Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 15 3 2025 11 22 Regression analysis of low beta anomaly in a stochastic portfolio with real market data Regression analysis of low beta anomaly in a stochastic portfolio with real market data 247 257 227176 10.48313/jqem.2025.532332.1562 FA Soheila Mirzaei Department of Mathematics and Computer Science, Faculty of Statistics, Allameh Tabatabaei University, Tehran, Iran. 0009-0002-7726-535X Shokoufeh Banihashemi Department of Mathematics and Computer Science, Faculty of Statistics, Allameh Tabatabaei University, Tehran, Iran. 0000-0002-6947-0284 Journal Article 2025 07 04 Purpose: This research aims to empirically analyze the low beta anomaly within the framework of random basket theory using linear and quantile regression. This financial anomaly refers to the higher long-term returns of a portfolio of low-beta stocks than of a portfolio of high-beta stocks. This study examines the excess growth rate generated in a random portfolio based on this financial anomaly. The statistical population of this study consists of 8 stocks from the US stock market during the period 2015 to 2023. Methodology: To achieve the research objectives, a continuous-time dynamic model with analytical solutions is proposed. To find its optimal weights or strategies, the "functionally generated portfolios" approach and the concept of "generating functions" are used. Finally, a regression analysis of US stock market data is conducted to examine the growth rate generated in this model. Findings: The results show that investors are always trying to increase their investment returns by adopting an appropriate method. In this regard, higher returns from low-beta investment portfolios have been observed over the past few decades, and the use of random portfolios as a reasonable method to examine the portfolio's excess return in this financial anomaly is thus crucial. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Originality/Value:<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;"> Given the innovative nature of this research in using stochastic portfolio theory to examine the excess growth rate generated based on the low beta anomaly, the results can help investors construct optimal portfolios with higher long-term returns. Purpose: This research aims to empirically analyze the low beta anomaly within the framework of random basket theory using linear and quantile regression. This financial anomaly refers to the higher long-term returns of a portfolio of low-beta stocks than of a portfolio of high-beta stocks. This study examines the excess growth rate generated in a random portfolio based on this financial anomaly. The statistical population of this study consists of 8 stocks from the US stock market during the period 2015 to 2023. Methodology: To achieve the research objectives, a continuous-time dynamic model with analytical solutions is proposed. To find its optimal weights or strategies, the "functionally generated portfolios" approach and the concept of "generating functions" are used. Finally, a regression analysis of US stock market data is conducted to examine the growth rate generated in this model. Findings: The results show that investors are always trying to increase their investment returns by adopting an appropriate method. In this regard, higher returns from low-beta investment portfolios have been observed over the past few decades, and the use of random portfolios as a reasonable method to examine the portfolio's excess return in this financial anomaly is thus crucial. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Originality/Value:<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;"> Given the innovative nature of this research in using stochastic portfolio theory to examine the excess growth rate generated based on the low beta anomaly, the results can help investors construct optimal portfolios with higher long-term returns.

