Rationalizing the cost of quality through lean and agile operations practices: Evidence from Aviation industry
Bibliographic record
Abstract
This research aims to account for the cost of quality through lean and agile operations strategies with empirical evidence from the Aviation industry in the UAE. The cost of quality improvement through lean and agile strategies was not researched enough in the aviation industry. This research will contribute with great knowledge in the aviation industry. The research design used a descriptive, explanatory, causal, and analytical method. A cluster sampling technique was used with a valid sample size of 251 respondents for analysis by multiple regression using ANOVA. Results indicated the significant relationship between lean and agile operations strategies on the cost of quality. There is a direct positive significant relationship between the cost of quality and SC strategies to get quality products cost-effectively. This research was limited to the lean and agile strategies and cost of quality analysis in the aviation industry in one city in the UAE. In contrast, it requires detailed research to explore other cities and assess the aviation industry's challenges while implementing lean and agile strategies. Cost elimination with high-quality production is fundamental for a successful business; lean and agile operations can reduce airline companies' costs and propose the criteria to adopt the strategic implementation efficiently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".