Industrial management
Hossein Sayyadi Tooranloo; Mohammad Zarei Mahmoudabadi; Reza Norouzi Avargani
Abstract
Over the past decade, the development of Network Data Envelopment Analysis (NDEA) models has enabled researchers to capture the internal structures and interrelationships among sub-units of decision-making units (DMUs). Compared to conventional DEA models, this approach provides deeper managerial and ...
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Over the past decade, the development of Network Data Envelopment Analysis (NDEA) models has enabled researchers to capture the internal structures and interrelationships among sub-units of decision-making units (DMUs). Compared to conventional DEA models, this approach provides deeper managerial and analytical insights into performance evaluation. The purpose of this study is to systematically review and clarify the overall trends in the development and application of NDEA models, with a particular focus on uncertainty-based approaches, over the period from 2014 to 2024. This research employs Microsoft Excel and VOSviewer software tools to conduct co-word analysis, visualize scientific networks, and identify research clusters. The reviewed articles were classified into two major dimensions: research domain (application areas) and research logic (deterministic or non-deterministic). The results indicate that most NDEA applications are concentrated in industrial sectors, and that deterministic logic dominates the existing body of literature. The term “network data envelopment analysis” was identified as the second most frequent keyword following “data envelopment” analysis. Based on the synthesis of reviewed studies, this research proposes a conceptual framework for NDEA and outlines potential future research directions centered on the integration of NDEA with uncertainty theories. To the best of our knowledge, no comprehensive study has simultaneously addressed network data envelopment analysis and uncertainty. By identifying research gaps, mapping the scientific structure of the field, and highlighting emerging themes and future avenues, this study provides a valuable reference for researchers and practitioners interested in performance evaluation under uncertainty.
Introduction
Over the past decade, Network Data Envelopment Analysis (NDEA) has emerged as a powerful extension of conventional Data Envelopment Analysis (DEA), enabling researchers to explicitly model the internal structures, intermediate products, and complex interrelationships among sub-processes within decision-making units (DMUs). Unlike traditional “black-box” DEA models, NDEA provides richer analytical and managerial insights by decomposing overall efficiency into stage-wise and network-based components. As real-world systems increasingly operate under uncertain, imprecise, or incomplete information, integrating uncertainty into NDEA models has become a critical methodological challenge. Despite the growing number of studies addressing NDEA and uncertainty separately, a comprehensive and systematic synthesis of the literature that jointly examines network DEA structures and uncertainty approaches remains limited.
Research Gap and Objective
Existing review studies on DEA and NDEA have primarily focused on methodological classifications, application domains, or dynamic and hierarchical extensions, while the uncertainty dimension has often been treated marginally or in isolation. Moreover, prior reviews rarely employ scientometric techniques to map the intellectual structure, thematic evolution, and research clusters within the NDEA–uncertainty literature. To address these gaps, the present study aims to systematically review and map the scientific landscape of Network Data Envelopment Analysis with an uncertainty approach. Specifically, this research seeks to (i) identify publication trends and influential sources, (ii) classify NDEA studies based on application domains and research logic (deterministic vs. non-deterministic), (iii) examine the dominant uncertainty modeling approaches adopted in NDEA, and (iv) propose a conceptual framework to guide future research in this field.
Methodology
This study adopts a systematic literature review combined with science mapping and bibliometric analysis. A structured search strategy was implemented in the Scopus and Web of Science databases, covering peer-reviewed journal articles published between 2014 and 2024. Keywords related to “Network Data Envelopment Analysis,” “Two-Stage DEA,” and “Uncertainty” were applied to titles, abstracts, and keywords. Following a multi-stage screening and filtering process based on time span, document type, language, and journal quality, a refined dataset of relevant articles was obtained. Bibliometric analyses, including co-word analysis and visualization of scientific networks, were conducted using Microsoft Excel and VOSviewer. The reviewed studies were systematically classified along two main dimensions: (1) research domain (application areas) and (2) research logic (deterministic versus non-deterministic modeling). Qualitative synthesis was then employed to interpret thematic patterns and methodological trends.
Results
The results reveal a steadily increasing trend in NDEA-related publications over the examined period, with a noticeable surge after 2020. Industrial and production systems constitute the dominant application domain, followed by energy, healthcare, transportation, banking, and supply chain management. The analysis indicates that deterministic NDEA models still prevail in the literature; however, uncertainty-based approaches—such as fuzzy sets, robust optimization, grey systems, stochastic programming, and rough sets—have gained growing attention in recent years. Keyword co-occurrence analysis identifies “data envelopment analysis” as the most frequent term, with “network data envelopment analysis” emerging as the second most prominent keyword. The findings further highlight that two-stage and multi-stage network structures are the most commonly employed configurations. Based on the synthesis of reviewed studies, this research develops a conceptual framework that links NDEA structures, uncertainty modeling techniques, and application domains, while outlining potential future research directions focused on hybrid and advanced uncertainty integration.
