production and operations management
Maghsoud Amiri; Seyyed Habibullah Rahmati; Masoud Taheri
Abstract
Supplier selection is a key issue in supply chain management. Today’s intense competition has forced organizations to adopt effective improvement paradigms, including lean, agile, green, resilient, and sustainable (LARGS). Integrating fuzzy logic with multi-criteria decision-making models enables ...
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Supplier selection is a key issue in supply chain management. Today’s intense competition has forced organizations to adopt effective improvement paradigms, including lean, agile, green, resilient, and sustainable (LARGS). Integrating fuzzy logic with multi-criteria decision-making models enables accurate responses to both qualitative and quantitative uncertainties. This study aims to evaluate and rank suppliers within the LARGS supply chain of the Firooz Hygienic Group. The research is qualitative–quantitative, inductive, and applied. Ten supply chain experts from the Firooz Group were selected through purposive sampling. Since decision-making involves risk and uncertainty, the Mamdani fuzzy inference system was applied, modeling each paradigm’s criteria with triangular membership functions. To optimize the rule structure, 42 effective and non-redundant rules were selected from 243 initial rules based on strength and coverage indices, reducing model complexity and improving inference accuracy. These final rules were combined with the Fuzzy TOPSIS method to rank suppliers.Results showed the performance ranking as follows: S7 > S5 > S1 > S6 > S8 > S3 > S2 > S4. Suppliers performed better in “agility” and “sustainability” and weaker in “resilience,” reflecting the current focus of the supply market in the detergent industry. The findings can assist supply chain managers in improving key LARGS indicators. Moreover, the proposed model can be adapted to other industries and service sectors by adjusting evaluation criteria and input variables according to operational conditions. Industry-specific calibration of variables, rules, and weighting schemes ensures model validity and decision accuracy across different contexts.
quality management
Pedram Esmaeilzadeh; Abolfazl Kazzazi; Amiri Maghsoud; Jahanyar Bamdadsoofi
Abstract
Despite the recognized importance of strategic alignment for competitiveness and quality management maturity for operational excellence, a significant literature gap persists in systematically integrating these concepts. This study addresses how to systematically align strategic alignment dimensions ...
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Despite the recognized importance of strategic alignment for competitiveness and quality management maturity for operational excellence, a significant literature gap persists in systematically integrating these concepts. This study addresses how to systematically align strategic alignment dimensions with quality management maturity. Using a systematic literature review and thematic analysis, 843 documents were screened, resulting in 47 articles for data coding and analysis. The primary contribution is the "Cross-mapping Table of Strategic Alignment and Quality Management Maturity Dimensions," which delineates interrelationships between six strategic alignment dimensions (skills, governance, value, communication, stakeholder participation, and scope) and seven quality management maturity dimensions (context, leadership, process management, resource management, performance analysis and evaluation, and improvement, learning and innovation). The research provides an integrated framework elucidating interaction mechanisms between these domains. Practically, it offers managers a diagnostic tool for assessing organizational status and developing simultaneous enhancement strategies for both quality maturity and strategic alignment. This framework fosters a paradigm shift in managerial approaches and facilitates leveraging existing platforms to strengthen the interaction between strategic and quality management systems, ultimately supporting sustained organizational excellence.
Industrial management
Asma Bakhtiari Tavana; Maghsoud Amiri; Amir Yousefli; Mohammad Taghi Taghavifard
Abstract
The transportation of biological specimens constitutes a challenging routing problem in healthcare logistics. Due to the perishable nature of specimens, adherence to transportation requirements regarding time, temperature, and physical conditions is essential. This problem focuses on route planning and ...
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The transportation of biological specimens constitutes a challenging routing problem in healthcare logistics. Due to the perishable nature of specimens, adherence to transportation requirements regarding time, temperature, and physical conditions is essential. This problem focuses on route planning and scheduling for the collection and transfer of specimens in the shortest possible time without compromising their quality. Despite the critical importance of this issue in healthcare systems and the publication of numerous studies, no systematic review has yet been conducted to provide a comprehensive overview of the current state of prior research. The present study, adopting a systematic literature review approach, seeks to identify, classify, and analyze the existing body of research to highlight research gaps and underexplored topics. Accordingly, after the design of a search protocol, retrieval, and screening of articles, 32 articles were finally selected and analyzed. The findings revealed that the development of dynamic and multi-objective models, the utilization of real-time decision-making, the broader application of innovative technologies such as drones, the Internet of Things, blockchain, and big data analytics, as well as the incorporation of machine learning algorithms, are among the most significant fields that could accelerate research progress in the field of routing and scheduling for biological specimen transportation. By mapping the current state and identifying research gaps, this systematic review provides a sound scientific foundation for future studies and for enhancing the efficiency of biological specimen transportation networks.
production and operations management
Aida Fallahpour Mobaraki; Mostafa Ebrahimpour Azbari; Maghsoud Amiri; keikhosro Yakideh
Abstract
The catch-up process plays a fundamental role in empowering companies and industries in a country to reduce the gap with global leaders. This study was conducted with the aim of designing and presenting a catch-up model for Iran's pharmaceutical industry. In terms of objective, this study is applied ...
