Seasonal Inventory Management Model for Raw Materials in Steel Industry
Bibliographic record
Abstract
We developed a seasonal inventory management model for raw materials, such as iron ore and coal, for multiple suppliers and multiple mills. The Nippon Steel Corporation imports more than 100 million tons of raw material annually by vessels from Australia, Brazil, Canada, and other countries. Once these raw materials arrive in Japan, they are transported to domestic mills and stored in yards before being treated in a blast furnace. A critical problem currently facing the industry is the limited capacity of the yards, which leads to high demurrage costs while ships wait for space to open up in the yards before they can unload. To reduce the demurrage costs, the inventory levels of the raw materials must be kept as low as possible. However, inventory levels that are too low may lead to inventory shortage resulting from seasonal supply disruptions (e.g., a cyclone in Australia) that delay the supply of raw materials. Because both excess and depleted inventory levels lead to increased costs, optimal inventory levels must be determined. To solve this problem, we developed an inventory management model that considers variations on the supply side, differences that should be observable upon looking at the ship operations. The concept is to model the probability distribution of ship arrival intervals by brand groups and mills. We divided ship operations into two stages: arrival at all mills (in Japan) and arrival at individual mills. We modeled the former as a nonhomogeneous Poisson process and the latter as a nonhomogeneous Gamma process. Our proposed model enables inventory levels to be reduced by 14% in summer and 6% in winter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".