Construction and Practice of Supply Chain Optimization Decision System Driven by Artificial Intelligence
Bibliographic record
Abstract
This article focuses on the supply chain optimization decision-making system driven by artificial intelligence (AI), aiming at coping with the complex challenges faced by enterprise supply chain management and improving its operational efficiency and competitiveness. Through the combination of theoretical research and case practice, this article first expounds the theoretical and technical basis of supply chain management, AI and decision-making system. On this basis, the system is constructed in detail, covering demand analysis, architecture design and key module design, in which the key module adopts demand forecasting method combining time series with neural network, inventory optimization strategy based on EOQ model and transportation scheduling scheme of genetic algorithm. The practice in large-scale electronic product manufacturing enterprises shows that after the system is implemented, the inventory turnover rate of enterprises increases by 37.14%, the shortage rate decreases by 62.20%, and the transportation cost decreases by 18.33%. The research shows that the AI-driven supply chain optimization decision-making system can effectively solve the supply chain management problems of enterprises, which is of great value to improve the operational efficiency of enterprises, but some models still need to be optimized when dealing with extreme market changes.
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How this classification was reachedexpand
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".