Discussion
The findings demonstrate that incorporating uncertainty into NDEA models significantly enhances their realism and applicability in complex decision-making environments. However, the dominance of deterministic logic suggests that many real-world uncertainties remain insufficiently addressed. From a methodological perspective, the study underscores the need for more integrated and hybrid uncertainty frameworks that combine multiple uncertainty theories within network DEA structures. From a practical standpoint, uncertainty-aware NDEA models provide decision-makers with more robust efficiency assessments, particularly in volatile sectors such as energy, healthcare, and supply chains. The scientometric mapping also reveals underexplored research clusters, indicating opportunities for interdisciplinary collaboration and methodological innovation.
Conclusion
This study represents one of the first comprehensive systematic and bibliometric reviews focusing explicitly on Network Data Envelopment Analysis under uncertainty. By simultaneously examining publication trends, research structures, uncertainty approaches, and application domains, the study offers a holistic understanding of the field. The proposed conceptual framework and identified research gaps provide valuable guidance for future theoretical development and empirical applications. Overall, this research contributes to advancing performance evaluation methodologies in uncertain environments and serves as a reference point for researchers and practitioners interested in NDEA and efficiency analysis under uncertainty.
multiple-criteria decision-making
Fatemeh Mojibian; Maryam Daneshvar; Ehsan Kafash Abdi
Abstract
With the accelerating digital transformation of the financial industry, FinTech innovations have reshaped traditional banking structures by introducing new technological capabilities, service delivery mechanisms, and competitive dynamics. As banks increasingly integrate FinTech solutions into their operational ...
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With the accelerating digital transformation of the financial industry, FinTech innovations have reshaped traditional banking structures by introducing new technological capabilities, service delivery mechanisms, and competitive dynamics. As banks increasingly integrate FinTech solutions into their operational and strategic processes, they encounter a broad set of risks that stem from technological complexity, regulatory uncertainty, cybersecurity vulnerabilities, and evolving customer expectations. These risks, if not properly identified and managed, can adversely affect financial stability, operational continuity, and institutional reputation. In emerging economies such as Iran, where digital transformation in banking is rapidly expanding, risk assessment becomes even more critical due to infrastructural constraints, regulatory gaps, and the heterogeneous maturity of financial technologies. Motivated by these challenges, this study conducts a comprehensive evaluation of major risks associated with FinTech adoption in Iranian banks, employing neutrosophic multi-criteria decision-making (MCDM) techniques to capture uncertainty, ambiguity, and expert judgment variability during risk assessment process.
Introduction
In recent years, the emergence of financial technologies (FinTech) has transformed the banking industry by enabling innovative financial services, reshaping business models, and changing customer expectations. While FinTech offers greater accessibility and efficiency, it also introduces new challenges for financial institutions, including heightened competitive pressure and disruptions to traditional banking operations. Although many studies highlight the positive contribution of FinTech to banking performance, others emphasize the potential negative consequences, such as reduced profitability due to competition from digital lending and investment platforms. In addition, risks such as cybersecurity threats, regulatory compliance issues, operational failures, data privacy concerns, technological dependency, and challenges in customer trust have become major barriers to FinTech adoption. Given these complexities, a systematic and structured assessment of FinTech-related risks is essential, particularly in developing countries where digital transformation is rapidly progressing but regulatory and infrastructural limitations persist. This study seeks to address this research gap by offering a comprehensive, uncertainty-aware evaluation of the critical risks influencing FinTech adoption in banks.
Methodology
This study adopts a hybrid neutrosophic multi-criteria decision-making (MCDM) framework to identify, validate, and prioritize key risks associated with FinTech adoption. First, an extensive literature review was conducted to extract potential risk factors highlighted in previous academic and industry reports. Next, the neutrosophic Delphi method was applied to refine and validate these factors based on expert consensus under uncertainty. Through this process, seven major risks were confirmed: security, credit, operational, strategic and competitive, legal and regulatory, reputational, and liquidity risks. Subsequently, the neutrosophic Best–Worst Method (BWM) was employed to determine the relative importance of these risks, enabling more accurate modeling of expert judgment hesitation and ambiguity. Finally, to evaluate potential FinTech implementation options for Pasargad Bank, the neutrosophic Multi-Attributive Border Approximation Area Comparison (MABAC) method was used. This integrated approach makes it possible to capture the complexity and uncertainty inherent in technological risk assessment.
Results and Discussion
The results of the BWM analysis indicate that security risk holds the highest importance in the context of FinTech adoption, reflecting the increased sensitivity of digital transactions and the potential for cyberattacks, data breaches, and system intrusions. Operational and reputational risks ranked next, underscoring the significance of system reliability and customer trust in digital financial environments. These findings are consistent with existing studies that emphasize the dominant role of cybersecurity threats in shaping FinTech outcomes.
In terms of implementation strategies, the MABAC analysis reveals that the scenario involving collaboration among banks to form a FinTech consortium holds the highest priority. This strategy supports resource sharing, cost reduction, and the development of standardized and secure financial technologies. Moreover, it enables banks to pool expertise and strengthen resilience against technological and regulatory uncertainties. For Pasargad Bank, the results suggest that focusing on robust security practices, operational risk management, and reputation preservation is essential to ensuring successful and low-risk FinTech implementation. The findings also align with international evidence indicating that collaborative and partnership-based FinTech models yield more sustainable and resilient outcomes in uncertain environments.
