Research on cost control and resource optimisation in supply chain link based on combination prediction model
Bibliographic record
Abstract
The supply chain applies large number according to the technology, can reduce the cost of each link, optimize the resource allocation, increase the enterprise benefit. In this paper, the supply chain cost control program based on large number according to the previous forecast, the control of the event and the analysis of the three levels of the analysis of the supply chain. The combination time series model and the multivariate regression model, the joint CPFR concept, the establishment of the CPFR sales combination demand prediction model, the design form according to the sales prediction system, the resource optimization plan of the supply chain inventory in real time. Analyze the prediction effect of the combination prediction model, predict the product sales in the week, calculate the product safety inventory and the remaining inventory. The analysis is based on the cost control effect of the enterprise supply chain according to the sales forecast. The cost of purchasing the supply chain costs less than the operating income, which fell to 0.5107in 2023. The gross margin of gross profit was 0.53666 in 2023, which was controlled by the gross margin, and the gross profit was improved. It is said that the enterprise is using large number according to the technology to the supply chain resource optimization in the supply chain cost control, the cost control effect is better.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".