Artificial intelligence as a key to improving the efficiency of logistics operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it