The effect of demand forecasting choices on the circularity of production systems: a framework and case study
Bibliographic record
Abstract
Designing Circular Economy systems needs previous decisions that depend on Planning and Operation decisions. One of these decisions relates to the forecasting demand method, because it scales the needs of the production flow and the necessary resources. This study investigates how the selection of forecasting methods affects the circularity of production systems and shows a case study of particleboard manufacturers. Scenarios with different parameters were tested. Results showed an increase in the Material Flow Analysis (MFA) indicators between the methods Linear Regression initiation (HW-LR) vs. Extended Additive Holt-Winters (EAWH) and variations in the MCI when restrictions on supply capacity are added in terms of recycled raw material use. Also, a positive correlation was found between the error rate in demand forecasting and MCI. These results highlight the relevance of choosing the forecast method due to its impact on production planning activities and environmental performance toward a more circular production system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".