Conclusion
This study contributes to the FinTech risk literature by providing a structured and uncertainty-aware assessment of the major risks influencing FinTech adoption in banking. The proposed hybrid neutrosophic MCDM framework—combining Delphi, BWM, and MABAC—offers a more realistic modeling environment for capturing ambiguity, incomplete information, and expert hesitation. According to the results, security risk represents the most critical concern for banks in their FinTech implementation efforts, followed by operational and reputational risks. The findings also highlight the strategic value of interbank collaboration, suggesting that forming a FinTech consortium is the most advantageous and least risky implementation scenario for Pasargad Bank.
The insights from this study have practical implications for bank managers and policymakers. Banks should establish specialized FinTech risk management units, invest in advanced cybersecurity infrastructure, strengthen digital operational capabilities, and enhance customer awareness programs. Furthermore, closer engagement with regulators is required to ensure compliance and foster a supportive regulatory environment. Future research may expand this study by incorporating time-series data on risk events, exploring emerging technologies such as AI and blockchain, and conducting comparative analyses across multiple banks or countries to develop more generalizable insights.
multiple-criteria decision-making
Iraj Rouhi; Mahsa Pishdar; Maryam Hassanikordede
Abstract
Food loss and waste represent a global challenge threatening food security and exacerbating climate change. Upcycling food waste into value-added products is increasingly recognized as an effective pathway toward a circular economy. This study introduces a novel integrated multi-criteria decision-making ...
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Food loss and waste represent a global challenge threatening food security and exacerbating climate change. Upcycling food waste into value-added products is increasingly recognized as an effective pathway toward a circular economy. This study introduces a novel integrated multi-criteria decision-making (MCDM) framework based on Circular Intuitionistic Fuzzy Sets (CIFS) combined with MEREC objective weighting and CIFS-MARCOS ranking, an approach not previously applied to food waste upcycling. Ten prominent upcycling strategies were identified from recent literature and evaluated against twelve sustainability criteria by ten food industry experts. Results revealed “market potential”, “public awareness of upcycled products,” and “food quality and safety” as the most influential criteria. Among strategies, producing sustainable textiles from food waste ranked first, followed by sustainable packaging, novel food ingredients, and bioenergy production. The proposed framework effectively handles uncertainty and dynamic interdependencies among criteria, offering a robust and original tool for prioritizing upcycling pathways.
Introduction
The circular economy and zero-waste economy are emerging as central components of the discourse on sustainable living, offering significant benefits for both humanity and ecosystems. An innovative approach to achieving this goal involves transforming food waste into value-added products. Food waste, discarded at various stages of the supply chain from production to consumption, poses a global challenge with profound environmental, economic, and social implications. These wastes encompass fresh horticultural products, dairy, meat, seafood, grains, expired materials, and consumption leftovers, all of which hold potential for utilization in other industries, while restaurants and the catering sector also generate substantial waste through surplus materials and unsold food. Global solid waste production is projected to increase from 2.01 billion tons in 2016 to 3.4 billion tons by 2050, with approximately one-third of food produced for human consumption (1.3 billion tons annually) being wasted, incurring an economic cost of $1 trillion, which rises to $2.6 trillion when accounting for social and environmental impacts.
Methodology
The methodology of this research is grounded in the integration of empirical and theoretical knowledge. Empirical data were collected through surveys conducted with experts. Using purposive sampling, ten experts with 10 to 25 years of experience in the food industry were selected. The selection criteria included their distinguished academic and practical backgrounds, which enabled a comprehensive and in-depth understanding of food waste valorization concepts and their practical strategies. Additionally, the theoretical foundation of the study was established through a thorough review of the literature, from which key criteria and indicators related to food waste valorization were extracted. To integrate these two knowledge domains and model the inherent uncertainty in expert judgments, Circular Intuitionistic Fuzzy Sets (CIFS) were employed. The developed multi-criteria decision-making framework in this study performs criteria weighting using the CIFS-MEREC method and strategy ranking using the CIFS-MARCOS method (see Figure 1). This approach enhances the accuracy and reliability of the analysis under complex decision-making conditions.
Findings
The results indicate that, in the ranking of strategies, the production of sustainable textiles from food waste secured the top position, as it simultaneously achieves economic value addition and reduces environmental impacts. Following this, sustainable packaging, the production of new food products from waste, and bioenergy production were ranked sequentially. This ranking suggests that the most successful strategies are those that close the resource cycle while generating economic value, thereby exhibiting the greatest potential for advancing sustainable development goals.
Results and Discussion
This research provides a comprehensive framework for prioritizing food waste valorization strategies, addressing the lack of integrated multi-criteria ranking despite extensive technical and environmental studies. By integrating Circular Intuitionistic Fuzzy Sets (C-IFS), MEREC, and MARCOS, twelve sub-criteria across environmental, economic, social, and technical dimensions were evaluated, yielding ten key strategies. Market potential, public awareness, and food quality and safety emerged as top priorities, offering strategic insights for stakeholders. Transforming food waste, such as fruit peels or coffee grounds, into high-value fibers, natural dyes, or vegan leather requires investment in research and development and collaboration with eco-conscious fashion brands. Converting waste like corn starch or shrimp shells into biodegradable films and containers addresses plastic pollution by developing competitive, durable, and cost-effective alternatives. Repurposing agricultural residues into protein, flour, or enriched foods demands stringent safety and quality standards to gain consumer trust. Producing biogas and biofuels through anaerobic digestion offers a scalable solution, reliant on robust infrastructure and a consistent waste supply.
