Investigating readiness in the Iranian steel industry through six sigma combined with fuzzy delphi and fuzzy DANP
Bibliographic record
Abstract
In today's competitive world, Six Sigma can serve as a corporate strategy to improve quality and reduce costs.Six Sigma is a comprehensive and flexible system for achieving, maintaining and maximizing organizational success, through an improved performance of processes.However, this tool can only be successful in organizations which are already prepared for Six Sigma implementation.This study investigated the readiness of an Iranian company, Tabarestan Steel Co., in terms of Six Sigma projects.In doing so, the study proposed a model designed for assessing the readiness of Six Sigma in organizations.To this end, primarily Six Sigma readiness indicators were identified through Fuzzy Delphi, by recourse to the experts' opinions.Next the interrelationships between the indicators were examined through the Fuzzy DEMATEL method.The internal links between the indicators were determined after the examination, and the degree of importance and the weights of the indicators were decided.Ultimately, three highly important dimensions were discovered that had a significant function in Six Sigma implementation (leadership and perspective, corporate strategy, and focus on the customer)..
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".