Implementation of Tpm and Rcm to Increase Service Level in a Maintenance Sector Company
Bibliographic record
Abstract
Industrial maintenance management plays a critical role in operational continuity and efficiency in key sectors such as construction and mining.This study analyzes the impact of the joint implementation of Total Productive Maintenance (TPM) and Reliability-Centered Maintenance (RCM) in an industrial services company.A structured methodology was adopted, including the classification of critical equipment through ABC analysis and risk matrices, the use of Failure Modes and Effects Analysis (FMEA), and the planning of preventive tasks.The results show significant improvements in key indicators: Mean Time Between Failures (MTBF) increased by an average of 28%, Overall Equipment Effectiveness (OEE) improved from 40% to 65%, and the service level increased from 38% to 63%.Additionally, operational costs were reduced by 20%, optimizing equipment availability and strengthening operational sustainability.This study validates the effectiveness of TPM and RCM as complementary tools for optimizing maintenance processes, reducing failures, and enhancing competitiveness.The findings highlight the importance of integrating preventive and predictive strategies to face the challenges of industrial maintenance in dynamic environments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".