Conclusion
The findings highlight a shift from traditional approaches, such as bioenergy, to innovative, high-value solutions like sustainable textiles. Strategy selection should align with waste type, technological capacity, and market needs, with hybrid approaches optimizing sustainability and profitability. The proposed C-IFS, MEREC, and MARCOS framework ensures robust decision-making for stakeholders, despite limitations including a limited expert sample and reliance on qualitative data. Future research should expand expert input and investigate long-term impacts, such as food security, employment, and emissions reduction. Integrating MEREC and MARCOS with MABAC and C-IFS could further enhance decision-making precision. This multi-criteria framework provides a solid foundation for policy-making, investment, and future research in the circular economy.
supply chain management
Zahrasadat Tabatabaei; Mohammad Taghi Rezvan; Saeed Dehnavi
Abstract
The broiler chicken industry plays a crucial role in food security and employment generation; however, it faces challenges such as increasing demand, mortality losses, and appropriate breed selection. This study aims to develop an integrated mathematical model for analyzing and optimizing the chicken ...
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The broiler chicken industry plays a crucial role in food security and employment generation; however, it faces challenges such as increasing demand, mortality losses, and appropriate breed selection. This study aims to develop an integrated mathematical model for analyzing and optimizing the chicken meat supply chain by simultaneously addressing breed selection, production planning, logistics management, and loss reduction. The proposed model is formulated as a mixed-integer linear programming problem that maximizes the total profit of the supply chain by considering revenues from chicken meat and poultry manure, as well as all procurement, production, and distribution costs. The model includes constraints related to supply availability, production and distribution capacities, demand satisfaction, and the limitation of raising only one breed per farm. The model is solved using GAMS based on data obtained from reliable sources. The results demonstrate effective resource allocation, optimal breed selection, and improved logistics network design. Sensitivity analysis further confirms the robustness and reliability of the proposed model.IntroductionThe broiler industry plays a crucial role in ensuring food security and creating employment. However, this industry faces several structural challenges, including demand fluctuations, rising input costs, inefficient loss management, and suboptimal breed selection. Previous studies have primarily focused on optimizing individual segments such as production, logistics, or financial performance, while limited attention has been given to integrated models that simultaneously address strategic and tactical decisions across the entire supply chain. This research gap highlights the need for a comprehensive decision-making framework capable of capturing interactions among different levels of the broiler supply chain. Accordingly, the main objective of this study is to design and present an integrated mathematical model that simultaneously optimizes breed selection, production planning, logistics management, and loss reduction within a four-tier supply chain.Research BackgroundIn recent years, extensive research has been conducted on broiler supply chain optimization. Some studies have focused on allocation and scheduling problems using mixed-integer linear programming (MILP) models, while others have examined the impacts of business policies or uncertainty management practices. Additional research has explored the integration of production and financial decisions, as well as supply chain modeling during disruptive events such as the COVID-19 pandemic. Despite these efforts, a comprehensive review of the literature reveals that most existing studies address the problem in an isolated and one-dimensional manner, often neglecting optimal breed selection and mortality management. In some cases, simplifying assumptions—such as allowing the simultaneous breeding of multiple breeds—have been adopted, which do not reflect operational realities. This study seeks to bridge this gap by explicitly integrating breed selection and loss management into a unified optimization framework.MethodThis study proposes an integrated mathematical model for optimizing the chicken meat supply chain. The system under consideration consists of a four-level supply chain including suppliers (day-old chicks, feed, and vaccines), breeding farms, slaughterhouses, and distribution centers (markets). The proposed model is formulated as a mixed-integer linear programming (MILP) problem with the objective of maximizing total network profit by accounting for revenues from chicken meat and poultry manure sales, as well as all supply, production, and distribution costs. Biological characteristics of different chicken breeds—such as feed conversion ratio, rearing period, mortality rate, and final weight—are incorporated as model parameters. The constraints include supply, production, and distribution capacity limitations, demand satisfaction requirements, and the restriction of raising only one breed per farm. The study is conducted in two main stages: model formulation and model implementation. In the first stage, the mathematical model is developed by defining key decision variables, including breed selection, supplier assignment, capacity allocation, and production and distribution planning. In the second stage, the model is implemented and solved using GAMS software with the CPLEX solver. The required data are obtained from a combination of industry reports, expert opinions, and market data. A realistic numerical example involving two chick suppliers, three feed suppliers, three vaccine suppliers, four farms, three slaughterhouses, and six distribution centers is designed to validate the model. After obtaining the optimal solution, a comprehensive sensitivity analysis is performed on key parameters such as meat selling price, input costs, mortality rates, and transportation costs to assess the stability and reliability of the model under varying market conditions.Discussion and ResultsSolving the model