The moderating role of lean operations between supply chain integration and operational performance in Saudi manufacturing organizations
Bibliographic record
Abstract
The fundamental reason for this study was to explore the impact of Supply Chain Integration on Operational Performance through the directing part of Lean Operations. The study essential information gathered from the example that contains 288 supervisors working in Saudi Industrial Organizations lied in the western locale, utilizing an all-around planned survey. This study coordinated to study how the supply chain integration, lean operations, and operational performance can impact each other in manufacturing associations. SEM model created and deliberately surveyed and tried. The key discoveries demonstrate that rehearsing supply chain integration cycles could bring about expanding the open door for manufacturing associations to accomplish operational performance through applying lean practices. Therefore, the connection between supply chain integration and operational performance, just as the connection between lean operations and operational performance, was positive, given these connections, it very well may be presumed that the lean operations (as a directing variable) can have a positive impact the connection between supply chain integration and operational performance. Particularly, the connection between supply chain integration and quality performance measures.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".