Integration of EFQM and Ultimate Six Sigma: A Proposed Model
Bibliographic record
Abstract
The aim of this article is to propose a comprehensive integrated model of the EFQM, i.e. European Quality Award, and USS i.e. the Ultimate Six Sigma in order to take more advantage from both of the models simultaneously towards improving organizational performance and excellence. For this purpose, the literature has been reviewed and the structure and criteria of the models have been compared. Then, an integrated model has been developed, in which the USS has been modified and restructured compatible with the EFQM model. The results imply that the proposed integrated model includes simultaneously the criteria of both EFQM and USS models and can be used as an appropriate reference model for assessing organizations in their ways towards excellence. The proposed model involves nine major criteria similar to the EFQM model and 56 sub-criteria similar to the USS model. The criteria have been classified into two categories of enablers and results. Out of 1000 scores of the new model, 795 and 205 scores have been allocated to the enablers and results, respectively, which is different from the EFQM model in which the sum of scores of enablers and results are divided equal (i.e. 500).
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".