A Hierarchical Structure Modeling and Relationships Exploration of Supply Chain 5.0 Capabilities
Bibliographic record
Abstract
Supply chain (SC) 5.0 is considered the next SC transformation to satisfy the diverse customer better, in incoherence with the next industrial revolution. SC 5.0 will enhance the technological adaptation of SC 4.0. It will bring an additional paradigm shift towards mass personalization and human-centricity, sustainability, and agile and flexible systems with transparency to promote a connected super-smart society. In light of these developments, this study aims to develop SC 5.0 capabilities and understand the interrelationship of these capabilities along with a hierarchical structure. The Delphi method has validated the identified capabilities. In addition, we utilized integrated interpretive structural modeling and decision-making trial and evaluation laboratory (ISM-DEMATEL) along with MICMAC analysis. This approach was employed to understand the interrelationships among these capabilities. This study is a stepping stone for future research on SC 5.0 capability analysis with a focus on the growing concept of Industry 5.0. Moreover, this study helps organizations develop their strengths and management system according to SC 5.0 capabilities.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".