GRMI4.0: a guide for representing and modeling Industry 4.0 business processes
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose a new business process representation adapted to the needs of Industry 4.0 to facilitate the implementation of technological solutions in the construction sector. Design/methodology/approach This work is based on the Design Research Methodology approach and includes four phases: (1) a literature review on the main business process modeling standards and their ability to take into account the specificities of Industry 4.0; (2) the identification of the attributes to be considered to model Industry 4.0 processes; (3) the development of a mapping model for Industry 4.0; and (4) the validation of the model using a case study from the construction sector. Findings To the authors’ knowledge, current business process modeling standards do not effectively represent business processes in the context of Industry 4.0. Originality/value The proposed model can represent not only the 4.0 solutions that can be implemented in the construction sector, particularly from a technology and data perspective but also measures, with the help of performance indicators, the impacts of successive process changes in terms of skills, cost and time for a true 4.0 transformation.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.017 |
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".