Evaluation of the integration level of quality and environmental management systems in a tire manufacturer
Bibliographic record
Abstract
Purpose This article aims to evaluate the integration level of a quality management system (QMS) and an environmental management system (EMS) in a tire manufacturer and propose a guide to evaluate the integration of these systems in companies. Design/methodology/approach The methodological strategies used in this research were literature review; and case study, with interviews to verify professionals' perception about benefits from integration. Data from interviews were analyzed through Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Findings The results showed that the studied company has a simple level of integration, observing only some low intensity benefits. Thus, it was recommended that the company partially integrate its management systems (MSs) before evolving into something more complex. The literature and the findings of case study were used as basis for proposing a guide to evaluate MS integration. Originality/value Lessons learned throughout the study and the suggested guide can support other companies to assess the integration level of their QMS and EMS. Thus, the findings presented here can be useful for researchers and managers.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".