Kano-based Six Sigma utilising quality function deployment
Bibliographic record
Abstract
For any company, the continuous and timely development of new products, which include creative features expected to satisfy customers, is essential to stay competitive. Currently, companies are not only aiming at satisfying customers, but also at delighting them. In fact, some companies aim at customers' loyalty, such that they only buy and recommend their products. Thus, it is important to attain a comprehensive understanding of the dynamic requirements of customers. One of the key models used to achieve that is Kano model. It strengthens Six Sigma and enhances customer satisfaction. Six Sigma is used to reduce variability. This leads to an almost defect-free level which is the focus of the design for Six Sigma (DFSS) approach in building quality upstream. This level can be essential to customers but may not always be economic. Therefore, it is important to understand customer needs and the company's own capabilities. In this paper, an integrated approach to product development is proposed using a Kano-based Six Sigma, which utilises Six Sigma structure and quality function deployment (QFD). This approach will contribute to the innovation of new and existing products or services.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".