A mixed integer programming model to evaluate integrating strategies in the forest value chain — a case study in the Chilean forest industry
Bibliographic record
Abstract
When a company is integrated vertically, it can manage and plan its overall value chain in one direct and integrated approach. However in many cases, companies follow a decoupled approach where forests and production plants optimize separately their processes in a supply-driven strategy. In Chile, the two largest forest companies are vertically integrated (i.e., they own forest and mills that produce logs, lumber, plywood, pulp, paper, and bioenergy, etc.). Historically, they have coordinated their value chains using a make-to-stock strategy, for which the forest is the main driver of the value chain activities. In this paper, we propose an integrated planning approach to show the impacts of a demand-driven integration of the value chain in the forest industry. To compare this strategy with the decoupled strategy, we propose a mixed integer programming (MIP) model for the integrated strategy. To illustrate our proposal, we use forest and production information from a Chilean forest company. The decoupled strategy, where the forest and industry planning are planned separately, uses two models. The first model deals with the forest management and harvesting decisions and maximizes the expected net present value (NPV) of logs. In this model, the planning horizon covers one full forest rotation, which in Chile corresponds to about 25 years. The second model maximizes the NPV of the downstream operations for a shorter business planning horizon (five years) constrained by the availability of the logs from the first model. In the integrated approach, all parts of the value chain (forest, transportation, and mills) are driven by final product demand and where the objective is to maximize the profit of the company (NPV of the entire value chain). The demand is only given for the shorter business planning horizon. The two strategies are evaluated using the MIP model, and NPV is used to determine the best practice. According to the results, the NPV can increase up to 5.0% when the proposed integrated strategy is implemented compared to a decoupled strategy. Moreover, the profit for the business period increases up to 8.5%.
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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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".