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The catch-up process plays a fundamental role in empowering companies and industries in a country to reduce the gap with global leaders. This study was conducted with the aim of designing and presenting a catch-up model for Iran's pharmaceutical industry. In terms of objective, this study is applied research, while methodologically, it adopts a qualitative approach. Data were collected through literature review and semi-structured interviews with 15 experts from the pharmaceutical industry using snowball sampling technique. After integrating and synthesizing similar themes, the findings revealed 3 global themes (governance and policy-making, organizational requirements, and industrial network requirements), 9 organizing themes (supportive and developmental policies, regulation of laws and policies, governance risks and constraints, learning and knowledge absorption, production and product empowerment, organizational management and resources, market strategies and drivers, collaboration and interactions in industry, and industry infrastructure and environment), and 51 basic themes. Furthermore, to assess the reliability and validity of the research, Holsti's coefficient of 0.987 was obtained. Finally, the catch-up thematic network of Iran's pharmaceutical industry was presented. The results of this research can clarify the path for formulating effective and strategic policies and programs aimed at reducing the gap with industry frontrunners and developing pharmaceutical companies.
Industrial management
Maghsoud Amiri; HamidReza Talaie; Shahab Bayatzadeh
Abstract
In an era of intensifying global competition and unprecedented environmental, economic, and technological changes, organizations require a high level of readiness to adopt Industry 5.0-based technologies and business models. This study aims to investigate the impact of Industry 5.0 readiness on sustainable ...
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In an era of intensifying global competition and unprecedented environmental, economic, and technological changes, organizations require a high level of readiness to adopt Industry 5.0-based technologies and business models. This study aims to investigate the impact of Industry 5.0 readiness on sustainable business growth, considering the mediating roles of efficiency, responsiveness, and competitive advantage, with a case study of Mobarakeh Steel Company in Iran. Despite the growing body of research on Industry 5.0, a literature review indicates that the impact of Industry 5.0 readiness on efficiency, responsiveness, competitive advantage, and sustainable business growth has not yet been examined within an integrated framework. The research is applied in nature and follows a descriptive-correlational design. Data were collected using a standardized questionnaire from a purposive sample of 105 employees. The validity of the instrument, including content, convergent, and discriminant validity, as well as its reliability, was confirmed. The data were analyzed using structural equation modeling (SEM). The results revealed that Industry 5.0 readiness significantly enhances organizational efficiency and responsiveness. These two factors, in turn, strengthen competitive advantage and ultimately lead to sustainable business growth. Moreover, efficiency and responsiveness were found to mediate the relationship between Industry 5.0 readiness and competitive advantage. These findings offer practical guidance for industrial managers aiming to strategically transition toward Industry 5.0 and effectively leverage emerging technologies through a human-centric approach to achieve sustainable growth.
Introduction
The growing complexities of global supply chains, the imperative for sustainability, and the limitations of automation-focused paradigms have accelerated the shift toward Industry 5.0. Unlike Industry 4.0, which predominantly emphasizes automation and digitalization, Industry 5.0 integrates human-centricity, resilience, and sustainability as core values. Industry 5.0 readiness promotes a collaborative interface between humans and smart machines to enhance operational performance while considering social and environmental responsibilities. In this context, Industry 5.0 readiness emerges as a critical construct that reflects an organization’s capability to adopt, internalize, and benefit from emerging technologies such as artificial intelligence, collaborative robots, digital twins, blockchain, and big data analytics in a manner aligned with human and environmental values. While this concept has gained attention globally, empirical investigations into its impact on business outcomes remain limited. The steel industry, given its scale, energy intensity, and central role in economic development, represents a compelling sector for exploring Industry 5.0 transformation. Among leading firms in this domain, Mobarakeh Steel Company (MSC) in Iran has launched several digital transformation initiatives aligning itself with the broader agenda of Industry 5.0. This study examines the impact of Industry 5.0 readiness on sustainable business growth, considering the mediating roles of operational efficiency, organizational responsiveness, and competitive advantage. Despite the growing body of research on Industry 5.0, a literature review indicates that the impact of Industry 5.0 readiness on efficiency, responsiveness, competitive advantage, and sustainable business growth has not yet been examined within an integrated framework.
Methodology
This study employed a quantitative, applied, and correlational design to investigate the effect of Industry 5.0 readiness on sustainable business growth, mediated by efficiency, responsiveness, and competitive advantage. The target population comprised employees of Mobarakeh Steel Company, which has undertaken several initiatives aligned with Industry 5.0 principles. A structured questionnaire was designed to capture respondents' perceptions of their organization's readiness for Industry 5.0, its operational and strategic capabilities, and sustainable growth outcomes. The instrument included 17 items distributed across six constructs: Industry 5.0 readiness, efficiency, responsiveness, competitive advantage, and sustainable business growth. All items were measured on a five-point Likert scale, ranging from strongly disagree (1) to strongly agree (5). The sampling strategy was purposive, aimed at selecting employees involved in digital transformation initiatives or operational excellence programs. A total of 105 valid responses were collected. Despite the limitation of not including executive-level policymakers, the selected respondents possessed relevant knowledge of the technological and operational transformations within MSC. To ensure validity and reliability, several procedures were undertaken. Convergent and discriminant validity were assessed through Confirmatory Factor Analysis (CFA) using outer loadings, Average Variance Extracted (AVE), and the Fornell–Larcker criterion. The composite reliability (CR) and Cronbach’s alpha values for all constructs exceeded the accepted threshold of 0.7, indicating acceptable internal consistency. The hypothesized relationships were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 3.0.
