Harnessing Supply Chain Efficiency Through Information Linkages
Bibliographic record
Abstract
The Indian retail industry majorly constitutes of small retailers, comprising of approximately 12 million small shopkeepers and increased competition has made companies understand the significance of this unorganized small retail sector. Most companies feel that coordinating their downstream supply chains is critical for long term growth and sustainability. The paper examines the supply chain coordination amongst retailers, distributors, logistics providers, customers, and major Fast Moving Consumer Goods (FMCG) multi-national companies in India. The findings confirm that supply chain integration, information sharing, and supply chain design are being given proper attention by FMCG companies. They appreciate the strategic value of information sharing for establishing collaborations with the small retailers for effective performance of supply chains. Even in the fragmented, ill-defined, unorganized, and disjointed small retail sector in India, information sharing between supply chain partners is given precedence. Lack of technological infrastructure does not deter MNCs from establishing information linkages with small retailers and harnessing it for supply chain efficiency.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.019 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".