Industrial Management Journal

Industrial Management Journal

Computational Discovery of Research Investment Patterns in Supply Chain Management: An Unsupervised Learning Framework for Funding Theme Extraction and Evolutionary Trajectory Analysis

Document Type : Original Research Article

Authors
1 Ph.D. Candidate, Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.
2 Assistant Prof., Cyberspace Research Institute, Shahid Beheshti University, P.O. Box 1983963113, Tehran, Iran.
3 Assistant Prof., Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.
10.22059/imj.2026.413257.1008311
Abstract
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.
Keywords
Subjects

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