Findings
The analysis of the structural model using PLS-SEM revealed several statistically significant relationships that validate the hypothesized impact pathways of Industry 5.0 readiness on sustainable business growth. The R² values for key endogenous variables, efficiency (0.57), responsiveness (0.54), competitive advantage (0.62), and sustainable business growth (0.66), indicate a good level of explanatory power for the model. The effect sizes (f²) were also moderate to strong, particularly for the paths from Industry 5.0 readiness to efficiency and responsiveness. The results confirmed that Industry 5.0 readiness has a significant and positive impact on both operational efficiency and organizational responsiveness. This finding aligns with Nazarian and Khan (2024), who demonstrated similar performance outcomes in European manufacturing contexts, and supports the idea that transitioning toward human-machine collaboration and real-time data systems yields operational improvements. In turn, efficiency and responsiveness were found to enhance competitive advantage, highlighting their mediating roles significantly. This corroborates Madhavan et al. (2024), who found that Industry 5.0-oriented capabilities in Thai marine SMEs improved competitive positioning through operational excellence. In the case of MSC, efficiency gains through AI-based predictive maintenance and responsiveness improvements via flexible scheduling systems contributed to a stronger market stance. Moreover, competitive advantage was shown to influence sustainable business growth significantly, suggesting that firms that achieve superior operational and strategic performance are more likely to maintain long-term viability and growth. This is consistent with studies by Alabi et al. (2025) and Bayatzadeh & Talaei (2024), who emphasized the link between technological transformation and long-term sustainability in industrial ecosystems. Importantly, the indirect effects of Industry 5.0 readiness on competitive advantage, through both efficiency and responsiveness, were also significant, confirming the partial mediating roles of these two capabilities. These results suggest that readiness for Industry 5.0 contributes not only to immediate performance benefits but also to longer-term strategic positioning, especially when internal capabilities are leveraged effectively.
Discussion and Conclusion
This study provides a nuanced exploration of how Industry 5.0 readiness contributes to sustainable business growth by enhancing efficiency, responsiveness, and competitive advantage, using the case of Mobarakeh Steel Company (MSC) in Iran. The study confirms the arguments of Nazarian and Khan (2024) that efficiency and responsiveness are critical conduits for the value generated by Industry 5.0 principles, such as smart automation and AI-human collaboration. It also aligns with the model proposed by Madhavan et al. (2024), showing that Industry 5.0 readiness can trigger significant organizational improvements when accompanied by complementary capabilities. Practically, the research illustrates how Industry 5.0 readiness serves as an enabler of sustainable growth, even in emerging economies where full deployment of Industry 5.0 technologies is not yet widespread. In the case of MSC, evidence from internal reports and strategic documents confirms the the Industry 5.0 readiness. Moreover, the results indicate that efficiency and responsiveness function as effective mediators, reinforcing the notion that performance benefits are not direct consequences of technology adoption, but instead are of the organization’s capacity to integrate and leverage such technologies. Looking ahead, these findings suggest that companies aiming to embark on their Industry 5.0 journey should focus not only on acquiring advanced technologies but also on developing internal capabilities, promoting a human-centric culture, and aligning operations with sustainability goals. Future research could explore cross-industry comparisons or longitudinal analyses to assess the evolution of Industry 5.0 readiness and its impact over time.
Industrial management
laya olfat; seyed sourosh ghazinoori; maghsood amiri; TAHEREH MIRHOSEINI
Abstract
The limited natural resources of the Earth have presented various industries, including the dairy industry, with increasing challenges in the sustainable and efficient use of resources. The high consumption of water, energy, and raw materials, as well as the significant production of waste—especially ...
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The limited natural resources of the Earth have presented various industries, including the dairy industry, with increasing challenges in the sustainable and efficient use of resources. The high consumption of water, energy, and raw materials, as well as the significant production of waste—especially organic waste—in this industry, has exposed it to environmental pressures. To improve organizational performance, new approaches for the optimal use of resources have been introduced, one of the most important of which is the concept of the circular economy. In recent years, researchers have attempted to combine this concept with other management frameworks to create an effective approach for enhancing organizational performance. In this regard, the present study aims to identify and model the concepts of the circular economy from the perspective of the EFQM 2020 Excellence Model in the dairy industry. For this purpose, first, the key indicators of the circular economy were identified using a systematic literature review and interviews with experts, and then these indicators were classified within the EFQM framework with the help of a panel of experts. Finally, confirmatory factor analysis was used to assess the validity of the designed model. The results indicate an appropriate fit of the model and meaningful relationships between the components. The findings of this study can serve as a model for establishing an excellence approach in the dairy industry based on a circular economy and can play an effective role in formulating national and industrial policies.IntroductionAs industrialization has progressed, the production sector has increasingly faced the challenge of diminishing non-renewable resources (Akhimien et al., 2021). Given the finite nature of these resources, it is crucial to adopt diverse strategies to maximize their use, as they are essential to maintaining economic activity. This has led to a growing focus on the interaction between economic and ecological systems. A new approach is needed to manage resource consumption and pollution to ensure the sustainability of both biological and socio-economic systems (Pourasghar et al., 2019). Historically, economies have operated on a linear model—based on extraction, production, and disposal. In contrast, the circular economy has emerged as a modern paradigm aimed at reducing the negative impacts of the linear model. It focuses on minimizing waste and maximizing resource efficiency (Suhaib & Fayaz, 2023). This concept promotes reducing, reusing, and recycling materials throughout production, distribution, and consumption processes to achieve sustainable development (Pourasghar et al., 2019). Given these considerations, this research seeks to identify and model key indicators related to the circular economy by reviewing relevant literature, including books, scholarly articles, and online resources, as well as conducting interviews with field experts. The identified indicators are then categorized within the EFQM 2020 Organizational Excellence