Internet of things (IoT) and big data analytics (BDA) for digital manufacturing (DM)
Bibliographic record
Abstract
This paper aims to investigate the impact of enterprise architecture (EA) on system capabilities in dealing with changes and uncertainties in globalised business environments. Enterprise information systems are viewed as information systems to acquire, process, and utilise data in decision-making supports at all levels and domains of businesses, and Internet of things (IoT), big data analytics (BDA), and digital manufacturing (DM) are introduced as representative enabling technologies for data collection, processing, and utilisation in manufacturing applications. The historical development of manufacturing technologies is examined to understand the evolution of system paradigms. The Shannon entropy is adopted to measure the complexity of systems and illustrate the roles of EAs in managing system complexity and achieving system stability in the long term. It is our argument that existing EAs sacrifice system flexibility, resilience, and adaptability for the reduction of system complexity; note that higher adaptability is critical to make a manufacturing system successfully. New EA is proposed to maximise system capabilities for higher flexibility, resilience, and adaptability. The potentials of the proposed EA to modern manufacturing are explored to identify critical research topics with illustrative examples from an application perspective.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".