Digital Twin-Driven Intelligent Collaborative Automation Model for Global Warehouse Networks and Application Validation
Bibliographic record
Abstract
In the context of accelerated globalization, multinational logistics companies are confronted with significant challenges in global warehousing networks, including information latency, resource misallocation, and inefficient collaboration. This study addresses these issues by introducing digital twin technology to construct a virtual mirroring system for the overseas warehouse layouts of DongGuan Kreen Import and Export Co., Ltd. (with branches in the United States, Canada, Germany, and Vietnam). The digital twin system enables real-time collaboration and automated scheduling across global warehousing nodes, thereby enhancing the resilience of the global supply chain. The research is centered on three main aspects: First, a dynamic digital twin model is developed based on the physical space, equipment status, and business data of global warehousing nodes, achieving millisecond-level synchronization between physical and virtual warehouses. Second, intelligent resource allocation algorithms, automated cross-continental transfer decision mechanisms, and autonomous abnormal event response processes are designed to upgrade warehouse collaboration from a “passive execution” to an “active prediction” mode. Third, the digital twin model is validated in the context of “Amazon FBA headhaul logistics,” focusing on its collaborative efficiency in global warehouse stocking, replenishment, and return/exchange processes, with an emphasis on improving the response speed of cross-border e-commerce supply chains. The study demonstrates that the digital twin-driven intelligent collaborative automation model for global warehouse networks significantly enhances collaborative efficiency and supply chain resilience, forming a technical standard and practical paradigm for digital twin automation collaboration in global warehouses. This provides technological support for multinational logistics companies to address global warehousing collaboration challenges and enhances China’s scheduling discourse power in the global supply chain.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".