Model. Finally, the model’s statistical validity is evaluated using confirmatory factor analysis, with input from industry managers.Research BackgroundResearch on the circular economy is still in its early stages, characterized by considerable diversity and fragmentation. Some studies focus on redesigning business models according to circular economy principles (Bocken et al., 2016), while others explore product design (Atlason et al., 2017) or examine strategic shifts and the identification of implementation barriers (Bjørnbet et al., 2021; Grafström & Aasma, 2021; Karali & Shah, 2022; Koide et al., 2022; Padilla-Rivera et al., 2021; Rincón-Moreno et al., 2021; Salmenperä et al., 2021). The expanding research in this area highlights the increasing integration of circular economy principles with other management frameworks, particularly in the realm of organizational excellence. The shift of manufacturing firms toward circular practices, coupled with substantial changes in industrial operations, has broadened the scope for research in this field. These transformations are not merely extensions of existing practices; they require fundamental shifts in organizational thinking, operations, and business logic. However, many organizations still struggle with how to implement circular economy principles effectively. This highlights the need for a comprehensive and practical model. The present study addresses this gap by proposing a model grounded in organizational excellence, specifically using the EFQM 2020 framework.MethodThis study employed a mixed-methods approach, combining a systematic literature review, expert interviews, and confirmatory factor analysis. The first phase involved conducting a systematic literature review to identify and explore the key dimensions and components related to circular economy excellence. Systematic reviews are essential for assessing the current state of knowledge in emerging research fields (Boland, 2013; Templier & Paré, 2015). In addition to the literature review, semi-structured interviews with field experts were conducted to gain a more nuanced understanding of circular economy principles and how they can be applied effectively. In the second phase, qualitative data were synthesized into a structured questionnaire. Quantitative data were then collected and analyzed to examine the relationships between the identified variables (Creswell, 2005). Confirmatory factor analysis was used to assess the validity of the indicators and to verify the overall structure of the proposed model.Discussion and ResultsThe findings suggest that the proposed model fits the data well, with all paths showing statistical significance. The model consists of three main components:Orientation: Organizational Culture and Leadership, Purpose and StrategyExecution: Stakeholder Engagement, Sustainable Value Creation, Performance ManagementOutcomes: Stakeholder Perceptions, Strategic and Operational ResultsKey performance indicators identified include waste management, the use of renewable energy, employee engagement, and circular product design. The model achieved a Goodness-of-Fit (GOF) index of 0.707, indicating strong empirical validity. By combining the EFQM 2020 framework with circular economy principles, this study presents a comprehensive model that supports both environmental and economic sustainability. The results emphasize the importance of fostering a culture of innovation, implementing green strategies, and maintaining effective communication with stakeholders. Additionally, the use of advanced technologies and information systems can enhance decision-making capabilities. The model offers a strategic roadmap for the dairy industry and other sectors within the food industry.ConclusionThis study provides a validated framework for implementing circular economy practices in the dairy industry, grounded in the EFQM 2020 organizational excellence model. By integrating these two approaches, organizations can drive continuous improvement and achieve long-term sustainability. Organizations are encouraged to adopt the identified indicators and take concrete actions to reduce waste, optimize resource use, and create sustainable value. Future research should explore the application of this model in other industries and test its effectiveness with larger and more diverse samples to increase generalizability.
Industrial management
Mohsen Keramatpanah; Mohammad Taghi Taghavifard; Maghsoud Amiri; Mehdi Mehdi Shamizanjani
Abstract
With the expansion of digital technologies and fundamental changes in the financial services ecosystem, the banking industry is facing new challenges on the path toward sustainable development. In this regard, the present study aims to propose a model for achieving sustainability in digital banking within ...
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With the expansion of digital technologies and fundamental changes in the financial services ecosystem, the banking industry is facing new challenges on the path toward sustainable development. In this regard, the present study aims to propose a model for achieving sustainability in digital banking within Iran’s private banking sector. To this end, key variables were initially identified through a review of the relevant literature and subsequently validated using the fuzzy Delphi method with input from subject matter experts. To analyze the relationships among the variables and determine their systemic structure, Interpretive Structural Modeling (ISM) was employed. Subsequently, a comprehensive digital banking sustainability model was developed and simulated using a system dynamics approach and related software tools. The results of the model analysis indicate that the development of technological infrastructure, enhancement of information security, improvement of customer experience, effective collaboration with regulatory bodies, and standardization of digital processes are key drivers in realizing sustainability in digital banking. Scenario analysis further reveals that simultaneous improvement of these components, by reinforcing positive feedback loops, can lead to sustainable growth and lasting competitive advantage for private banks. In this context, attention to sustainability requirements in digital banking and the development of related models can contribute to reducing operational costs in the banking network, optimizing energy consumption, increasing financial inclusion, and delivering more efficient services, thereby facilitating sustainable development. The primary contribution of this study is the development of an analytical and practical framework based on system dynamics modeling, which can support private banks in formulating strategies for sustainable digital transformation and serve as a roadmap for improving their economic, social, and environmental performance. The findings of this research can assist policymakers, banking executives, and researchers in the digital banking domain to better understand the factors influencing sustainability and to design effective interventions.IntroductionAs the banking industry experiences rapid digitalization driven by emerging technologies and evolving customer expectations, the necessity of aligning digital transformation with sustainability imperatives has become increasingly evident. This shift transcends traditional financial performance and calls for banking institutions to engage with social and environmental responsibilities while sustaining innovation. Particularly in developing economies such as Iran, the dual challenge of digital modernization and