Kriging analysis of an integrated demand management process in softwood industry
Bibliographic record
Abstract
Objective: This paper aims to develop a basic understanding of a demand management process integrating sales and operations planning (S&OP) and order promising in a Make-To-Stock environment and to compare different demand management policies. Contribution: Typical researches about demand management processes analyze few system specifications or vary few potential factors one at time. Yet, we can get additional insights by employing design of experiments (DOE). Methodology: For making promises, we compare a First-Come First-Served approach to an approach using nested booking limits and giving advantage to profitable customers and attractive periods. Considering various sequences of order arrival, we generate Kriging metamodels that best describe the nonlinear relationships between the simulation responses and system factors for Canadian softwood lumber firms. We employ a Latin hypercube design to take into account different environmental scenarios. Results: Our analysis reveals the potential to improve the performance of the demand management process if we know high-priority customers needs before fulfilling less-priority orders and if we use nested booking limits concept.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".