Inventory Models in a Sustainable Supply Chain: A Bibliometric Analysis
Bibliographic record
Abstract
This paper presents a bibliometric analysis of inventory models in a sustainable supply chain. The methodology contains reviewing previous research with a performance evaluation, network analysis, and science mapping to identify the applications, trends, and future research topics. Scientific mapping examines the periods and volumes of publications, authors, journals, countries, regions, organizations, subject areas, and citation analyses. The dataset was obtained with the Scopus database and analyzed using MS Excel and VOSviewer. The search equation identified 335 research papers, which resulted in 131 significant manuscripts on the subject after being screened and filtered. The most notable countries in developing research were Iran, India, China, the United States, Canada, Taiwan, France, the United Arab Emirates, Turkey, and Denmark. Saha, S., Ajay, S.Y., and Baboli, A. were the most cited authors. The journals that publish the most research were Sustainability, the Journal of Cleaner Production, and the International Journal of Production Economics. Some research focuses on reducing carbon emissions and polluting agents applied in different industries in China, Brazil, India, and others. The main findings were the number of industry sectors researching this topic, increasing the number of publications, and promoting the proper use of resources within a sustainable supply chain. There are many investigations of theoretical models that have applications in real-life cases. There is also evidence of the high importance of promoting sustainable development. The emissions regulations in a green supply chain applied to agricultural products have allowed for more actions to achieve responsible production and consumption, as seen in applied research in the pulp and paper industry.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.161 | 0.362 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".