sustainable development presents a unique strategic frontier. In response to these dynamics, the present study proposes a comprehensive and dynamic model of sustainable digital banking, focusing on the private banking sector in Iran.Research BackgroundWhile digitalization and sustainability have been widely studied, there exists a significant gap in integrated models that account for the systemic interactions between the two domains. The prevailing literature tends to analyze digital banking from a technological perspective, often overlooking its environmental and social consequences (Del Carmen, 2022; Migliorelli & Dessertine, 2023). Similarly, sustainability-focused frameworks rarely include the disruptive potential of digital technologies such as blockchain, artificial intelligence (AI), and mobile banking. Some studies have suggested the relevance of digital sustainability in areas like waste reduction, financial inclusion, and eco-efficiency (Castro et al., 2021; Di Vaio et al., 2021), yet empirical applications remain sparse. By building on these foundations, this research aims to bridge conceptual and practical gaps by designing a policy-oriented, simulation-based model grounded in a system thinking approach.Materials and MethodsThis study adopted a hybrid methodology by integrating three complementary techniques—Fuzzy Delphi, Interpretive Structural Modeling (ISM), and System Dynamics (SD). Initially, a structured literature review spanning 2016 to 2023 was conducted using scholarly databases such as Scopus and Google Scholar. This led to the identification of 26 preliminary variables, which were refined and validated through expert consensus using the Fuzzy Delphi method. Experts were selected based on their experience in digital transformation initiatives and their roles in IT, innovation, or strategic planning within private banks. The Delphi rounds applied fuzzy logic to quantify agreement levels, ensuring rigor in variable selection. To structure the relationships among the validated variables, ISM was employed. This method facilitated the construction of a hierarchical framework to determine which variables serve as inputs, intermediaries, and outputs in the sustainability process. The final phase involved constructing a system dynamics model with feedback loops and flow diagrams to simulate the behavior of key sustainability indicators over time and under different scenarios. The sustainability model incorporated variables reflecting organizational, technological, social, and environmental dimensions. These include elements such as process improvement, trust, profitability, service quality, digital culture, smart leadership, electronic KYC, stakeholder satisfaction, employee welfare, and business model adaptability. By embedding these interconnected elements into the system structure, the model is designed to capture the complexity of digital sustainability transitions in a banking context.Data Analysis and FindingsThe system dynamics model as shown in Figure (1) was simulated across multiple scenarios to understand how different policy strategies would influence long-term outcomes. The simulation process ensured that model behavior aligned with historical data and logical expectations. Scenario analysis provided valuable insights into how banks can prioritize investments and strategic decisions to balance economic growth with sustainability. For instance, increasing investment in digital infrastructure and employee training led to noticeable gains in trust, customer acquisition, and organizational resilience. Similarly, enhancing authentication systems and E-KYC protocols improved operational efficiency while supporting environmental and social performance through reduced paperwork and increased accessibility. Findings confirmed that sustainability in digital banking is best achieved through a synergistic approach that combines internal cultural transformation with technological innovation and regulatory collaboration. Variables such as digital culture, smart leadership, and business model flexibility played a central role in facilitating systemic resilience. The results emphasize that merely focusing on digital upgrades is insufficient without aligning human capital and governance frameworks with sustainability objectives. Scenario comparisons further revealed that a balanced investment strategy targeting both operational excellence and environmental awareness yields the most robust outcomes.Figure 1:SD model ConclusionThe results of this research highlight the imperative for private banks in Iran and similar emerging markets to adopt integrated strategies that harmonize digital transformation with sustainability goals. This study makes a significant contribution by constructing and validating a system dynamics model that incorporates 26 interdependent variables derived from theory and practice, offering a holistic view of the sustainability landscape in digital banking. The model is unique in that it not only captures the technological enablers of sustainable banking but also embeds social and environmental dimensions, reflecting the true complexity of this transformation. The integration of Fuzzy Delphi and ISM methods with SD modeling allows for both structural clarity and dynamic simulation, enabling banks to test and adjust strategies before implementation. By embedding feedback loops and behavioral equations, the model delivers actionable insights into how various factors—such as trust, service quality, organizational agility, and stakeholder alignment—can collectively drive sustainability in the digital era. This comprehensive approach moves beyond linear planning, providing a real-time decision-making tool for managers, regulators, and researchers alike.Further Research IdeasFuture research can extend the current model by integrating real-time behavioral data through machine learning algorithms or agent-based modeling, offering more granular and predictive insights. Comparative studies across multiple banking systems or geographic regions would also enhance generalizability. In addition, introducing ESG indicators and green finance parameters could further enrich the environmental scope of the model. Finally, longitudinal studies using historical banking data can help validate the dynamic behavior of the system across different economic cycles.Managerial SuggestionsFrom a managerial perspective, several strategic recommendations emerge. First, banks should prioritize long-term investment in digital literacy and workforce development to foster a resilient culture aligned with sustainability values. Second, integrating advanced E-KYC and biometric verification tools can enhance customer trust and operational transparency. Third, collaborative governance involving fintechs, third parties, and regulatory bodies is essential for ecosystem resilience. Fourth, it is crucial to continuously monitor and update business models to reflect technological, societal, and environmental shifts. Finally, the adoption of sustainability-oriented performance metrics should be institutionalized to ensure that growth and digitalization are guided by ethical and ecological responsibility
supply chain management
Abolfazl Sadeghian; Seyed Mohammad Ali Khatami Firouzabadi; Laya Olfat; maghsoud Amiri
Abstract
Nowadays attending to closed-loop supply chain matter for survival in competitive circumstances not only has been become a controversial topic but also has been considered as a critical topic too. Close loop supply chain has combined to direct and reverse flow (method/ manner). This paper’s goal ...