https://www.pqprc.ir/article_227176_43b9e6bd98c48ea590a46b825d2d0fa0.pdf

Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 15 3 2025 11 22 The use of quality benchmarking deployment to achieve world-class performance in pharmaceutical services for rare diseases The use of quality benchmarking deployment to achieve world-class performance in pharmaceutical services for rare diseases 258 270 228074 10.48313/jqem.2025.535761.1565 FA Mohsen Shafiei Nikabadi Department of Industrial Management, Faculty of Economics, Management and Administrative Sciences, University of Semnan, Semnan, Iran. 0000-0002-9744-960X Mojtaba Pourbagherian Department of Industrial Management, Faculty of Economics, Management and Administrative Sciences, University of Semnan, Semnan, Iran. 0000-0002-6240-2190 Maryam Eshghali Department of Industrial Management, Faculty of Economics, Management and Administrative Sciences, University of Semnan, Semnan, Iran. 0009-0008-2322-6973 Journal Article Purpose: The pharmaceutical services sector is vital in all countries for two reasons. First, it concerns human lives, and in all societies, human capital is one of the most essential assets of a country. Second, it is due to the high financial turnover in this industry. In recent years, many advances have been made in the pharmaceutical industry. Still, the most essential problem is the lack of a clear, logical solution for identifying patients' needs, especially those with rare diseases. This research aims to identify the priorities of medical services for rare diseases in Iran, in comparison with the best in this field worldwide, and to achieve world-class standards and prioritize these needs. Methodology: This study is an applied research that combines the approaches of quality function deployment and benchmarking under the title of quality benchmarking development. First, quality requirements are collected from patients, doctors, and pharmacists; then, by comparing top pharmaceutical companies, the relationship between requirements and quality elements is analyzed; and finally, the weighted importance of each element for improving pharmaceutical services is determined. Findings: The results of the study showed that the five factors that have the highest priority in the field of pharmaceutical services in Iran are, respectively: improving the quality of drug production, empowering medical personnel, improving the quality of drug distribution, promoting medical services for rare diseases, and improving supervision of drug production. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Originality/Value:<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;"> This study presents a structured approach for identifying and prioritizing the needs of patients with rare diseases within Iran's pharmaceutical service system. The main innovation of this research lies in the simultaneous integration of the voice of the customer (patients, physicians, and pharmacists) with benchmarking against leading global pharmaceutical companies, enabling the identification of performance gaps and the determination of key factors for enhancing the quality of pharmaceutical services. Purpose: The pharmaceutical services sector is vital in all countries for two reasons. First, it concerns human lives, and in all societies, human capital is one of the most essential assets of a country. Second, it is due to the high financial turnover in this industry. In recent years, many advances have been made in the pharmaceutical industry. Still, the most essential problem is the lack of a clear, logical solution for identifying patients' needs, especially those with rare diseases. This research aims to identify the priorities of medical services for rare diseases in Iran, in comparison with the best in this field worldwide, and to achieve world-class standards and prioritize these needs. Methodology: This study is an applied research that combines the approaches of quality function deployment and benchmarking under the title of quality benchmarking development. First, quality requirements are collected from patients, doctors, and pharmacists; then, by comparing top pharmaceutical companies, the relationship between requirements and quality elements is analyzed; and finally, the weighted importance of each element for improving pharmaceutical services is determined. Findings: The results of the study showed that the five factors that have the highest priority in the field of pharmaceutical services in Iran are, respectively: improving the quality of drug production, empowering medical personnel, improving the quality of drug distribution, promoting medical services for rare diseases, and improving supervision of drug production. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Originality/Value:<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;"> This study presents a structured approach for identifying and prioritizing the needs of patients with rare diseases within Iran's pharmaceutical service system. The main innovation of this research lies in the simultaneous integration of the voice of the customer (patients, physicians, and pharmacists) with benchmarking against leading global pharmaceutical companies, enabling the identification of performance gaps and the determination of key factors for enhancing the quality of pharmaceutical services.

