Generative AI in Supply Chain Resource Orchestration: A Conceptual Perspective
Bibliographic record
Abstract
Generative Artificial Intelligence (GAI) revolutionizes supply chain management (SCM) by facilitating resource orchestration via predictive analytics, intelligent resource allocation, network synchronization, and ongoing learning. This research relies on Resource Orchestration Theory (ROT) to examine the role of GAI in organizing, integrating, and utilizing resources within SCM. A systematic literature review (SLR) was performed, incorporating findings from 32 research papers to propose a conceptual framework that aligns GAI-enabled capabilities with resource orchestration processes, specifically elucidating how GAI facilitates the structuring, bundling, and leveraging of resources. Then, this proposed paradigm demonstrates the relationship between GAI’s predictive and adaptive capabilities and the essential processes of resource orchestration, offering a systematic method for comprehending its contribution to improving supply chain operations. The results highlight GAI-enabled capacities to enhance decision-making, optimize resource distribution, and bolster supply chain resilience and efficiency. The study improves theoretical understanding by extending the relevance of ROT to digital supply chains and provides practical insights for managers seeking to integrate GAI into supply chain operations. This study identifies GAI as a crucial enabler of competitive advantage in dynamic supply chain environments by integrating technical capabilities with resource management tactics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".