The influence of supplier competency on business performance through supplier integration, vendor-managed inventory, and supply chain collaboration in Fuel Station: An evidence from Timor Leste
Bibliographic record
Abstract
Fuel availability is essential in supporting the sustainable economic growth of a country. The manufacturing industry, transportation activities, and shipping products between regions are the primary sectors that require a sustainable fuel supply. The fuel station contributes to distributing and delivering fuel in serving the demand for the fuel. This study investigates supplier competency's role in supporting fuel station business performance through supplier integration, vendor-managed inventory, and supply chain collaboration. The research surveyed 71 fuel stations in Timor Leste using a questionnaire designed with a five-point Likert scale. The questionnaires are distributed to supervisors or higher positions at fuel stations by distributing questionnaires by direct delivery and also through Google Forms for areas far away in downtown Timor Leste. Data from respondents were analyzed using smartPLS software version 4.0. The results found that supplier competency positively impacts supplier integration, vendor-managed inventory, and supply chain collaboration. Moreover, supplier integration positively impacts vendor-managed inventory, supply chain collaboration, and business performance. Vendor-managed inventory fuel station sites can have an impact on improving supply chain collaboration and business performance. In addition, supply chain collaboration between vendors and fuel stations in Timor Leste enables continuous business performance improvement. This research paves the way for supervisors, managers, and fuel station top management to collaborate with suppliers in maintaining inventory levels and forecasting the supply and demand for fuel procurement requirements. Finally, this research contributes to the theory of inventory optimization and supply chain performance.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".