Supply Chains in India: Can We Organise Them Better?
Bibliographic record
Abstract
Worldwide, interest in supply chain management has increased steadily since the 1980s when companies began to see the benefits of collaborative relationships. The supply chain concept is still nascent in India. However the need for the same, at this stage, is more than ever before because of the challenges unleashed on the competitiveness of the Indian industry by deregulation and globalisation. An essential first step in the process is to assess the current supply chain capability. The article is based on a recently concluded extensive research carried out jointly by Management Development Institute, Gurgaon and KPMG India, the first of its kind in the country, to gauge the current state of supply chain management in the Indian industry. The research concludes that a beginning has been made and large number of Indian organisations today are realising the importance of developing and implementing a comprehensive supply chain strategy - and then linking that strategy to deliver bottom-line results.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".