Critical Factors of Supply Chain Based on Structural Equation Modelling for Industry 4.0
Bibliographic record
Abstract
Industrial Revolution 4.0 encourages the digitalization of manufacturing, especially in terms of improving the supply chain processes.Currently, the development of information technology follows the principles of Industrial Revolution 4.0 in the form of interconnections/connections to communicate with machines, transparency, and technical assistance.The presence of a decision support system, COBIT 5, and ISO 9126 are expected to maximize business processes and technology.Thus, relationship analysis needs to be carried out on decision support systems, COBIT 5, and ISO 9126, to determine the relationship of each variable with the supply chain process.Relationship testing can be performed using statistical tool such as Structural Equation Modelling (SEM).The study focuses in analyzing influence of DSS COBIT 5 variables, ISO 9126 functionality, ISO 9126 reliability, and decision support systems on supply chain management.The primary data are based on questionnaires to employees of the furniture manufacturing industry.The results of this study are DSS COBIT 5, functionality ISO 9126, reliability ISO 9126, and decision support systems significantly affecting the supply chain management process.Thus, the industry needs to focus on implementing these factors so that business processes comply with the standards and principles of the industrial revolution 4.0.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| 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 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".