The impact of supply chain integration on operational performance with supply chain capability
Bibliographic record
Abstract
Force majeure in Indonesia, especially during pandemics, causes a drastic fluctuation in the market and makes many medicine products related to the pandemic become scarce and stocked out. Major pharmaceutical companies manufactured in Indonesia need solutions for the significant change in demand level and find solutions to balance the supply and demand level. According to the existing literature, by doing internal integration, supplier integration, and customer integration, and supported with supply chain capability, companies can find solutions regarding market fluctuation and increase their competitive performance. This research uses 102 listed Indonesians chosen by the purposive sampling method. Research analysis was conducted using structural equation modeling and SmartPLS 3 software. This research finds that, in general, supply chain capability influences competitive performance. Meanwhile, internal integration by sharing activity information in departments and coordinating integrated planning can positively and significantly affect supplier integration and customer integration. Sharing inventory and information with suppliers and coordinating with suppliers about materials availability significantly influence supply chain capability. Internal integration also has a significant influence on supply chain capability. Customer integration with information sharing with customers and the company involving the customers when demands influence supply chain capability. Supply chain integration (internal, supplier, and customer) does not directly impact operational performance, so supply chain capability is a perfect intervening variable. Supply chain capability can help internal and external integration better affect competitive performance. This research also makes practical contributions to give managers input about how internal integration, supplier and customer integration, and supply chain capability can affect companies' competitive 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".