University of Tehran PressIndustrial Management Journal3115-738618420261001Presenting a Global Optimization-based Dynamic Fuzzy Inference System (GODFIS) for online data stream prediction104710860510.22059/imj.2026.412275.1008301ENFarzadBahramiAssistant Prof., Department of Industrial Management, Faculty of Management, University of Arak, Arak, Iran.0000-0002-3327-648XMohammad HoseinAlavidoostPh.D., Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.Journal Article20260317Objective: Real-world data streams exhibit nonlinear and non-stationary characteristics that create significant challenges for effective predictive modeling. While evolving fuzzy systems (EFS) can dynamically update their structure and parameters to address these challenges, existing methods primarily focus on system identification rather than the optimality of learned parameters. This paper aims to bridge this gap by proposing a novel Global Optimization-based Dynamic Fuzzy Inference System (GODFIS) designed to enhance prediction accuracy through optimized parameter estimation.
Methodology: The proposed GODFIS framework leverages two key concepts: recursive center-of-gravity and global parameter estimation. Under this framework, we introduce a new Global Optimization-based Evolving Clustering Algorithm (GOECA) to optimize clustering in the response space and accurately determine initial parameters. Furthermore, a novel noise elimination principle is incorporated to maintain response quality and mitigate the negative impact of noise on the system’s knowledge base.
Results: To evaluate the performance of GODFIS, extensive experiments were conducted on standard benchmarking datasets. The empirical results demonstrate that the proposed model achieves significantly higher prediction accuracy in solving benchmark problems compared to state-of-the-art and existing evolving fuzzy approaches.
Conclusion: By integrating global parameter optimization with robust noise elimination, GODFIS effectively addresses the limitations of conventional evolving fuzzy systems in non-stationary environments. The findings confirm that optimizing learned parameters and refining the clustering process lead to superior and more reliable predictive performance in dynamic, real-world data streamshttps://imj.ut.ac.ir/article_108605_f927519212c629fdb8f6cf9a616ef348.pdfUniversity of Tehran PressIndustrial Management Journal3115-738618420261001Designing the Cognitive Map of Key Actors in the Innovation Ecosystem: An Exploratory Study Using the Fuzzy Cognitive Modeling Approach487710860610.22059/imj.2026.413648.1008314ENZahraShahsavariPh.D. Candidate, Department of Management, Faculty of Management and Economics, University Guilan, Rasht, Iran.MahmoudMoradiAssociate Prof., Department of Management, Faculty of Management and Economics, University of Guilan, Rasht, Iran.0000-0002-6272-7892Mohammad RahimRamazanianAssociate Prof., Department of Management, Faculty of Management and Economics, University of Guilan, Rasht, Iran.0000-0003-1026-8636RezaAsadifardAssociate Prof., Department of Innovation Policy and Foresight, Technology Studies Institute, Tehran, Iran.Journal Article20260502Objective: Innovation ecosystems are complex, multilayered structures comprising diverse actors such as universities, industries, investors, policymakers, and researchers. Identifying and analyzing the interconnections among these actors is paramount for advancing innovation and improving ecosystem performance. This study investigates the relationships among actors within the innovation ecosystem of the laboratory industry using Fuzzy Cognitive Mapping (FCM) to analyze their causal relationships and mutual influences.
Methodology: Based on a literature review, field data, and interviews with academic and industry experts, seventeen key actors were identified. A 17×17 interaction matrix was developed to determine the influence of each actor on others within the ecosystem. The FCM analysis was used to model the network structure of these actors and to evaluate the strength of their interactions.
Results: The cognitive map analysis identified 17 actors and 268 causal relationships within the laboratory industry innovation ecosystem, indicating a highly interconnected structure. Centrality analysis shows that the LabsNet network, accelerators, platforms, and coordinating units play the most influential roles in shaping ecosystem dynamics.
