How Does Knowledge Diffusion Impact Maintenance Performance? Lessons from a Survey in a Brazilian Petrochemical Company
Bibliographic record
Abstract
Continuous process industries depend on complex systems that operate in uninterrupted cycles using diversified resources, such as dedicated equipment; process and auxiliary materials; production and maintenance personnel; as well as several types of services. Maintenance services are an important element of these resources, focusing on asset preservation and improvement. To perform satisfactory work, maintenance teams increasingly appear to depend on the streams of knowledge that flow through them. The main objective of this paper is to investigate the relationship between the diffusion of organisational knowledge through the social network of a maintenance team and the performance indicators of the team. We conducted a survey in a Brazilian petrochemical company by collecting data from four maintenance teams. To examine that relationship we used, respectively, two proxies: the Overall Performance Index (OPI) and the Knowledge Diffusion Index (KDI). A positive correlation between OPI and KDI was noted, indicating that diffusion of organisational knowledge should be, to a certain extent, stimulated as a way of contributing to the improvement of the maintenance performance of the team.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".