Supplier Selection by AHP in KMC Pharmaceutical: Use of GMIBM Method for Inconsistency Adjustment
Bibliographic record
Abstract
The supplier selection problem is one of the most important constituent for managers. There are some influential criteria in the selection of supplier and in this paper selection of supplier includes quality, cost, service, risk management and supplier profile. However, it is frequently impossible to find a best supplier in all areas. In addition, lack of proper selection and evaluation of excels supplier can affect long term survival of firms. The contribution of this paper is in threefold. First, a geometric mean induced bias matrix (GMIBM), is used to quickly identify the most inconsistent data in the judgment matrix. This helps to preserves most of the original information in matrix, but also faster than existing models. Secondly, it solves the supplier selection process problem in a Kathmandu Medical College (KMC) pharmaceutical firm using analytic hierarchy process (AHP) model. AHP is a decision making method and considered a reliable model for supplier evaluation problem in KMC. At last, Development of Supplier Selection Process (SSP) shows the whole steps followed for supplier selection.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".