Investigating the mediating role of information sharing strategy on agile supply chain
Bibliographic record
Abstract
Supply chains need to redesign their existing strategies and must develop new strategies to effectively face the challenges posed by certain disruptions, both man-made and natural. This requires the supply chains to be highly flexible, visible, reliable and cost-effective leading to the achievement of Agile Supply Chain (ASC). In today's competitive market, achieving agility in supply chain needs dynamic leadership, strategic vision, mutual cooperation from all members and effective utilization of information technology through customer focus. In spite of their initiatives to achieve ASC for improving their organizational performance, barring a few large companies, the medium and small size manufacturing companies have not yet been able to adopt and design supply chains which lead to ASC. This may be due to various challenges in the process of achieving agility. Also, the published literature in this area is very scanty. Therefore, to fill this gap, the purpose of this study is to determine the mediating role of Information Sharing Strategy (ISS) on Agile Supply Chain (ASC) practices for achieving Supply Chain Performance (SCP) in medium size manufacturing companies in UAE. An empirical survey of supply chain managers in UAE is conducted for this purpose. It is found that information sharing plays a major mediating role in ASC to achieve superior supply chain performance.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".