Supply chain mapping for improving “visilience”: A hybrid multi‐criteria decision making based methodology
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.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".