study, the exact value of the Kullback–Leibler divergence between two normal distributions with equal variance is first analytically derived, and then three estimation methods (maximum likelihood, Bayesian, and shrinkage) are proposed to estimate this measure. The performance of each estimator is evaluated via Monte Carlo simulations using the Mean Squared Error (MSE) criterion. Findings:  The simulation results indicate that the Bayesian estimator outperforms the MLE in terms of estimation accuracy. Furthermore, the shrinkage estimator performs best, achieving the lowest MSE among the three methods. This argument suggests that incorporating prior information or penalization techniques can significantly improve estimation quality. Originality/Value: This study contributes to the literature by providing a detailed comparison of classical and modern estimation techniques for KL divergence in the context of normal distributions with equal variance. The novelty lies in integrating shrinkage methodology and demonstrating its superior performance, which is quantitatively validated through simulations. The findings have practical implications across fields such as machine learning, signal processing, and information theory.

Abstract Purpose: The purpose of this paper is to address one of the most critical challenges faced by organizations today: controlling carbon dioxide emissions. This study aims to provide a model for designing a green supply chain network that minimizes total network costs while incorporating environmental considerations. The research seeks to achieve a balanced optimization of costs, carbon emissions, and service levels in supply chain management.
Methodology: This study proposes a novel integrated optimization model that considers economic, environmental, and customer satisfaction aspects within the supply chain network. The mathematical model is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem. An exact method is employed to solve the model, which is coded and implemented using GAMS optimization software. The efficiency and effectiveness of the model are validated through numerical examples and data analysis.
Findings: The results demonstrate the model's ability to optimize both economic and environmental dimensions while maintaining high service levels and customer satisfaction. The numerical examples, solved for problems of varying dimensions, confirm the practicality and effectiveness of the proposed approach. The findings highlight the trade-offs between cost minimization, carbon emission reduction, and service quality in supply chain networks.
Originality/Value: This research contributes to the field by presenting a new integrated optimization model that simultaneously addresses cost efficiency, environmental sustainability, and customer satisfaction in green supply chain design. The use of a mixed-integer nonlinear programming approach and its implementation in GAMS provides a robust framework for solving complex supply chain problems. The study offers valuable insights for organizations aiming to achieve sustainability goals while maintaining economic viability and customer-centric operations.

Abstract Purpose: Reliability is one of the most critical quality characteristics of components, products, and systems. Unlike other attributes, it cannot be directly measured and is usually evaluated only after significant operational time under real conditions. However, waiting for long-term field data may reduce market competitiveness in commercial industries and pose serious safety risks in sensitive systems such as military equipment. Therefore, reliability prediction plays a vital role in key decision-making areas such as product release timing, warranty policies, and maintenance planning. This study aims to present an integrated model based on accelerated degradation testing and accelerated life testing to predict the lifetime of a turbine engine nozzle under operational conditions.
Methodology: Initially, the ADT was designed and conducted to monitor the degradation trend of the nozzle's critical feature at various temperature and time levels. Using the power-law and Arrhenius acceleration models, acceleration parameters and the activation energy were estimated. Subsequently, the ALT was performed under high-stress thermal conditions using the extracted parameters, and the corresponding failure times were recorded. Finally, by integrating the results of both tests and applying statistical methods such as maximum likelihood estimation and degradation path modeling, the system's lifetime distribution was modeled.
Findings: The implementation of the proposed model on a turbine engine nozzle demonstrated its ability to predict lifetime accurately and to reduce testing time and cost significantly.
Originality/Value: This model introduces a novel analytical framework that systematically combines two testing methods (ADT and ALT), with the output of one serving as input to the other. The proposed approach can be generalized and applied to other critical industrial and defense-related products.

Abstract Purpose: Given that determining the aggregate coefficient is a key constraint in Aggregate Production Planning (APP), this study seeks to identify the factors influencing this coefficient and to analyze them using Fuzzy Interpretive Structural Modeling (FISM) to explore their interrelationships. Methodology: After identifying the key factors influencing the aggregate coefficient, an Interpretive Structural Modeling (ISM) questionnaire was distributed among experts, and the responses were aggregated. Subsequently, the FISM steps were carried out. Finally, an interaction network was constructed, and an analysis was performed to evaluate the degree of dependence and driving power among the identified factors. Findings: The developed interpretive structural model comprised 11 hierarchical levels. The fuzzy analysis of dependence and driving power indicated that none of the factors were categorized as autonomous, reflecting a strong degree of interconnection among the variables within the model. Originality/Value: Production planning for multiple products utilizing shared resources is a complex challenge. Thus, analyzing the variables that influence the determination of the aggregate coefficient in production planning provides valuable insights, facilitating informed decision-making, particularly in optimizing resource allocation to achieve an optimal production level.

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.