OCP Optimizes Its Supply Chain for Africa
Bibliographic record
Abstract
Operations research specialists at the OCP Group, the Mohammed VI Polytechnic University, and Polytechnique Montreal operationalized a system that optimizes the OCP downstream supply chain operations. The system simultaneously schedules production, inventory, and vessels, ensuring the highest demand fulfillment level. To operationalize the system, the team equipped it with various heuristic and exact operations research tools. These tools provide the user with satisfactory schedules. Furthermore, inspired by the practice, the team implemented a novel hybrid variant of Benders decomposition, which consists of fixing some complicating variables related to confirmed orders and freeing others related to unconfirmed orders in the Benders subproblem. The system has become central to the OCP planning process. Planners use the optimizer’s solutions and insights to improve plans in different OCP sites. Initially, the system was a bottleneck, curbing the use of other supply chain management tools. OCP management now credits the system operationalization with providing operational benefits, contributing to more than a $240 million increase in annual turnover. History: This paper was refereed. Funding: This work was supported the Institute for Data Valorisation [Grant PhD-2021-6414389718], Fonds de recherche du Québec–Nature et technologies [Grant 304395], and the Office Chérifien des Phosphates Group.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".