Game—The Online Wood Supply Game
Bibliographic record
Abstract
This paper describes the Wood Supply Game (WSG), a prize-winning e-learning tool that is freely available for players all over the world. The game effectively helps students and managers realize the challenges in managing demand and supply in wood supply chains, and gain insight into the types of measures required to make these divergent chains effective. The WSG is an adaptation of the Beer game, a popular didactic tool used to empirically demonstrate demand amplification in a simple and generic context. The supply chain modeled by the Beer game does not involve co-products, and thus is very different from the wood supply chain, which is divergent by nature. The WSG presented in this paper models a supply network with one point of divergence and demand for two products. This preserves the simplicity of the game but enables it to offer a base for supply network simulation in a large number of industrial sectors with divergent processes. We describe an online version of the WSG, discuss our experiences playing it with students and managers, and provide hints to the instructor.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".