Inventory evaluation and product slate management in large‐scale continuous process industries
Bibliographic record
Abstract
Abstract Managing production and inventory for large‐scale continuous processing plants is a key to success in the chemical process industry, particularly in the production of commodity polymers such as polyethylene and polypropylene. When setting up a production schedule, planners must consider the effects of off‐grade production and production rate penalties, which are typically sequence‐dependent as products are transitioned from one to another, as well as inventory holding costs and capacity constraints. We call this the continuous economic lot sizing and scheduling problem (CELSP) to distinguish it from closely related problems found in discrete part production industries. We present a formulation that addresses the particular aspects of the CELSP and apply the proposed mathematical modeling approach to several plants of a chemical processing company. This company was concerned with ensuring the plants were carrying proper amounts of inventory and with evaluating the number of products produced at each plant. Our results show actual overall inventory levels were close to the levels suggested by our model and therefore company plans for further inventory reductions would not be appropriate. However, the company should carefully consider the addition of new products to current plant product slates. Due to the effects of product transitions and inventory levels, even adding products with significant contribution margins may negatively affect the financial performance of the plant.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".