using realistic data demonstrates its capability to generate operationally optimal solutions. The key findings are summarized as follows:Optimal breed selection: The final solution selects only the Ross and Kap breeds, while the Aryan breed is excluded. This outcome reflects the superior performance of these breeds in terms of biological parameters (e.g., mortality rate and feed conversion ratio) and their compatibility with the cost and capacity structure of the network.Resource allocation: Among the four hypothetical farms, only two are optimally activated. This result is attributed to their comparative advantages in terms of proximity to slaughterhouses, effective capacity utilization, and reduced logistics costs.Sensitivity analysis: The sensitivity analysis confirms the rational and stable behavior of the model. The selling price of chicken meat and the purchase price of chicks are identified as the most influential parameters affecting profit. Additionally, the results indicate that increases in mortality rates have a nonlinear and substantial negative impact on profitability, underscoring the importance of investing in effective loss reduction strategies. Optimal breed combination: In the final solution, only the Ross and Kap breeds were selected and the Aryan breed was eliminated. This selection indicated the superiority of these two breeds in terms of the combination of technical parameters (such as mortality rate and conversion factor) and coordination with the cost and capacity structure of the network.Resource allocation: Out of the four hypothetical farms, only two farms were optimally activated, the reason for which can be found in the comparative advantage of these farms in terms of proximity to slaughterhouses, effective capacity and reduced logistics costs.Sensitivity analysis: The results of the sensitivity analysis confirmed the rational and stable behavior of the model. Specifically, the selling price of meat and the price of chicken were identified as the most influential parameters on increasing and decreasing profits, respectively. The model also showed that increasing casualty rates have a nonlinear and significant effect on decreasing profits, which highlights the need to invest in casualty reduction strategies.ConclusionThis study developed an integrated modeling framework for optimizing the broiler supply chain by endogenously incorporating breed selection and mortality management into the decision-making process. The results demonstrate that simultaneously considering biological, economic, and logistical factors enhances supply chain integration and performance. The proposed model serves as an effective decision-support tool for poultry industry managers and policymakers, enabling optimal resource allocation and strategic planning to maximize profitability and efficiency in competitive environments. The primary limitation of the study lies in the difficulty of accessing accurate and confidential data from production units, which was addressed through the use of composite and approximate data. Therefore, the results should be interpreted with this limitation in mind. The model can also be applied as an analytical tool to estimate equilibrium chicken meat prices under scenarios involving changes or removal of government support policies, such as preferential exchange rates for livestock inputs. By evaluating alternative input pricing scenarios, the model helps identify prices that maintain producer profitability without imposing excessive economic pressure on consumers. At a macro level, the application of such models can assist policymakers in designing targeted support strategies to improve productivity, enhance profitability, and promote sustainability in the broiler supply chain under volatile market conditions. Future research may extend the model by incorporating uncertainty-based approaches, such as stochastic or fuzzy programming, to better address demand and price fluctuations. Additionally, developing a multi-objective version of the model that accounts for environmental (e.g., carbon footprint reduction) and social sustainability criteria represents a promising direction for further research.
supply chain management
sahar yazdani; ahmad mehrabian; samad ayazi; Ali khamaki
Abstract
Pharmaceutical supply chain management faces specific challenges and risks under economic sanctions. In these circumstances, sanctions can have serious impacts on the supply chain and its performance. The emergence of new processes in the environment of companies and organizations, such as the political ...
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Pharmaceutical supply chain management faces specific challenges and risks under economic sanctions. In these circumstances, sanctions can have serious impacts on the supply chain and its performance. The emergence of new processes in the environment of companies and organizations, such as the political relations of governments, and sanctions inside and outside the company, causes supply chains to face new and diverse risks every day. In such an environment, it is not possible to manage the organization without paying attention to the risks of the supply chain, and the administration and leadership of the organization will need to identify, prioritize, and monitor the components related to supply chain risks.Achieving such a goal requires examining and identifying the development of a framework for supply chain risk management components.Risk and failure in the supply chain can have a significant impact on short-term performance and a negative long-term impact on the financial performance of the organization. Therefore, supply chain risk management is essential to reduce failures caused by various risks such as uncertain economic cycles, uncertain customer demand, and unpredictable natural and human disasters. For supply chain risk management, especially in the pharmaceutical industry, there is no complete and accurate model that can identify and analyze the various components and dimensions of this phenomenon.The country's pharmaceutical industry, as one of the most important industries with investment and employment potential, is one of the fundamental concerns of health and treatment systems. Because the pharmaceutical industry supply chain is the main part of healthcare systems in distributing medicines to the community. Supply