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Nowadays attending to closed-loop supply chain matter for survival in competitive circumstances not only has been become a controversial topic but also has been considered as a critical topic too. Close loop supply chain has combined to direct and reverse flow (method/ manner). This paper’s goal is presenting a model for inventory control in closed loop supply chain by multiple objective approach. This research intends to reach it's main goal including reduce expenses such as production, maintenance, transportation in direct flow also decrease the waste material and defective in reverse flow and in conclusion increase the company’s profit by desingning and optimizing multiple objective model. Hence a double purpose model in closed-loop supply chain consists three classes direct flow in which conclude suppliers, manufactures and customers. Furthermore this consists four classes in reverse flow that concludes: collection centers, inspection, repair centers, recycling centers and disposal centers. According to the article’s model, which is multipurpose, linear and integer, At the beginning the model convert to single objective by Weighting and Constraint method and then is solved by using Branch and bound algorithm and Lingo software. Finally, the model extended in Iran Khodro Company as a study case and its function validated. Results and output of model solving demonstrate its capability to be useful for planning and inventory control in closed-loop supply chain.
Taher kouchaki tajani; Ali Mohtashami; maghsoud Amiri; Reza Ehtesham Rasi
Abstract
In this paper, we have proposed a model based on Mixed Integer Non-Linear Programming for the blood supply chain under conditions of uncertainty in supply and demand, from the stage of receiving blood from volunteers to the moment of distribution in demand centers. The challenges addressed in this optimization ...
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In this paper, we have proposed a model based on Mixed Integer Non-Linear Programming for the blood supply chain under conditions of uncertainty in supply and demand, from the stage of receiving blood from volunteers to the moment of distribution in demand centers. The challenges addressed in this optimization model are the reduction of blood supply chain costs along with minimizing the shortage and expiration rate of blood products. The Markov chain has been used to address the uncertainty of donor blood supply. To estimate the needs of medical centers, the received demand is considered fuzzy. Then, the proposed model is solved in small dimensions by GAMS software and in large dimensions by Bat and Whale meta-heuristic algorithms, and the results are presented. In addition, a case study is presented to show the applicability of the proposed model. The results show a reduction in the level of costs as well as a reduction in the shortage and expiration of blood products in the supply chain.IntroductionOne of the important topics researched in the global healthcare systems of different countries is the improvement of supply chain performance. The health system has one of the most complex and challenging supply chains due to its direct relationship with human lives. Issues such as uncertainty in blood demand and supply, blood inventory planning, delivery schedule, ordering time, attention to expiration date, and limited human resources are among the challenging issues in the field of health, especially the supply chain of blood and blood products. A unit of blood, from the time it is received from the donor to the time it is injected into the patient as whole blood or blood product, includes many processes and challenges that must be taken into account to ensure the health of the blood and the health of the supply chain. Redesigning an existing blood supply chain is not possible in the short term due to significant costs and time required, so using existing facilities and optimizing conditions is more preferable than reestablishing equipment, blood centers, and other facilities related to the blood supply chain. In this research, by presenting a mathematical model, we try to optimize the tools and facilities in a blood supply chain. The important goal in the blood supply chain is the cost factor. The costs incurred on the blood supply chain include costs such as blood collection from volunteers, product processing and blood inventory costs in hospitals and blood centers, and blood transfer costs to demand centers. On the other hand, the balance in storage and waste reduction is also very important in this chain. High storage increases the amount of inventory (increase in cost) and also increases the rate of perishability (increase in cost) of blood products. It is important to pay attention to the fact that the reduction of costs should be accompanied by the reduction of shortages and waste. In addition to the lack of blood, improper distribution and untimely supply of blood to hospitals can be completely disastrous. Requests to blood centers are made under certain conditions, such that the requested product(s) are separated in terms of blood group or the presence or absence of a specific antigen. Paying attention to blood groups and compatibility indicators is one of the principles of blood transfusion, and not observing them can cause unfortunate events.Due to the disproportionate percentage of distribution of blood groups among volunteers, there has always been a possibility of a shortage in the supply chain. In the medical world, in case of a shortage of a blood product of a certain group, attempts are made to replace that product from groups that can be matched. This will reduce the shortage and save the lives of patients whose blood with the required blood group and RH is not available at the same moment. In order to solve this challenge, in the upcoming research, a solution based on the versatility of unanswered demands will be considered, which will be included in the mathematical model. Another important issue is the age of the demand for the requested product, which creates an age-based demand in the supply chain. (Some special patients need