A MULTI-LEVEL FRAMEWORK FOR DEMAND FULFILLMENT IN A MAKE-TO-STOCK ENVIRONMENT- A CASE STUDY IN CANADIAN SOFTWOOD LUMBER INDUSTRY
Bibliographic record
Abstract
This paper proposes a demand fulfillment process for Make-To-Stock environments, integrating sales and operations planning (S&OP) and order promising, for a commodity market characterized by prices and demand seasonality. Considering differentiated customers, different products and multiple sourcing locations in a multi-period context, we define a multi-level decision framework in order to support short and medium term sales decisions in a way to maximize profits and to enhance the service level offered to high-priority customers. Our research exhibits three valuable elements: (1) we developed an order promising model based on nested booking limits and which allows order reassignment i.e. changing decisions of how firm orders have to be fulfilled (2) we used a rolling horizon simulation to evaluate performance of the demand fulfillment process proposed and (3) we compare it with common fulfillment processes such first-come first-served order processing. In order to evaluate the demand fulfillment process proposed, a numerical application based on softwood lumber manufacturers located in Eastern Canada is conducted and provides evidence that better performances (overall service level, high-priority service level and overall net profit) can be achieved by using nested booking limits and reviewing previous order promising decisions whilst respecting sales commitments.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".