Integrating fuzzy analytic hierarchy process with PROMETHEE method for total quality management consultant selection
Bibliographic record
Abstract
Evaluation of the proper and appropriate consultants can play an important role in successful total quality management (TQM) program implementation and helps the manufacturing organizations to attain competitive advantage. In general, many conflicting factors affect the appropriate consultant selection problem which adheres to uncertain and imprecise data. In this paper, a simple, systematic and logical scientific approach is structured to evaluate TQM consultant through integrating Fuzzy Analytical Hierarchy Process with the Preference Ranking Organization Method for Enrichment Evaluations. The proposed decision-making approach takes advantage of the synergy between these two well-known multi-criteria decision-making methods in which their strong and weak points are detected and a ranking is provided which facilitates the final selection for the decision-maker. To accredit the proposed model, it is implemented in a furniture industry in Bangladesh. The results indicate that technical/administrative is the most significant criteria whereas work experience in related field is the most important sub-criteria.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".