Quality Management and Sustainability in the Defense Industry: Synergies and Challenges for a Sensitive Sector
Bibliographic record
Abstract
Objectives: This article analyzes how quality management practices can be integrated with sustainability in the defense industry, aiming to balance effectiveness, safety, and socio-environmental responsibility in a strategic and sensitive sector. Theoretical Framework: The study is based on international standards such as ISO 9001 (quality management) and ISO 14001 (environmental management), as well as regulations like the REACH Directive, the Montreal Protocol, and the Paris Agreement. It also discusses environmental impacts and strategies such as circular economy, renewable energy use, and Kaizen methodology. Method: A mixed-methods approach (qualitative and quantitative) with a descriptive and exploratory character was adopted. Data were collected through a structured questionnaire applied to 18 employees of a Brazilian defense company, including closed-ended (Likert scale) and one open-ended question. Results and Discussion: Findings indicate a positive perception of the integration between quality and sustainability: 67% perceive strong integration, 66% report improved product quality, and 72% highlight a positive impact on institutional image. Main challenges include lack of financial resources (44%), insufficient training (17%), and limited leadership support (11%). Success cases like IMBEL, Embraer, and Helibras show that sustainable practices can reduce costs, improve efficiency, and strengthen corporate image. Research Implications: The integration of quality and sustainability is feasible and strategic, promoting innovation, efficiency, and legal compliance. Results can guide public policies and business practices in the defense sector. Originality/Value: The study shows that quality and sustainability are complementary. Its originality lies in the integrated approach applied to a traditionally change-resistant sector, offering practical paths for sustainable modernization.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".