Utilizing QFD model to determine quality characteristics of the products and priority needs of customers in the medical industry products (Case Study: Plasma seat product in mashhad`s Sahateb medical equipment company)
Bibliographic record
Abstract
Quality Function Deployment (QFD) as one of the quality engineering methods; originates from market study and product or service customers identification, where by determining their needs; tries to involve them in all stages of product or service development.This study uses QFD method to apply customers' criteria in production of Coach Plasma in Mashhad`s Sahateb Company.Coach Plasma is used for healthy bloodletting.The proposed study of this paper designed and distributed a questionnaire, which includes identification & determination of customers' needs and investigation of their satisfaction of manufactured products, while looking for technical and engineering characteristics related to their needs.The Coach Plasma costumers are categorized into two groups of local and external customers.Data collection was done based on available documents, experts opinions, structured interview with managers and questionnaire.Customers' needs were studied in QFD teams.Collecting essential information such as needs importance degree and competitive benchmarking of customer`s needs, the weight of each need has been evaluated.In this research, House of Quality was used from first matrix of QFD leading to estimation of engineering & technical characteristics in order to enter to the quality deployment matrix.Take a look at obtained results, we could mention the role of each of these external factors in satisfaction of Sahateb Company customers and technical characteristics of the company in providing these factors and the prioritization of the customer's needs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".