University of Tehran Press Industrial Management Journal 3115-7386 18 4 2026 10 01 Computational Discovery of Research Investment Patterns in Supply Chain Management: An Unsupervised Learning Framework for Funding Theme Extraction and Evolutionary Trajectory Analysis 104 142 108625 10.22059/imj.2026.413257.1008311 EN Mehrdad Maghsoudi Ph.D. Candidate, Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran. 0000-0002-1896-1825 Sadra Ahmadi Assistant Prof., Cyberspace Research Institute, Shahid Beheshti University, P.O. Box 1983963113, Tehran, Iran. 0000-0003-0032-6839 Alireza Motameni Assistant Prof., Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran. Ali Otarkhani Assistant Prof., Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran. Journal Article 2026 04 24 Objective: 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.pdf