https://www.pqprc.ir/article_228074_d4977537602a03fa6bc5d6a6251dc6ee.pdf

Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 15 3 2025 11 22 Optimization for enhancing the quality of the queueing model family {M/Er/1,r∈N} based on the cost function, probability of system stationary, and customer satisfaction under a finite time horizon Optimization for enhancing the quality of the queueing model family {M/Er/1,r∈N} based on the cost function, probability of system stationary, and customer satisfaction under a finite time horizon 271 280 228073 10.48313/jqem.2025.537574.1567 FA Shahram Yaghoobzadeh Shahrastani Department of Statistics, Payame Noor University, Tehran, Iran. 0000-0002-8794-2222 Amrollah Jafari Department of Statistics, Payame Noor University, Tehran, Iran. Iman Makhdoom Department of Statistics, Payame Noor University, Tehran, Iran. 0000-0002-2768-1024 Journal Article 2025 07 28 <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Purpose:<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;"> This study aims to determine the optimal model within the family of queueing models, where interarrival times follow an exponential distribution and service times follow an Erlang distribution, under a finite stopping time TTT. The significance of this research lies in its application to optimizing the performance of service systems using queueing theory. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Methodology: To select the optimal model, a cost function and a performance metric, namely the average customer satisfaction level, are first defined. Subsequently, a new index, named ORS, is introduced based on the cost function, average customer satisfaction, and the system's stability probability. The optimal model is identified as the one with the highest ORS value. Numerical analysis is employed to demonstrate the procedure for determining the optimal model. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Findings: The numerical results indicate that the ORS index is an effective criterion for evaluating and comparing different queueing models, enabling optimal model selection by incorporating multiple performance aspects. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Originality/Value: The main contribution of this research is the introduction of the ORS index as a novel and comprehensive measure for optimal model selection in queueing systems. This approach can enhance service system design and improve customer satisfaction levels in practical applications. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Purpose:<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;"> This study aims to determine the optimal model within the family of queueing models, where interarrival times follow an exponential distribution and service times follow an Erlang distribution, under a finite stopping time TTT. The significance of this research lies in its application to optimizing the performance of service systems using queueing theory. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Methodology: To select the optimal model, a cost function and a performance metric, namely the average customer satisfaction level, are first defined. Subsequently, a new index, named ORS, is introduced based on the cost function, average customer satisfaction, and the system's stability probability. The optimal model is identified as the one with the highest ORS value. Numerical analysis is employed to demonstrate the procedure for determining the optimal model. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Findings: The numerical results indicate that the ORS index is an effective criterion for evaluating and comparing different queueing models, enabling optimal model selection by incorporating multiple performance aspects. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Originality/Value: The main contribution of this research is the introduction of the ORS index as a novel and comprehensive measure for optimal model selection in queueing systems. This approach can enhance service system design and improve customer satisfaction levels in practical applications.

https://www.pqprc.ir/article_228073_22cd72023aad64581d4e92815e136dac.pdf

Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 15 3 2025 11 22 Rethinking strategic decision quality through big data: A decision architecture based on data quality, information quality, and information adoption Rethinking strategic decision quality through big data: A decision architecture based on data quality, information quality, and information adoption 281 303 232722 10.48313/jqem.2025.544451.1571 FA Soheila Khoddami Department of Business Management, Faculty of Management, Kharazmi University, Tehran, Iran. 0000-0001-6461-1351 Rasoul Nosrat Panah Department of Business Management, Faculty of Management, Kharazmi University, Tehran, Iran. 0000-0002-3713-9933 Journal Article 2025 01 27 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. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Originality/Value:<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;"> 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. 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. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Originality/Value:<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;"> 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.

https://www.pqprc.ir/article_232722_d7275bd2e8b0e3a9de56374913ccc0f3.pdf

Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 15 3 2025 11 22 Analysis of the demand forecasting process through the supply chain and comparison of the current and desired states in the book publishing Analysis of the demand forecasting process through the supply chain and comparison of the current and desired states in the book publishing 304 315 234161 10.48313/jqem.2025.550673.1576 FA Fatemeh Zahra Montazeri Morvarid Intelligent Industrial Systems Research Group, Iran. 0000-0001-5439-2167 Zahra Joorbonyan Morvarid Intelligent Industrial Systems Research Group, Iran. 0000-0003-3579-830X Habibeh Karimi Department of Research Center, Shiroud Municipality, Mazandaran, Iran. 0009-0009-8554-7008 Journal Article 2025 04 15 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. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Originality/Value:<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;"> 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. 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. <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Originality/Value:<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Garamond',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;"> 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.

https://www.pqprc.ir/article_234161_6fb56e8f31470b094d8ed0b25931bf66.pdf

Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 3134-0849 15 3 2025 12 01 Analysis of key factors of customer satisfaction with airline service quality and airline ratings: A combined VIKOR-DEMATEL approach Analysis of key factors of customer satisfaction with airline service quality and airline ratings: A combined VIKOR-DEMATEL approach 316 338 231146 10.48313/jqem.2025.533452.1564 FA Yousef Ramezani Department of Management, Faculty of Humanities sciences, University of Gonabad, Gonabad, Iran. 0000-0001-8094-0614 Amirhosein Okhravi Department of Management, Faculty of Humanities sciences, University of Gonabad, Gonabad, Iran. 0000-0002-9159-9746 Naemeh Jafari Department of Business Administration, Imam Reza University, Mashhad, Iran. Journal Article 2025 03 16 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. 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.

https://www.pqprc.ir/article_231146_f16887e28b74834cff07bcdc9a64c25c.pdf