The human-machine interface enables collaborative decision-making and supply chain flexibility to boost operational performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using technology, such as human-machine interfaces, can enhance operational performance processes and increase the flexibility of the supply chain. Human-machine interfaces can produce operational control systems quickly and accurately. The research aims to explore the impact of human-machine interface on operational performance through collaborative decision making and supply chain agility. The sample criteria are the manufacturing companies with over 20 employees in Indonesia. The questionnaires were distributed offline (76 respondents) and online through Google Forms (427 respondents), so 503 questionnaires were valid—data processing using SmartPLS software version 4.0. The study results showed that the human-machine interface technology positively affects collaborative decision-making, supply chain flexibility, and operational performance with coefficients of 0,559, 0,490, and 0,340, respectively. Collaborative decision-making involving customer partners in planning decisions and communicating decisions with external partners influences supply chain flexibility by a coefficient of 0.375 and operational performance by 0.149. Moreover, supply chain flexibility with flexible planning and production processes and flexible labor placement influences operational performance by a coefficient of 0.381. The practical contribution of research enlightens company managers to build integrated systems and automation. It encourages top management and owners to think about investing in machines with high automation in the economy. Besides, these findings enrich the theoretical background in supply chain management and the resource-based view.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 it