Application and Challenges of IoT Technology in the Logistics Economy: Efficiency Enhancement in Smart Warehousing and Automated Delivery Systems
Bibliographic record
Abstract
The rapid development of digital technology and artificial intelligence has made the improvement and optimization of intelligent warehousing and automated distribution systems important topics for research in modern logistics management.With this as the background, the current study uses a systematic approach to explore critical factors, innovative ways, and implementation strategies related to these factors and their role in improving the effectiveness of intelligent warehousing systems.The study adopts a mixed-methodological approach, establishing a comprehensive evaluation index system including operational efficiency, technical performance, and economic benefits, and simultaneously verifying the implementation of the system through empirical analysis.According to the findings, the intelligent warehousing system increased the efficiency of operations in relation to order processing time and had reduced it by 71.7%, and enhanced the accuracy of picking to 99.8%.The intelligent warehouse system by use of machine learning and meta-heuristic algorithms had greatly improved the efficiency in resources utilization and energy as storage utilization increased by 19.3% while energy consumption dropped by 31.4%.A cost-benefit analysis shows that, despite the significant up-front financial investment, the system achieved a 186% return on investment over three years.This research deepens the theoretical understanding of intelligent warehousing and, at the same time, provides optimization strategies applicable to industry practice.Future research directions should focus on exploring the applications of multi-agent digital twin technology and researching how intelligent warehousing systems contribute to supply chain resilience and sustainability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".