Optimization of Thai-Lao cross border transportation via R9 route for Thai shippers
Bibliographic record
Abstract
The objectives of this research are to find the proper measures to improve efficiency of Thai-Lao cross border transportation (R9 route) for Thai shippers and study their performance after implementing the developed measures. This research implements both quantitative and qualitative research, where the population of this research consists of shipper’s groups which transport products via the Thai Lao border located along R9 route. The research tools are questionnaire and structured interview form passed variability and reliability tests. Then the questionnaires are distributed to 3 large size firms (sample size of 316 people) and an in-depth interview with 18 people. This research analyzes gathered data using descriptive analysis, construct validity, structural equation modeling (SEM), path analysis, and content analysis for qualitative data. The analysis results reveal important findings according to each research objective as follows. For the first objective, to improve cross-border transportation, the case study firms should concentrate on three measures as: 1) transportation safety in terms of sound safety control and monitor, increasing rest areas and truck stops including those on pavement or roadside, safety monitoring during transportation, and weather conditions checking; 2) documentation procedure in terms of reducing the procedure steps, IT implementation, and procedure improvement both inbound and outbound; and 3) vehicles management in terms of appropriate resources allocation, facilities availability, and appropriate resources selection. For the second objective, the study of the case study firms’ performance after implementing the developed measures, it reveals benefits in several aspects of 1) cheaper (they can deliver items with lower costs, lower expenses, or lower price than before implementation as well as other competitors); 2) faster (better customers responsiveness, faster delivery and operating than other competitors, and lead time reduction), and 3) better (in operations with less bottlenecks, mistakes, and disruptions manifested in delivery; higher competitiveness and better service quality). To summarize, the implementation of the developed model can help the shippers to increase their capability as well as their competitiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".