Petri net model for supply‐chain quality conflict resolution of a complex product
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This paper aims to develop a Petri net model for analyzing the quality conflict and its resolution of a complex product. The result aims to assist decision makers (DMs) to properly select their activities when a quality conflict has happened. Design/methodology/approach According to the features of Petri net and conflict analysis theory, a novel Petri net for conflict analysis (PNCA) is designed which contains transition and preference labels to describe DMs' decision activities and profit comparisons. Additionally, a generating approach is proposed, which can help DMs to construct a PNCA. Furthermore, based on players' bounded rationality, the equilibrium of PNCA is studied to provide scientific supports for DMs' decision‐making. A case study on an aircraft production system is conducted to demonstrate the feasibility and effectiveness of the new model, which furnishes a fresh perspective on the supply chain quality management of a complex product. Findings A new methodology is proposed for the domain of conflict analysis, which is easier to understand and improves the operation efficiency. What is more important, DMs can clearly be aware of their following choices according to the corresponding transition information. Originality/value The paper contributes to conflict analysis theory by designing a new model and develops a new graph model for managing the supply chain quality of a complex product.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it