Conclusion: The findings highlight that the performance of the laboratory industry innovation ecosystem depends on the coordinated functioning of multiple interdependent actors. In particular, facilitative and coordination oriented structures—such as accelerators, platforms, and coordinating units—play a critical role in enabling interaction, knowledge exchange, and collaborative innovation. Strengthening these components can therefore support the sustainable development and dynamism of the ecosystem.https://imj.ut.ac.ir/article_108606_474073abeaed0cffca96ce5c3728a6e4.pdfUniversity of Tehran PressIndustrial Management Journal3115-738618420261001Doing More with Less: A Meta-Analysis of How Frugal Innovation Drives Circular Economy7810310860710.22059/imj.2026.413756.1008315ENHosseinBakhtiariMSc, Department of Management, University of Qom, Qom, Iran.0009-0008-9317-841XHosseinShiraziAssistant Prof., Department of Technology Management, Islamic Azad University, Qom Branch, Qom, Iran.0000-0003-3130-1642AbolfazlEdalatipourPh.D., Department of Computer Science, University of Maryland, Baltimore, USA.MooudDabaghiroodsariPh.D., Department of Computer Science, University of Maryland, Baltimore, USA.0009-0000-3827-7740Journal Article20260504Objective: Among the different types of innovation that firms can pursue, frugal innovation has attracted practitioners in the sustainability domain. Frugal innovation can improve the Circular Economy by driving resource efficiency, cutting waste, and promoting the reuse and recycling of materials. However, the existing research has not drawn consistent conclusions about the relationship between FI and CE. Hence, in this study, an effort has been made to aggregate the quantitative results obtained through a systematic review and meta-analysis method to determine the overall impact of FI on the CE. Methodology: The paradigm of this research is positivist, its reasoning method is deductive, and its approach is quantitative. The study population consisted of English-language articles published between 1979 and 2025 in the Scopus and WoS databases. Using the PRISMA method, out of 876 studies on the impact of FI on the CE, a final number of 6 studies were selected based on the inclusion and exclusion criteria defined by the researchers, and their data were included in the final analysis. Results: The meta-analysis conducted using the Comprehensive Meta-Analysis software (CMA3) showed that, overall, FI has a significant positive impact on CE. Also, the studies were heterogeneous, and the effect size's value fell within the medium range (Based on Cohen’s criterion). Additionally, Egger’s linear regression method rejected the hypothesis of publication bias, and the results of the fail-safe N method indicated the robustness of the study’s findings.Conclusion: This meta-analysis demonstrates that frugal innovation is a powerful engine for transition to a circular economy and provides a pragmatic pathway where sustainability intersects with economic viability and strategic innovation. By embracing the “more with less” paradigm, businesses can unlock circularity as a source of resilience, efficiency, and long-term value creation.https://imj.ut.ac.ir/article_108607_5944f723b73cf0280299ff653694b341.pdfUniversity of Tehran PressIndustrial Management Journal3115-738618420261001Computational Discovery of Research Investment Patterns in Supply Chain Management: An Unsupervised Learning Framework for Funding Theme Extraction and Evolutionary Trajectory Analysis10414210862510.22059/imj.2026.413257.1008311ENMehrdadMaghsoudiPh.D. Candidate, Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.0000-0002-1896-1825SadraAhmadiAssistant Prof., Cyberspace Research Institute, Shahid Beheshti University, P.O. Box 1983963113, Tehran, Iran.0000-0003-0032-6839AlirezaMotameniAssistant Prof., Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.AliOtarkhaniAssistant Prof., Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.Journal Article20260424Objective: This study maps global research investment patterns in supply chain management by identifying latent funding themes and their temporal trajectories.
Methodology: This study analyzed 7,956 quality-assured grant records retrieved from Web of Science in June 2026, covering award years from 1963 to 2026. The framework combines systematic deduplication and quality filtering, multilingual transformer embeddings, K-means clustering with multi-metric validation, cluster-level TF-IDF, GERD normalization, right-censoring correction, and lifecycle sensitivity analysis.
Results: The optimal solution contains 13 thematic clusters. Five themes show GERD-normalized growth above 67%: bioenergy value chains (172%), quantum infrastructure (95%), battery supply networks (94%), additive manufacturing (83%), and cybersecurity systems (67%). Portfolio analysis assigns 37.6% of grants to emerging themes and 7.6% to mature themes, compared with the study benchmark of 25% and 30%, respectively.
Conclusion: Grant-based science mapping provides forward-looking intelligence on supply chain research priorities. The results indicate rapid investment in sustainability and advanced technologies while suggesting a relative shortage of mature-stage translational research.https://imj.ut.ac.ir/article_108625_b9aaf2e085c8792b9890c85214a7edb4.pdfUniversity of Tehran PressIndustrial Management Journal3115-738618420261001Resilient Blood Supply Systems under Demand Surges: A Scenario-Based Robust Inverse Data Envelopment Analysis Approach14317310862810.22059/imj.2026.415751.1008323ENSeyed HosseinRazavi HajiaghaAssociate Prof., Department of Management, Faculty of Management and Financial Science, Khatam University, Tehran, Iran0000-0003-2084-7244MohammadMozaffariMSc., Department of Management, Faculty of Management and Financial Science, Khatam University, Tehran, Iran.0009-0003-2913-722XJournal Article20260602Objective: This study proposes an approach to maintain the efficiency of the blood transfusion system in response to increasing demand caused by disruptive events. The primary objective is to determine the necessary input adjustments for the blood transfusion system to accommodate the expected increase in its output. Methodology: To achieve the study objectives, an inverse data envelopment