IMPROVING THE QUALITY OF COVERS FOR SOFT FURNITURE MANUFACTURING PROCESS
Bibliographic record
Abstract
The paper presents a case study regarding the analysis of the quality of the manufacturing process of soft furniture covers within the company "Z" Ltd, which operates on the territory of the Republic of Moldova. Starting from the fact that the products are destined for export to the EU, USA, Canada, China and the Russian Federation, it is obvious that their quality must be ensured at the highest level. The research methodology included the application of Shewhart p-type statistical control charts for the quality indicator - the percentage of the defective products, identified at the final control of manufactured products for a period of two consecutive years. It was found that the manufacturing process is organized at a high level, being equipped with high-performance equipment. The company has implemented a Quality Management System according to ISO 9001, but its efficient capitalization is diminished by staff turnover. The research results indicate that overall the manufacturing process is not kept under statistical control, because the variation of the analyzed quality indicator exceeds the calculated control limits of the process. It is recommended to develop measures to improve the quality of processes and products through actions aimed at directly productive staff and methods of work organization.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".