Petri net model for supply‐chain quality conflict resolution of a complex product
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".