The influence of supply chain quality integration on operational performance through innovation quality integration
Bibliographic record
Abstract
Sanitary and product manufacturing companies' care in Indonesia is increasingly challenging to carry out production due to limitations caused by the lockdown during the pandemic. The conditions demanded very high and required fast distribution mobility. The company maintains product quality by established standards according to specifications, but the production process time is limited. To maintain product quality, companies must retain supply chain quality integration and innovation quality integration to support Operational Performance. The research uses manufacturing companies focusing on plastic companies with bottle and tube production related to supply packaging health protocol products. Analysis to answer the research hypothesis uses the software SmartPLS. The study results found that internal supply chain quality integration by integrated manufacturing processes positively and significantly affects supplier and customer quality integration. Supply chain integration, which consists of supplier quality integration, internal quality integration, and customer quality integration, impacts increasing product innovation and the number of new products. Supply chain quality integration and innovation quality integration directly influence operation performance. This research enlightens company managers on improving internal capabilities and establishing synergy with suppliers and customers to create innovation, aiming to increase operational performance to compete with the global market and face market fluctuations. Research makes a theoretical contribution to quality development and supply chain integration.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".