Agent-Based Simulation Of The Amplification Of Demand Variability In A Supply Chain
Bibliographic record
Abstract
A supply chain is the set of companies producing or carrying products to customers. In such a supply chain, the bullwhip effect is the amplification of demand variability that is distortion in information when this information travels from one end of a supply chain to the order. Inefficiencies which are due to this effect are excessive inventory poor customer service ineffective transportations, missed production schedules…. A game called the Beer Game is a widely used classroom exercise for demonstrating the dynamics in a supply chain. We focus on an adaptation of this game to the forest industry : the Québec Wood Supply Game. We has simulated this game in a spredsheet program : this first implementation is the base of the multi-agent simulation presented in this paper where intelligent agents represent companies. These agents will simulate how comapanies order, produce and store products. In this paper. We describe the supply chain model in the Quebec Wood Supply Game and how we will make it more realistic with agents. However, we present neither our spreadsheet simulation of this game nor our solution to the bullwhip effect
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".