chain risks can waste resources and also worsen the performance of the drug supply chain. Therefore, proper identification and analysis of risk components in developing strategies to minimize risk in the drug supply chain is something that should be addressed, especially since pharmaceutical products are fully controlled products and are under the legal supervision of public regulatory authorities.Considering the meaning of each code, each category is categorized into its own similar concepts.Therefore, the innovative aspect that can be considered for this research is having an integrated and holistic approach to the phenomenon of supply chain risk management in the pharmaceutical industry, so that, like the systematic approach of Corbin and Strauss in the data-driven theory, the phenomenon in question is divided into main dimensions, namely drivers (causal conditions), background conditions, intervening conditions, consequences, strategies, and supply chain risks as the main phenomenon, and the prioritization of the characteristic codes is done in each dimension.The aim of this study is to identify and classify the risk management of the pharmaceutical supply chain under economic sanctions. For this purpose, using the meta-synthesis methodology of Sandelowski and Barso (2007), categories and groups related to supply chain risk were identified and coded.Then, through content analysis using the meta-synthesis method, the results and achievements of previous research were analyzed. Out of 113 articles reviewed, only 19 articles were selected for analysis. Finally, the different aspects of pharmaceutical risk management under economic sanctions were classified into three main dimensions including internal, external and logistics and financial risk, 14 sub-dimensions and 29 characteristic codes.Then, using the structural equation method and based on the initial model presented in the meta-synthesis method, a proposed model for supply chain risk management under economic sanctions for the pharmaceutical industry was presented.In this research, an attempt was made to examine the components of supply chain risk management under economic sanctions in the pharmaceutical industry using the meta-synthesis method.The results of this study show that the imposition of economic sanctions creates severe disruptions that pose significant risks to supply chain operations. The pharmaceutical industry plays a critical role in ensuring public health by providing essential medicines and treatments. However, under economic sanctions, supply chains in this sector face unprecedented challenges that threaten their stability and performance.The results of the research showed that the risks facing supply chain management in the pharmaceutical industry under economic sanctions have their own subcategories, attention to which can significantly reduce supply chain-related risk. The importance and attention to organizational, management, production, quality, and technology risks, which are all subcategories of internal risk in the supply chain management process, can vary depending on the strategies appropriate to the pharmaceutical industry under economic sanctions.The results of this study show that the imposition of economic sanctions creates severe disruptions that pose significant risks to supply chain operations. The pharmaceutical industry plays a critical role in ensuring public health by providing essential medicines and treatments. However, under economic sanctions, supply chains in this sector face unprecedented challenges that threaten their stability and performance. For example, supply risk arises from disruptions in the supply of raw materials or active pharmaceutical ingredients and from the inability to procure essential raw materials, components or services due to restrictions imposed by sanctions.Suppliers may face operational challenges or cease operations altogether, forcing businesses to seek alternative sources. This often results in increased costs and longer delivery times. Market risks refer to changes in market dynamics, such as fluctuations in demand or shifts in consumer behavior. Technological risk involves the inability to access advanced manufacturing technologies or software due to restrictions imposed by sanctions. This can hinder innovation and reduce operational efficiency.Sanctions often limit access to advanced technologies, software, and equipment essential to maintaining competitive operations. This creates technological obsolescence and hinders innovation. Organizational risks arise from internal inefficiencies or mismanagement within pharmaceutical companies. These include poor communication, inadequate resource allocation, and inadequate strategic planning. Organizational risk arises from internal inefficiencies and the inability to adapt quickly to external disruptions caused by sanctions.Production risk is associated with disruptions to production processes, often due to shortages of raw materials, outdated equipment, or labor constraints. Sanctions can disrupt production processes by restricting access to essential inputs or creating operational inefficiencies. This may result in delays, reduced output quality, or a complete halt to production. Management risks relate to the ability of leadership to respond effectively to crises. Poor decision-making or a lack of contingency planning can exacerbate the impact of sanctions.Economic sanctions often lead to disruptions in the flow of raw materials, delays in manufacturing and distribution processes, and increased operational costs. Sanctions often restrict access to essential resources, constrain market activities, and create uncertainty in the legal and regulatory environments. The present study seeks to categorize and analyze the various risks that emerge in pharmaceutical supply chain management under economic sanctions. By doing so, it helps develop strategies to mitigate these challenges and increase supply chain resilience.
modeling and simulation
Atoosa Ebrahimi Shah Abadi; Jahangir Yadollahi Farsi; Niloofar Nobari
Abstract
The rapid rise of AI-based platforms demands a clearer grasp of their technical and managerial aspects. To address this, this study compares three major types (transactional, innovation, and integrated) using the ADO (Antecedents-Decisions-Outcomes) framework. Through a meta-synthesis of 70 articles ...
