Information Technology Revolutionising Supplier Development Performance in Indian Automotive Sector
Bibliographic record
Abstract
1024x768 Normal 0 false false false EN-IN X-NONE X-NONE 1024x768 Normal 0 false false false EN-IN X-NONE X-NONE The growth of automobile production in the ASEAN region has in tandem spurred the growth of an automotive supplier industry. In today’s “hypercompetitive” business environment, most of global automobile OEMs have set up their manufacturing and sourcing operations in India to realize low cost advantage and skilled man power of India. Since the birth of 21st century, Indian automobile industry is not only going through a cut-throat completion and increasing sales volume but also has a challenge of supplying components at reduced cost and improved quality at par with global standards. Work-Culture of traditional businesses changed drastically by strong efforts of parent company on strategically, technological lean-manufacturing, training and development and usage of Information and communication technology for supplier development. Most of the auto OEMs being brand ambassadors having assembly lines, Green Supply Chain Management(GSCM) supported by Information and communication technology(ICT) is playing a vital role in meeting the procurement of auto components in most economical way . This study revolves around the change in the thought process by parent companies regarding supplier development from the last decade of twentieth century till first decade of twenty-first century where traditional manufacturing systems in Indian Automobile Industry were transforming to most modern system supported by Information and Communication Technology.ICT has still played a considerable role in QCD (Quality, Cost and Timely Delivery)and new concept of cloud computing is going to revolutionise the Indian Automobile Industry to be one of the most competitive supplier at global front in the coming future.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".