fresh or normal products according to the type of disease.)MethodologyIn this research, a comprehensive mathematical model has been developed in the form of a MINLP model. The research model is based on a comprehensive blood supply chain consisting of three components: collection, processing, and consumption of blood products. There are three types of collection centers in this model: first, vehicles that serve blood donors at predetermined locations and collect blood; second, fixed collection facilities located in some areas of the city that solely perform the task of collecting blood; and third, blood centers (blood transfusion centers) that perform both blood collection work and other tasks related to product processing, testing, and transfer planning to demand centers and hospitals. The next part of the model is related to the processing of the collected blood. In this part, the blood collected by the collectors in the blood center is aggregated, the percentage of each blood group is determined, and according to the need in the blood centers, products such as red blood cells, platelets, and whole blood plasma are sent to hospitals. It is worth noting that as blood is converted into other products, some characteristics of the product, including the age of the products, differ from each other. Therefore, in the continuation of transferring the products and responding to their demand, the age of the blood product will be considered. Additionally, it should be noted that the blood product requested from the demand centers is in two forms. For some special patients and in special surgeries, a series of blood products with a certain age (young blood) are needed. Therefore, the importance of the age of the blood sent to the hospitals is also seen in the model. In the real world, in the face of a shortage in hospitals, a solution is thought out, which is to use the principle of adaptability of blood groups. Through a pre-accepted adaptability matrix, a series of demands for blood groups g, in case of shortage, can be satisfied with the supply of blood groups f turn around. Deterministic supply chain network design models do not take into account the uncertainties and information related to the future affecting the supply chain parameters and as a result cannot guarantee the future performance of the supply chain because due to the inherent and fluctuating and sometimes severe change in the environment of many operating systems Parameters in optimization problems have random and non-deterministic characteristics. In this research, two different approaches have been used to face the uncertainty in blood supply and demand values. For the demand, a triangular fuzzy approach has been proposed. According to the conditions of uncertainty, the appropriate alpha cut is selected based on the opinion of the decision-makers, and the demand is adapted to the conditions. Regarding the amount of supply, in order to estimate the number of donors in future periods, we have used the Markov chain to predict the number of donors based on the records in the past.FindingsIn order to evaluate the presented model, it is necessary to solve the research in both small and large sizes to determine the reaction of the research target function to changes in the parameters of the problem. For this purpose, the research model was first coded in GAMS 24.1 software. According to the designed sample problems, up to a certain size, it is possible to solve the problem within a certain time frame using GAMS software. However, as the size of the problem increases and the time to reach the answer also increases, meta-heuristic algorithms such as WOA and BAT were employed to solve this problem. The results indicate that the Whale Optimization Algorithm (WOA) performed better. Subsequently, based on a case study, a problem was presented to illustrate the efficiency of the model and its solution method. The results obtained for the objective function and the values obtained for the main variables of the research demonstrate the effectiveness of the model and its solution approach.ConclusionThe purpose of this article is to design a comprehensive supply chain that includes three parts: collection, processing, and distribution of blood products. The supply chain comprises mobile and fixed blood collection units that receive blood from donors and send it to blood centers. At these centers, blood is processed into required products and then distributed to demand centers based on demands categorized as fresh or normal products. In this research, the objective was to minimize costs such as blood collection, blood inventory in blood centers and hospitals, as well as the cost of blood products expiring due to non-use. To address blood deficiency, the blood compatibility system was incorporated into the model. This system ensures that if a certain product of a certain group is not available, a compatible product from another group is sent as a replacement. The model was solved using the exact solution approach of GAMS software for smaller-sized problems. However, for larger-sized problems, meta-heuristic algorithms such as WOA and BAT were employed to achieve reasonable solving times. Additionally, a fuzzy coefficient was proposed for relatively accurate demand prediction, and the Markov chain and the Kolmograph left-hand theorem were utilized to predict the number of blood donors. The results obtained from small-sized problems using accurate solver algorithms, as well as medium and large-sized problems using WOA and BAT meta-heuristic algorithms, demonstrate the efficiency of the designed model. Finally, a sensitivity analysis based on changes in fuzzy coefficients of demand and coefficients, including the alpha cut transformation function, and its effect on the objective function are presented.
maedeh mosayeb motlagh; Parham Azimi; maghsoud Amiri
Abstract
This paper investigates unreliable multi-product assembly lines with mixed (serial-parallel) layout model in which machines failures and repairing probabilities are considered. The aim of this study is to develop a multi-objective mathematical model consisting the maximization of the throughput rate ...