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The rapid rise of AI-based platforms demands a clearer grasp of their technical and managerial aspects. To address this, this study compares three major types (transactional, innovation, and integrated) using the ADO (Antecedents-Decisions-Outcomes) framework. Through a meta-synthesis of 70 articles (2015–2025) and systematic coding, it shows that transactional platforms use microservices and open APIs for fast data exchange; innovation platforms rely on deep learning and open collaboration; and integrated platforms focus on unified data governance. Cloud infrastructure, security, and transparent governance link them all, but each type follows unique strategies shaping decisions and outcomes for firms, users, and ecosystems. This work clarifies how shared technological foundations diverge into distinct paths, offering insights for managers, policymakers, and future researchers to design robust, tailored governance for evolving AI ecosystems.IntroductionArtificial Intelligence has become the beating heart of digital economies, redefining industries through machine learning, big data analytics, IoT integration, and cloud solutions. AI-driven platforms, ranging from data marketplaces to open innovation hubs, play a central role in reshaping value chains and amplifying agility and productivity. Despite this prominence, prior literature remains fragmented, focusing on technical specifications or isolated managerial aspects, without providing a comprehensive framework that interlinks their foundations, strategic choices, and impacts. The scholarly literature on AI-based digital platforms, despite rapid expansion, lacks an integrated, comparative framework that systematically distinguishes platform types and clarifies their technological and governance pathways. Building on this categorization, AI platforms can be grouped into three archetypes: Transactional platforms, which enable exchanges of goods, services, or data through modular architectures and open APIs; Innovation platforms, which nurture co-creation through open collaboration and advanced processing frameworks; and Integrated platforms, which fuse transactional and innovation capabilities with robust data governance and ecosystem-wide orchestration. Yet, much of the extant research remains confined to technological facets, such as machine learning pipelines, or to managerial dimensions like trust, compliance, and governance protocols. To address this gap, the present study employs the ADO framework to map how technological prerequisites, strategic design choices, and multi-level outcomes interlock across these platform types. By combining Cusumano’s typology with the ADO lens, this research proposes a structured comparative perspective: Transactional platforms optimize rapid, low-cost exchanges; Innovation platforms fuel open co-creation; and Integrated platforms consolidate efficiency through standardized, unified data governance. Through this integrated approach, the study illuminates the causal pathways that link technological foundations to managerial choices and, ultimately, to the societal and organizational impacts of AI-driven platforms.Research QuestionThe overarching research question thus asks: How do the antecedents, key decisions, and outcomes of AI-based platforms differ and intersect across transactional, innovation, and integrated models?Literature ReviewThe existing literature on AI-based platforms reveals that these platforms, through the fusion of machine learning, big data, and cloud computing, play a pivotal role in facilitating data exchange, open innovation, and data governance. According to the well-known classification by Cusumano et al. (2020), these platforms can be grouped into three main categories:Transactional: They serve as intermediary infrastructures for the rapid exchange of data and services by leveraging modular architecture, open APIs, and cloud processing.Innovation: They offer an open environment for co-creation of products and deep learning models, relying on GPU/TPU capabilities and developer networks.Integrated (Hybrid): They represent a combined structure that integrates data governance while simultaneously managing transactions and innovation at an industrial scale.In parallel with this classification, the ADO framework acts as a causal model linking three fundamental layers:Antecedents: Core technologies such as cloud computing and machine learning, skilled human capital, and a regulatory playing field that aligns ethics with the law.Decisions: Technical architecture, a seamless user experience, innovation policies, data regulations, and standards that shape the identity of these platforms.Outcomes: Ranging from profitability and organizational efficiency to user satisfaction and engagement, and ultimately, far-reaching economic and ecosystem effects that redraw industrial boundaries.Combining this framework with Cusumano’s model offers a fresh analytical pathway for comparative investigation of the structural, managerial, and outcome dimensions of AI platforms, providing a coherent foundation for future research in designing and governing this digital ecosystem.MethodologyTo address this question, a systematic meta-synthesis approach was employed. Relevant articles were sourced through comprehensive database searches (Google Scholar, Scopus, and Science Direct) covering 2015–2025. After rigorous screening, seventy high-quality articles were selected based on relevance to AI platforms, user-centric applications, and compatibility with the ADO structure. Data were coded thematically in three stages: open coding (identifying raw concepts per platform type), axial coding (categorizing into antecedents, decisions, and outcomes), and selective coding (developing the integrated conceptual framework). Inter-coder reliability was validated through Cohen’s Kappa, which reached a robust score of 0.80.Results and DiscussionThe meta-synthesis results reveal that AI-based platforms can be grouped into three distinct yet interconnected types: transactional, innovation, and integrated. Across seventy reviewed studies, common technological antecedents emerged—cloud computing, robust data governance, and open API frameworks are foundational for all three. Transactional platforms use microservice architectures and clear Service-Level Agreements to lower costs and build trust, especially in fast-paced data exchanges like financial transactions. Innovation platforms stand out for leveraging deep learning modules and open collaboration to co-create new products and expand complementary markets. Integrated platforms emphasize large-scale data orchestration and compliance, aligning diverse systems through unified governance to improve organizational efficiency and national data ecosystems. The combined findings and discussion show that, despite shared foundations, each platform type follows unique decision pathways: cost efficiency for transactional, collaborative agility for innovation, and cohesive governance for integrated. This validates the ADO framework’s capacity to trace how technological and institutional conditions shape strategic design and produce layered outcomes. These insights help managers align architecture, policies, and stakeholder roles to balance performance, security, and innovation in complex AI ecosystems.ConclusionIn summary, this study clarifies how the intertwined dimensions of technology, governance, and user-centric design shape the trajectories of transactional, innovation, and integrated AI-based platforms. The proposed ADO-based synthesis does more than classify platform archetypes; it demonstrates a coherent causal pathway from technological and organizational prerequisites, through architectural and managerial decisions, toward tangible outcomes spanning operational, user, social, and economic domains. Practically, the findings deliver valuable guidance for managers, platform developers, and policymakers. Transactional platform managers should emphasize transparent contracts, modular microservice design, and robust security layers to build user trust and lower transaction costs. For innovation-driven ecosystems, fostering open collaborations, protecting intellectual property rights, and investing in scalable GPU/TPU infrastructure are key to sustaining rapid innovation cycles. Integrated platform leaders must adopt comprehensive data governance frameworks, aligning diverse standards and compliance policies to harmonize data flows across industries. From a policy standpoint, governments and regulators are urged to craft clear guidelines for secure data exchanges, promote open innovation incentives, and support national investments in cloud and AI infrastructure to maintain global competitiveness. Future research should extend this conceptual foundation through mixed-method studies, localized models, and robust quantitative testing to validate the ADO pathways across varying cultural and industrial contexts. By bridging theory and practice, this work contributes a systematic lens for understanding and steering the complex dynamics of AI-based platform ecosystems, charting a path toward more resilient, innovative, and ethically governed digital futures.
supply chain management
Moezodin Mazaheri Tirani; mehdi karbasein; Hadi Shirouyehzad
Abstract
Nowadays, outsourcing is considered one of the most critical strategies across various industries and sectors. A proper selection of activities for outsourcing results in organizational efficiency and enhances the ability to focus on competitive advantages, while an improper choice can lead to significant ...
