A grey decision-making trial and evaluation laboratory model for digital warehouse management in supply chain networks
Bibliographic record
Abstract
Integrating digitalization and warehouse management systems (WMS) is a crucial aspect of enhancing supply chain performance for strategic competitiveness. Multiple technologies promote digital development and supply chain management (SCM) transformation. They include artificial intelligence and robotics, cloud computing, 3D printing, advanced analytics, blockchain, augmented reality, radio frequency identification (RFID), the internet of things (IoT), and cloud technology. This research aims to identify and evaluate the factors of digitalization, WMS, and supply chain performance by combining a comprehensive literature review analysis with the grey decision-making trial and evaluation laboratory (DEMATEL) method. An extensive literature review is conducted to identify the primary determinants of supply chain performance. Subsequently, the expert panel from the textile industry is consulted to obtain expert opinions on these factors’ relative importance. The findings of this study demonstrated that by considering the interdependencies on supply chain performance and the uncertainties related to expert judgments, the suggested comprehensive model is highly capable of addressing the digitalization WMS problem
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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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".