analysis (IDEA) model is extended to a scenario-based robust counterpart. Considering different scenarios with increasing blood demands and specified probabilities of occurrence, the model is formulated to maintain the efficiency levels while preserving the optimality and feasibility of the model under the considered scenarios.Results: The proposed model is applied to a real case in the provinces of Iran. After identifying the system inputs and outputs, a set of scenarios was defined to represent an increase in blood demand. Solving the model illustrates that the system requires an increase in the number of donation centers in a range of 0 to 46 across different provinces, while the percentage of donors to the province's population must increase by at most 18% in some provinces. These adjustments help preserve the current level of blood transfusion efficiency.Conclusion: The present study proposes a novel scenario-based robust inverse data envelopment analysis (DEA) framework for evaluating the efficiency of blood transfusion centers under varying scenarios of increased demand. By analyzing multiple disruption levels, the proposed framework enables decision-makers to determine the necessary resource adjustments to maintain operational efficiency and develop effective plans for managing uncertainty.https://imj.ut.ac.ir/article_108628_cc89223c28a94277df25fe218cc1eb42.pdfUniversity of Tehran PressIndustrial Management Journal3115-738618420261001Mapping the intellectual structure of Industry 4.0, circular economy, and sustainable performance: A bibliometric review of knowledge evolution, emerging themes, and future research directions17420410864410.22059/imj.2026.417040.1008327ENMaryamHeidariPh.D. Candidate in Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.0009-0007-9408-1778AkbarAlem TabrizProf., Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.0000-0002-4562-8032MostafaZandiehProf., Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.0000-0003-1209-9514DavoodTalebiAssistant Prof., Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.0000-0002-2780-8320Journal Article20260621Objective: Research at the nexus of Industry 4.0, the circular economy (CE), and sustainable performance (SP) has gained considerable momentum within the broader fields of industrial transformation and sustainable development. To address this gap, the present study systematically maps the intellectual structure underlying the convergence of Industry 4.0, CE, and SP, identifies major thematic clusters, pinpoints structural deficiencies in the literature, and outlines promising directions for future inquiry.
Methodology: A bibliometric analysis was conducted using VOS viewer software on 741 articles indexed in the Scopus database from 2016 to 2026, covering eight analytical dimensions.
Results: Keyword co-occurrence analysis shows that SP holds a central, highly connected position within the knowledge network. Accordingly, the existing literature frequently examines SP in tandem with Industry 4.0 and CE frameworks, typically treating SP as an outcome of adopting Industry 4.0 technologies and implementing CE practices.
Conclusion: The analysis reveals notable knowledge voids concerning "reverse logistics" and "smart manufacturing". Conversely, "social sustainability," "digital product passport," and "Industry 5.0" emerge as rising research trends. Scientific collaboration analysis further indicates that the United Kingdom plays a pivotal role in the international research cooperation network within this field.https://imj.ut.ac.ir/article_108644_857642fac3f87b8b9b47fd6cd5edc5c8.pdfUniversity of Tehran PressIndustrial Management Journal3115-738618420261001Managing Agility and Efficiency under Uncertainty in Supply Chains: A Dynamic Capabilities Framework Based on Fuzzy Cognitive Maps20523410864510.22059/imj.2026.417468.1008328ENMohammad RezaSadeghi MoghadamAssociate Prof., Faculty of Industrial and Technology Management, College of Management, University of Tehran, Tehran, Iran.0000-0002-9584-581XTahaMansouriAssociate Prof., School of Science, Engineering & Environment, the University of Salford, Salford, Greater Manchester, UK.0000-0003-1539-5546AmirhoseinMardaniPh.D., Faculty of Economics and Management, Tarbiat Modares University, Tehran, Iran.0000-0003-2694-7457MahdiAzzaviMSc., Department of Performance Management, Faculty of Economic and Management, University of Qom, Qom, Iran.0000-0002-4573-1486Journal Article20260628Objective: Information and Communication Technology (ICT) companies operate in highly dynamic environments characterized by rapid technological change and substantial uncertainty. Although organizational agility is widely recognized as an effective response to uncertainty, achieving agility often requires sacrificing efficiency, creating an enduring agility–efficiency tradeoff. This study aims to develop a quantitative framework for analyzing this tradeoff from a dynamic capabilities perspective.
Methodology: A fuzzy cognitive map (FCM) approach was employed to model the causal relationships among dynamic capabilities, organizational agility, and efficiency. Research variables were identified through a comprehensive literature review and refined through expert panels. The FCM was developed based on the judgments of 30 experts from 12 leading Iranian ICT companies, including chief executive officers, marketing managers, technical managers, and senior strategy specialists.
Results: The findings indicate that dynamic capabilities play unequal roles in managing the agility–efficiency tradeoff. Sensing, seizing, and transformation capabilities exert different levels of influence on organizational agility and efficiency. The simulation results further show that alternative capability development strategies lead to different organizational outcomes under conditions of uncertainty.
Conclusion: The proposed FCM-based framework provides a quantitative decision-support tool for understanding and managing the agility–efficiency tradeoff in uncertainty-driven industries. The study contributes to the dynamic capabilities literature by integrating uncertainty management, organizational agility, and efficiency within a unified analytical framework.https://imj.ut.ac.ir/article_108645_82261c4d50a370d548ed49b8d4c8cc95.pdf