Guidelines for logistics system and supply chain management for environment in industrial busines
Bibliographic record
Abstract
Industry plays a vital role in developing Thailand and supporting and driving businesses. The increase in industry benefits the national income. However, it also affects the environment. Industrial companies must adapt to reduce problems by developing organizational infrastructure, such as logistics activities or operations, which are the major systems and causes of environmental issues. Therefore, this study aims to study guidelines for logistic system and supply chain management for the environment in industrial business and develop a structural equation model (SEM). The study used a mixed-method research design; the qualitative method, in-depth interviews with nine experts and a focus group with 11 experts were used. Concerning the quantitative method, a questionnaire survey was used with 500 green industries. Descriptive statistics, inference statistics, and multivariate analysis were also used to analyze the data. The study found four essential aspects: (1) Customer orientation, (2) Green network, (3) Logistic process, and (4) Organization management. Finally, the hypothesis testing results found that in medium- and large-sized industries, the importance of the variables of the logistic system and supply chain management for the environment had no statistically significant difference at 0.05. The results from the analysis of the structural equation model found that the study passed the evaluation and was relevant to empirical data with a chi-square probability level of 0.066, a relative chi-square of 1.133, a goodness of fit index of 0.954, and root mean square error of approximation equal to 0.016.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".