Bibliographic record
Abstract
Purpose The purpose of this paper is to combine systems thinking, lean management, value methodology and Six Sigma concepts into an integrated quality methodology using the TALEVAS model. Design/methodology/approach TALEVAS is an acronym for Tandem‐Lean‐Value‐Sigma, as each element correlates to a best practice or concept mentioned by intent. The model is based on two theories: “The rising pendulum system” and “The seven rules of quality driving” proposed in this paper. Findings Four key performance drivers are identified using the model. These are: communication, investigative correction, innovation, and synchronization. Practical implications The integrated methodology can be deployed by any type (product‐or‐service based) or level (small, medium or corporate) of an organization in order to gain a competitive advantage in the market. Further, there is a possibility that recent cases of product recalls could be reduced or avoided by companies through implementing a TALEVAS Quality approach. Originality/value The paper displays the interdependence between the quality concepts by model analysis. This reflects a more holistic approach to quality required by organizations to raise the bottom line, reduce costs, promote value, and provide consistent products to customers.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".