Assessment of Total Productive Maintenance (TPM) Implementation in Industrial Environment
Bibliographic record
Abstract
Maintenance strategies play a crucial role in achieving organizations’ goals and abilities to reach their profit targets and survive in the competitive global marketplace and changing economies. Total productive maintenance (TPM) is one of the lean manufacturing approaches that help to improve equipment performance by increasing production rate and equipment availability and enhancing the overall productivity of manufacturing. Implementing the eight pillars of TPM involves many challenges and difficulties, and it is difficult for small to medium enterprises (SMEs) in Canada to successfully implement TPM. The main objective of this study is to determine whether the Short-Term TPM (STTPM), based on Autonomous Maintenance and Planned Maintenance pillars and 5S technique can minimize losses in a production process and have a positive impact on manufacturing performance (MP). Furthermore, this study is to facilitate successful TPM implementation using the Short-Term TPM (STTPM) approach. Therefore, this research is to develop an implementation framework for the introduction of the TPM improvement approach into SMEs. The framework’s fundamentals are STTPM team commitment and involvement, training, member involvement, and culture change. Overall line effectiveness (OLE) should be calculated based on the overall equipment effectiveness (OEE) metrics. The OLE was analyzed for different production line configurations and the multivariate consideration of quality rate through principal component analysis (PCA). Daily data from production lines was collected from a real manufacturing environment. A paired t-test was conducted to compare a production rate (P_r R), equipment availability (EV), and cycle time (CT) before and after STTPM implementation for each production line. The study was performed using Minitab 19 software to identify the effect of STTPM on MP. The result shows that P_r R, EV, and CT had significant differences before and after the implementation of STTPM in the production line. Similarly, the OEE was significantly different before and after the implementation of STTPM in the production line. This study will also make a meaningful contribution to the related scholarly literature in the form of a novel model of TPM implementation, mainly among Canada’s SMEs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".