Implementation of artificial intelligence project, quality internal and external supply chain integration, responsiveness for operational performance in manufacturing
Bibliographic record
Abstract
This paper analyzes the effect of Artificial Intelligence (AI) implementation projects on the operational performance of manufacturing companies in Indonesia, considering the mediating role of quality internal integration, quality external integration, and supply chain responsiveness. This study uses a quantitative research approach, utilizing a survey method, with 109 manufacturing companies in Java and Bali that have adopted AI technology in their production processes. Data processing was carried out using the Partial Least Squares (PLS) approach. The results indicate that the AI implementation project has a significant impact on both internal quality integration and external quality integration but does not directly affect supply chain responsiveness or operational performance. Quality internal integration is proven to be a key variable that significantly affects quality external integration, supply chain responsiveness, and operational performance. Meanwhile, quality external integration has a significant effect on supply chain responsiveness but not on operational performance directly. Additionally, supply chain responsiveness has been proven to positively contribute to improving operational performance. The conceptual model developed in this study successfully demonstrates a multi-layered influence path from AI implementation project to operational performance through quality integration and supply chain responsiveness. This study highlights the importance of synergy between AI technology and internal and externally integrated quality management systems in achieving operational excellence in the manufacturing sector. These findings expand the theoretical understanding of the strategic role of AI in the context of supply chain and operational quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".