Supply chain model in retail business: A systematic mapping study
Bibliographic record
Abstract
Scientific breakthroughs in the field of the supply chain must keep pace with technological advancements and environmental changes. Modifying processes, systems, structures, roles, and skills is the goal. The relevance of staff training and the necessity for technology is currently a hot topic in managing changes in the concept of the supply chain in retail business. The purpose of this study is to classify and identify journal and conference articles and undertake analysis of the present paper to develop a deep comprehension of the supply chain in retail business. A systematic mapping study (SMS) examines scientific publications produced over time, focus, locus, and the most widely investigated type of research. It is also the most widely used research method. The SMS technique follows well-established empirical guidelines, and the mapping data is based on Scopus. According to SMS research findings on organizational change, 68 studies match the inclusion criteria. By paper type, method, focus, locus, and year of research publication, we divided 68 publications into topic groups. The current studies are then categorized and quantified based on multiple parameters, topic descriptions, and current research trends.
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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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, 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".