Supplier Selection Analysis for Sand Materials Using AHP in the Indonesian Manufacturing Industry
Bibliographic record
Abstract
The analysis of supplier selection aims to identify suppliers that best match an organization's specific needs.In the past, an Indonesian manufacturing enterprise relied solely on price as a criterion for selecting suppliers, overlooking other important factors.This study employs a qualitative approach using the analytic hierarchy process (AHP) to select suppliers of sand raw materials.A geometric mean algorithm calculates eigenvalues and ranks the suppliers based on various criteria.The evaluation is based on the vendor performance indicator (VPI) criteria, which include quality, cost, lead time, flexibility, and responsiveness.The findings indicate the following global weightings for each supplier: Supplier U (0.224), Supplier V (0.327), Supplier W (0.197), Supplier X (0.123), Supplier Y (0.052), and Supplier Z (0.077).From this assessment, Supplier V is identified as the most suitable vendor for raw sand materials.Consequently, the enterprise should consider selecting Supplier V as its primary supplier.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".