Enhancing Operational Efficiency in E-Commerce Through Artificial Intelligence and Information Management Integration
Bibliographic record
Abstract
In the rapidly evolving domain of global e-commerce, operational organizations are increasingly grappling with unprecedented competitive pressures and challenges in operational efficiency.This study investigates the integration of artificial intelligence (AI) and information management as a transformative approach to augment operational efficiency in e-commerce organizations.The research delves into the optimization of organizational structures, leveraging the predictive capabilities of key information facilitated by AI.A novel decision-making model, integrating AI techniques, is developed to address the limitations inherent in existing decision-making technologies.The model's efficacy is demonstrated through a detailed case study of an e-commerce platform, where objectives and constraints for optimizing e-commerce operational organizations are meticulously constructed.The research identifies critical optimization points, including resource allocation, inventory management, logistics distribution, and customer relationship management.A distinctive organizational operation decision-making model, synergizing with the established Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), is proposed.This model focuses on optimizing variables pivotal to operational efficiency, with the dual goals of elevating efficiency and enhancing customer satisfaction.The findings underscore the significant role of AI technology in amplifying decision quality and boosting operational efficiency in e-commerce operational organizations, while also presenting potential applicability to a broader spectrum of organizational types.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".