Current Overview of CMMS Operationality: Brazilian Scenario
Bibliographic record
Abstract
Currently, the impacts of Industry 4.0 are observed in the construction industry, commonly referred to as Construction 4.0, emerging linked to technological innovations. Construction 4.0 presents a standardized model for smart cities and buildings, with the existence of some important points being essential, such as the capacity for adaptation, improvement and efficiency of resources and connections for everyone involved. As a result, new technologies and applications have been emerging that directly impact building maintenance activities, enabling increased efficiency and productivity in this sector, which reduces the risk of errors, failures and defects by maintenance managers. One of these applications is the use of computerized maintenance management systems (CMMS), which is a software solution designed to simplify building maintenance processes, in addition to improving the management of organizations' assets. In this sense, the present work aims to evaluate the CMMS available in the Brazilian scenario, showing the current panorama in which this technology has been presented to the market. To this end, we carried out a survey of the CMMS used by Brazilian maintenance companies using the snowball method to list the CMMS to be analyzed. The research carried out in 16 maintenance companies identified 9 CMMS, which were evaluated using the method proposed by Roscoff; Costella; Pilz (2020), which evaluates CMMS functionalities and activities. As a result, the sample analyzed presents heterogeneity in the results linked to CMMS functionalities and activities. In short, the registration items, a basic function, were met by all CMMS analyzed. However, advanced functions that are linked to the principles of Industry 4.0, such as interoperability, virtualization, real-time and service orientation, present disparities in results. Because among the nine CMMS evaluated, only three reached the levels proposed in the methodology.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".