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This paper investigates unreliable multi-product assembly lines with mixed (serial-parallel) layout model in which machines failures and repairing probabilities are considered. The aim of this study is to develop a multi-objective mathematical model consisting the maximization of the throughput rate of the system and the minimization of the total cost of reducing mean processing times and the total buffer capacities with respect to the optimal values of the mean processing time of each product in each workstation and the buffer capacity between workstations. For this purpose, in order to configure the structure of the mathematical model, Simulation, Design of Experiments and Response Surface Methodology are used and to solve it, the meta-heuristic algorithms including Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Ranked Genetic Algorithm (NRGA) are implemented. The validity of the multi-objective mathematical model and the application of the proposed methodology for solving the model is examined on a case study. Finally, the performance of the algorithms used in this study is evaluated. The results show that the proposed multi-objective mathematical model is valid for optimizing unreliable production lines and has the ability to achieve optimal (near optimal) solutions in other similar problems with larger scale and more complexity.IntroductionA production line consists of a sequence of workstations, in each of which parts are processed by machines. In this setup, each workstation includes a number of similar or dissimilar parallel machines, and a buffer is placed between any two consecutive workstations. In production lines, the buffer capacity and processing time of machinery have a significant impact on the system's performance. The presence of buffers helps the system to maintain production despite possible conditions or accidents, such as machinery failure or changes in processing time. Previous research has investigated production lines without any possibility of machinery failure, referred to as "safe production lines." However, in real production lines, machinery failure is inevitable. Therefore, several studies have focused on "uncertain production lines,"assuming the existence of a probability of failure in a deterministic or exponential distribution. This research examines uncertain production lines with a combined layout, resulting from the combination of parallel deployment of machines within each workstation, if necessary, and serial deployment of workstations. The objective of this research is to determine the optimal values (or values close to optimal) of the average processing time of each product in each workstation, as well as the volume of buffers, as decision variables. The approach aims to maximize the system's output while minimizing the costs associated with reducing the processing time of workstations and minimizing the total volume of buffers between stations. Moreover, simulation can be applied without interrupting the production line or consuming significant resources. In this research, due to the high cost and time involved, implementing the proposed changes on the system is not cost-effective for investigating the changes in the production system's output rate. Therefore, the simulation technique has been utilized to optimize the production line.Research methodThe present study aims to develop a multi-objective mathematical model, based on simulation, to optimize multi-product production lines. In the first step, the structure of the multi-objective mathematical model is defined, along with the basic assumptions. To adopt a realistic approach in the model structure, the simulation technique has been employed to address the first objective function, which is maximizing the output rate of the production line. To achieve this, the desired production system is simulated. The design of experiments is used to generate scenarios for implementation in the simulated model, and the response surface methodology is utilized to analyze the relationship between the input variables (such as the average processing time of each product type in each workstation and the buffer volume between stations) and the response variable (production rate).ResultsTo implement the proposed methodology based on the designed multi-objective programming model, a case study of a three-product production line with 9 workstations and 8 buffers was conducted. Subsequently, to compare the performance of the optimization algorithms, five indicators were used: distance from the ideal solution, maximum dispersion, access rate, spacing, and time. For this purpose, 30 random problems, similar to the mathematical model of the case study, were generated and solved. Based on the results obtained, both algorithms exhibited similar performance in all indices, except for the maximum dispersion index.ConclusionsIn this article, the structure of a multi-objective mathematical model was sought in uncertain multi-product production lines with the combined arrangement of machines in series-parallel (parallel installation of machines in workstations if needed and installation of workstations in series). The objective was to determine the optimal values of the average processing time of each type of product in each workstation and the buffer volume of each station, with the goals of maximizing the production rate, minimizing the costs resulting from reducing the processing time, and the total volume of inter-station buffers simultaneously. To investigate the changes in the output rate of the production system, due to the high cost and time, it was deemed not cost-effective to implement the proposed changes on the system. Therefore, the combination of simulation techniques, design of experiments, and response surface methodology was used to fit the relevant metamodel. In the proposed approach of this research, taking a realistic view of production line modeling, the probability of machinery failure, as well as the possibility of repairability and return to the system, were considered in the form of statistical distribution functions. Additionally, all time parameters, including the arrival time between the parts, the start-up time of all the machines, the processing time, the time between two failures, and the repair time of the machines, were non-deterministic and subject to statistical distributions. Finally, to solve the structured mathematical model, two meta-heuristic algorithms (NSGA-II) and (NRGA) were considered.
maghsoud Amiri; mohsen shafiei nikabadi; Armin Jabbarzadeh
Abstract
In recent years, the complexity of the environment, the intense competition of organizations, the pressure of governments on producers to manage waste products, environmental pressures and most importantly, the benefits of recycling products have added to the importance of designing a closed loop supply ...
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In recent years, the complexity of the environment, the intense competition of organizations, the pressure of governments on producers to manage waste products, environmental pressures and most importantly, the benefits of recycling products have added to the importance of designing a closed loop supply chain network. Also, the existence of inherent uncertainties in the input parameters is another important factor that the lack of attention them can affect the strategic, tactical and operational decisions of organizations. Given these reasons, this research aims to design a multi-product and multi period closed loop supply chain network model in uncertainty conditions. To this aim, first a mixed-integer linear programming model is proposed to minimize supply chain costs. Then, for coping with hybrid uncertain parameters effectively, randomness and epistemic uncertainty, a novel robust stochastic-possibilistic programming (RSPP) approach is proposed. Furthermore, several varieties of RSPP models are developed and their differences, weaknesses, strengths and the most suitable conditions for being used are discussed. Finally, usefulness and applicability of the RSPP model are tested via the real case study in an edible oil industry.
pedram Pourkarim guilani; Mani Sharifi; parham azimi; maghsoud Amiri
Abstract
Due to the high sensitivity in applying of electronic and mechanical equipment, creating any conditions to increase the reliability of a system is always one of the important issues for system designers. Hence, making academic models much closer to the real word applications is very attractive. In the ...
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Due to the high sensitivity in applying of electronic and mechanical equipment, creating any conditions to increase the reliability of a system is always one of the important issues for system designers. Hence, making academic models much closer to the real word applications is very attractive. In the most studies in the reliability area, it is assumed that the failure rates of the system components are constant and have exponential distributions. This distribution and its attractive memory less property provide simple mathematical relationships in order to obtain the system reliability. But in real word problems, considering time-dependent failure rates is more realistic to model processes. It means that, the system components do not fail with a constant rate during the time horizon; but this failure rate changes over the time. One of the most useful statistical distributions in order to model the time-dependent failure rates is the Weibull distribution. This distribution is not a memory less one, so it was impossible to apply simple and explicit mathematical relationships as the same as exponential distributions for the reliability of a system. Therefore, researchers in this field have used simulation technique in these circumstances which is not an exact method to get near-optimum solutions. In this paper, for the first time, it is tried to obtain a mathematical equation to calculate the reliability function of a system with time-dependent components based on Weibull distribution. Also, in order to validate the proposed method, the results compared with exact solution that exists in literature.
