Integrating operational planning decisions throughout the forest products industry supply chain under supply and demand uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
In the face of both supply and demand uncertainty, the forest products industry needs advanced supply chain management models that can significantly improve their competitiveness in global markets. This paper aims to provide a decision support tool for integrating operational planning decisions with inventory management of all agents in a multi-product forest industry supply chain under supply and demand uncertainty. A pulp mill is considered as the nodal agent and an integrated simulation-based optimization model is developed, which minimizes the cost of the entire supply chain for different customer satisfaction levels, while material and information flow both upstream and downstream of the pulp mill. The incorporation of a merchandizing yard helps in managing risks associated with supply and demand uncertainty in the forest products industry supply chain. There is a net annual cost saving of $17.4 million by including a merchandizing yard in the supply chain. However, there is an increase in handling, holding and transportation costs. Comparing the shortage cost to the handling, holding and transportation costs, it is observed that as long as the shortage cost is above $6.80 per m3, it is viable to keep the merchandizing yard. The merchandizing yard not only absorbs supply shocks for the pulp mill, but also reduces the safety stocks on the downstream side. This integrated supply chain model can be used for operational planning decisions that minimize overall cost for any agent in the supply chain.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it