A DEMATEL method for identifying supply chain complexity drivers of footwear industry
Bibliographic record
Abstract
This study aims to investigate the key drivers of supply chain complexity in the footwear industry business sector. After conducting extensive literature review and consultation with experts, complexity drivers in the context of the footwear business sector were identified. The content validity of the identified drivers was checked by 13 experts using content validity ratio (CVR), which were used as the basis to select 20 key Supply Chain complexity drivers. Using a multi-criteria decision-making DEMATEL approach the cause and effect drivers were investigated. The findings of the study investigated 12 cause and 8 effect drivers in the footwear sector. Among the cause drivers, four linkage drivers that have strong driving power and dependency were identified. This study is the first in the developing county footwear business sector using DEMATEL approach to identify the key supply chain complexity drivers and their interrelationship. The study identified complexity drivers in the context of the footwear business sector that were not explored before in the existing studies. The outcome of this study will help SC complexity decision-makers in that case sector to control and manage the cause and effect drivers and thus improve the efficiency and effectiveness of their SC 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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.029 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".