The Impact of Smart Warehousing and Last-Mile Delivery on E-commerce Supply Chain Performance: An Empirical Study Using Machine Learning-Enhanced SEM Analysis
Bibliographic record
Abstract
This study investigates the pivotal roles of smart warehousing and last-mile delivery in enhancing e-commerce supply chain performance, utilizing advanced machine learning-enhanced Structural Equation Modeling (SEM) for analysis. The findings reveal that the effective integration of smart warehousing solutions significantly improves operational efficiencies in inventory management, order fulfillment, and logistics responsiveness. Moreover, optimizing last-mile delivery emerges as a critical factor directly influencing customer satisfaction and competitive advantage within the digital marketplace. The study highlights practical implications for e-commerce practitioners, emphasizing the necessity for investments in innovative technologies and the development of strategic partnerships to optimize logistics processes. Additionally, this research encourages scholars to further explore the intersection of intelligent logistics solutions and supply chain performance through longitudinal studies and diverse methodological approaches. Ultimately, this research contributes to the understanding of supply chain dynamics in e-commerce and serves as a foundation for future inquiries into enhancing performance through the strategic deployment of advanced technologies.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".