Supply chain management of the Canadian Forest Products industry under supply and demand uncertainties: a simulation-based optimization approach
Bibliographic record
Abstract
The Canadian forest products industry has failed to retain its competitiveness in \nthe global markets under stochastic supply and demand conditions. Supply chain \nmanagement models that integrate the two-way flow of information and materials under \nstochastic supply and demand can ensure capacity-feasible production of forest industry \nand achieve desired customer satisfaction levels. This thesis aims to develop a real-time \ndecision support system, using simulation-based optimization approach, for the Canadian \nforest products industry under uncertain market supply and demand conditions. First, a \nsimulation-based optimization model is developed for a single product (sawlogs), single \nindustry (sawmill) under demand uncertainty that minimizes supply chain costs and finds \noptimum inventory policy parameters (s, S) for all agents. The model is then extended to \nmulti-product, multi-industry forest products supply chain under supply and demand \nuncertainty, using a pulp mill as the nodal agent. Integrating operational planning \ndecisions (inventory management, order and supply quantities) throughout the supply \nchain, the overall cost of the supply chain is minimized. Finally, the model integrates \nproduction planning of the pulp mill with inventory management throughout the supply \nchain, and maximizes net annual profit of the pulp mill. \nIt was found that incorporation of a merchandizing yard between suppliers and \nforest mills provides a feasible solution to handle supply and demand uncertainty. \nAlthough the merchandizing yard increases the total daily cost of the supply chain by \n$11,802 in the single industry model, there is a net annual cost saving of $17.4 million in \nthe multi-product, multi-industry supply chain. Under supply and demand uncertainty \nwithout a merchandizing yard, the pulp mill is only able to operate at 10% of its full \ncapacity and achieve a customer satisfaction level of 9%. The merchandizing yard \nensures pulp mill running capacity of 70%, and customer satisfaction level of at least \n50%. However, the merchandizing yard is economically viable only, if the sales price of \npulp is at least $680 per tonne. Efficient and effective management of inventory \nthroughout the supply chain, integrated with production planning not only ensures \ncontinuous operation of forest mills, but also significantly improves the customer \nsatisfaction.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".