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Nowadays, outsourcing is considered one of the most critical strategies across various industries and sectors. A proper selection of activities for outsourcing results in organizational efficiency and enhances the ability to focus on competitive advantages, while an improper choice can lead to significant challenges. In this research, a comprehensive model is proposed for ranking outsourcing options in the service sector. The model calculates the weights of evaluation criteria based on different domains and can be applied across various job sectors. Initially, 285 outsourcing decision-making criteria were identified. Using a structured questionnaire and the Lawshe method, these criteria were reduced to 25 key indicators, and their content validity was confirmed. Subsequently, the finalized criteria were categorized into the four perspectives of the Balanced Scorecard (BSC) using a fuzzy questionnaire. The criteria were then weighted using the Fuzzy SWARA method, and finally, the outsourcing alternatives in the case study, Amir al-Momenin Hospital, were ranked using the fuzzy ARAS method. The results showed that restaurant, pharmacy, IT and technical support, and finance and accounting activities are prioritized for outsourcing with a desirability of 96%, 82%, 63%, and 36%, respectively. By providing a scientific and flexible approach, this model helps decision-makers in the service sector to make effective decisions for outsourcing by considering comprehensive indicators and appropriate weighting, thereby improving organizational productivity.
Introduction
Outsourcing has become one of the most effective strategic tools for improving organizational efficiency and focusing on core competencies. In service-based organizations—particularly in healthcare—determining which activities to outsource is a critical decision that directly affects cost, performance, and service quality. Incorrect outsourcing decisions may lead to inefficiency, increased expenses, and service deterioration. Despite its importance, many organizations lack a comprehensive and integrated framework that considers both financial and non-financial dimensions for evaluating outsourcing alternatives.
This study aims to develop a comprehensive and systematic model for ranking outsourcing options in the service sector. The model integrates the Balanced Scorecard (BSC) framework with fuzzy multi-criteria decision-making (MCDM) methods to provide a structured, data-driven basis for managerial decisions. The research applies this integrated model to a real case study in the Amir al-Momenin Hospital in Isfahan, Iran, where several service areas were evaluated for potential outsourcing.
Methodology
The research adopted a quantitative and descriptive-analytical approach. Initially, 285 decision criteria related to outsourcing were identified through literature review and expert interviews. Using the Lawshe method and content validity ratio (CVR), these criteria were reduced to 25 key indicators.
The validated indicators were classified according to the four perspectives of the Balanced Scorecard (BSC), financial, customer, internal processes, and growth and learning.
To determine the relative importance (weights) of each criterion, the Fuzzy Step-wise Weight Assessment Ratio Analysis (Fuzzy SWARA) technique was applied. This approach allows experts’ qualitative judgments to be expressed as fuzzy numbers, capturing uncertainty in human assessment.
After obtaining the weights, the Fuzzy Additive Ratio Assessment (Fuzzy ARAS) method was used to evaluate and rank the outsourcing alternatives. The model was implemented in Amir al-Momenin Hospital, where four key service areas were analyzed: 1- Catering and restaurant services, 2- Pharmacy services, 3-IT and technical support, and 4- Financial and administrative services.
Expert questionnaires and interviews were conducted among hospital managers and key staff familiar with operational performance and outsourcing processes.
Findings
The fuzzy SWARA analysis revealed that financial and process-related indicators carried the highest importance, reflecting the hospital’s priority for cost-effectiveness and efficiency improvement. The subsequent ranking through the Fuzzy ARAS method produced the following results:
Catering and Restaurant Services achieved the highest preference value (0.96),
Pharmacy Services ranked second (0.82),
Maintenance and Technical Support ranked third (0.63),
Financial and Administrative Services ranked last (0.36).These findings indicate that outsourcing catering services would yield the most significant operational and financial benefits, while maintaining internal control over financial management is more suitable for the organization.
The results validate the proposed model’s capability to handle multiple, often conflicting criteria in a fuzzy environment, ensuring a robust and reliable decision-making process.
Discussion and Conclusion
The findings emphasize that outsourcing decisions in service fields should be made by a logical method and consider both financial and non-financial perspectives. The integration of BSC, Fuzzy SWARA, and Fuzzy ARAS provides a holistic and flexible framework that aligns strategic goals with operational realities.
This study contributes to the literature by presenting a practical decision-support model that can reduce uncertainty and subjectivity in outsourcing evaluations. In practice, hospital managers can apply this model to identify optimal outsourcing opportunities, balance efficiency and service quality, and enhance transparency in decision-making.
The proposed model can also be adapted for other service-oriented organizations. Future research may focus on extending this framework by integrating sustainability indicators, dynamic weighting mechanisms, and sensitivity analysis to strengthen the decision-making process further.
