The influence of the human-machine interface on operational performance through supply chain agility
Bibliographic record
Abstract
Manufacturing companies continue to carry out activities by maximizing the role of the human-machine interface. Its function is to provide work effectiveness and efficiency, compliance with social distancing at the working place, and maintain the production optimum utilization level of machines. The human-machine interface, semi-automatic or fully automatic, gives a significant role to operators and supervisors in company operations to monitor production results in real time. This study examines the influence of human-machine interface adoption on operational performance through supply chain agility. Questionnaires were distributed to 77 companies in East Java, and 56 questionnaires were considered valid for analysis. The data processing results show that the human-machine interface impacts the supply chain agility with a path coefficient of 0.665. The human-machine interface affects operational performance with a path coefficient of 0.334. Similarly, supply chain agility impacts operational performance with a path coefficient of 0.306. The human-machine interface affects operational performance through an agile supply chain with a path coefficient 0.203. This result implies that firm management needs to consider adopting HMI technology to improve the firm's performance and competitive advantage. This work could also contribute to the current research in operations and supply chain management.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".