Towards better understanding of the complex industrial systems: Case of production systems
Bibliographic record
Abstract
Abstract Growth of the world population and the globalization of trade are the origins of the fourth industrial revolution, called “Industry 4.0”. What engineers call systems are becoming more and more complex as businesses strive to stay competitive and meet ever-changing demand. While automation and information digitization and transmission technologies are increasingly becoming major assets in modern industries, the changes they bring are having an impact on the management of occupational health and safety. The aim of this article is to provide an overview of the progress achieved in the understanding of complex systems and to test some of the published theory by comparing it to a case study. The major scientific databases were searched to retrieve the literature on complexity, and a large company in the steel products business was queried to determine how its complexity as perceived by its managerial staff compares to the theory of complex systems. Our main conclusion is that, based on the data gathered in the case study, the perception that the managerial staff has of the company corresponds closely to the current definition of complex systems as proposed by researchers. However, it remains to be determined whether this correspondence holds over the range of business sizes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".