A Fuzzy Data-Driven framework for Enhanced risk management Decision-Making in Manufacturing: A Case study
Bibliographic record
Abstract
In today’s fast-paced and competitive business world, companies are constantly looking for ways to increase their profits by reducing disruptions and failures. This research examines risks within a manufacturing company to facilitate sustainable growth. To achieve this, possible failures were identified using a combination of Risk Priority Number (RPN) criteria, improved by Fuzzy Shannon’s Entropy, through group decision-making. Then, a framework based on Multi-Criteria Decision Making (MCDM) and Failure Mode and Effects Analysis (FMEA) was developed to assess and prioritize potential failures. The study highlights the necessity of analyzing the interplay between various risk assessment indicators, including the costs associated with failures, all while considering uncertainties through fuzzy modeling. These factors significantly influence how failures are ranked for risk management strategies. The methodology demonstrated effectiveness, particularly in prioritizing costly failures. Additionally, this research introduces an innovative aspect of risk assessment by integrating the confusion matrix concept from Machine Learning (ML) for data classification and exploring statistical correlations. The results revealed that the aggregated data ranking was most effectively matched and influenced by the Weighted Aggregated Sum Product Assessment (WASPAS) method, reaching significant recall and precision metrics rates.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".