Agent-Based Distributed Collaborative Sourcing of Stamped Parts
Bibliographic record
Abstract
Metal stampings are an important manufactured product. Their production cycle includes the steps of part design, process and tooling design, tooling construction, and production. Each of these activities may be performed within departments of a single manufacturing plant, but more commonly are separated across firms and are widely distributed geographically. To be most effective, this process should be performed concurrently, with considerable flows of information along the supply chain. Research on Agent-based systems shows they are very promising in this type of task as individual agents can wrap particular domain knowledge and analysis algorithms, are always available to respond and are able to autonomously form networks as needed to complete product and process design tasks. In this paper we examine the particular case where alternate suppliers may be selected to supply components to the supply chain. Agents are used to connect the suppliers to the supply chain network. Each supplier firm’s agent understands the firm’s production capabilities, costs and capacity loading, and is able to negotiate with a broker agent representing the stamped part customer. A prototype system is described, and case studies are used to show the benefits of an Agent-based approach to this supply chain problem.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".