Supply chain mapping for improving “visilience”: A hybrid multi‐criteria decision making based methodology
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
Abstract Supply chain mapping is gaining heightened attention due to its vital role in improving supply chain visibility and resilience. Despite its crucial role in uplifting supply chain resilience, the critical elements of supply chain mapping are yet to be determined. The study adopts a twofold approach to identify and prioritize the dimensions and sub‐dimensions of supply chain (SC) mapping. At the first stage, through an extensive review of literature, 43 sub‐dimensions of SC mapping were identified. In the second stage, Gray ‐ DEMATEL‐based Analytic Network Process (GDANP) was employed by taking the input from 25 experts selected from Oil and Gas industry of an emerging market. The findings reveal three major dimensions of SC mapping followed by 15 sub‐dimensions. Among the dimensions, upstream mapping contains the highest priority weights, followed by midstream and downstream mapping. The findings suggest a step‐wise strategy to adopt SC mapping where upstream mapping should be given the first priority. The major contribution of this study is to develop a framework for measuring the extent of SC mapping of a firm using GDANP.
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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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