Supply Chain Resilience Strategies for Surviving of Industry in Thailand
Bibliographic record
Abstract
Thailand’s advantageous location, strong infrastructure, and uptake of digital technologies all contribute to its standing as a Southeast Asian centre for logistics and supply chains. The impact of internal skills on performance during interruptions and how Thai-listed firms handle the risk of supply chain disruptions are poorly understood. By combining the resource-based view, contingency theory, and dynamic capabilities theory, this research fills this knowledge gap by investigating how industry reacts to particular threats, including those involving infrastructure, planning, workforce, and security, and how their mitigation strategies—such as internal risk management and collaboration—affect firm performance. The research investigates 14 hypotheses that relate disruption threats and mitigation techniques to firm performance using data from 167 listed firms on the Stock Exchange of Thailand (SET), survey data, and statistical analysis. The findings show that while well-managed employees and security concerns may have beneficial effects, infrastructure hazards significantly impair corporate performance. Performance is improved by internal resources and cooperative partnerships with supply chain partners, although collaboration with government organisations might be less successful. The results also founded on three recognised ideas, the research contributes to theory and in action by giving governments and businesses advice on how to prioritise resilience investments. To increase Thailand's total supply chain resilience in accordance with national plans like Thailand 4.0, recommendations include enhancing infrastructure, customising regional responses, and fostering digital capabilities. Future studies on sector-specific hazards, resilience indicators, and cross-country comparisons in an ASEAN context are suggested by the findings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".