A review of lean, agile, resilient, and green (LARG) supply chain management in engineering, business and management areas
Bibliographic record
Abstract
Supply chain management (SCM) that is Lean, Agile, Resilient, and Green (LARG) are required for competitiveness in today's complex, high-demand market. SCM must consider LARG paradigms concurrently, a rarely investigated topic. This study provides a comprehensive review of publications that combine all four LARG principles in engineering, business, and management domains. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, two Scopus and Google Scholar databases were exhaustively examined. Thirty-two manuscripts were selected for a comprehensive review. The year of publication, document type, countries, authors, journals, keywords, and topics was analyzed from 2000 to 2023. Also, each paper's methodology, central topic, findings, limitations, and future recommendations were outlined. Consequently, the current systematic literature review (SLR) revealed that the proposed topic is in its infancy, with promising prospects. By emphasizing the findings of this study, managers and businesses can increase consumer satisfaction and reduce costs.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".