A grey decision-making trial and evaluation laboratory model for digital warehouse management in supply chain networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it