Artificial intelligence as a key to improving the efficiency of logistics operations
Bibliographic record
Abstract
The article examines the application of artificial intelligence (AI) in warehouse process management and its impact on the economic efficiency of logistics companies. The main areas of AI utilization, including demand forecasting, inventory management, robotics, and computer vision technologies, are analyzed. Special attention is paid to the experience of logistics companies such as DHL, Walmart, and X5 Group, which have successfully integrated AI into their operations. The article also explores examples of AI use in seaports, such as the Port of Los Angeles, where technologies have enhanced cargo flow management. The article presents the results of a survey conducted among logistics industry professionals, which identified the level of AI adoption, key areas of application, and expected benefits. It discusses both the advantages, such as increased accuracy and reduced processing time, and the challenges, including implementation costs and the shortage of qualified specialists. The role of AI in reducing operating costs and accelerating data processing in large-scale logistics chains is emphasized. As a result, the application of AI in logistics, while requiring significant investment, is transforming traditional management practices and leading to more efficient and sustainable operations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".