Enhancing Overall Equipment Effectiveness in Indonesian Automotive SMEs: A TPM Approach
Bibliographic record
Abstract
In the dynamic landscape of the automotive industry, operational efficiency is a pivotal factor for sustained growth.This study delves into the intricacies of enhancing manufacturing operations, mainly focusing on the blowing machines at RMA Ltd, a key player in the Indonesian automotive SME sector.The primary objective is to optimize Overall Equipment Effectiveness (OEE) by implementing a Total Productive Maintenance (TPM) approach.The Indonesian automotive sector, vital to national economic growth, needs help maintaining optimal production efficiency.This study centres on the blowing machines at RMA Ltd's Plant 7, emphasizing the need to address breakdowns, particularly in the blowing machine, which has been identified as the primary source of production losses.A comprehensive research methodology is outlined, beginning with an extensive literature review on TPM and OEE.The study then focuses on the Indonesian automotive SME sector, with RMA Ltd as the primary research subject.Data collection involves an initial survey to assess the current state of blowing machines, encompassing OEE, Six Big Losses, and other relevant factors.Post-implementation of improvements, the study reveals substantial enhancements in OEE.Availability rates increased (93.19%),Performance Efficiency improved (84.84%), and Quality Rate remained consistently high (98.41%).The calculated OEE rose from 67.42% to an impressive 77.80%.Noteworthy reductions in Six Big Losses, particularly in breakdowns, setup losses, and reduced speed losses, validate the efficacy of TPM implementation.This research introduces a novel approach by integrating socialization strategies, detailed work instructions, and proactive maintenance practices.Through a comprehensive research methodology, including an initial survey and post-implementation analysis, this study demonstrates significant OEE improvements of 11%.The findings underscore the novelty of this research in emphasizing the importance of holistic TPM implementation strategies in enhancing manufacturing operations within the Indonesian automotive SME sector.Furthermore, this study provides actionable insights for SMEs in the Indonesian automotive sector, highlighting the relevance of TPM in achieving operational excellence and competitive advantage.Ultimately, this research contributes a valuable blueprint for SMEs seeking to navigate the complexities of the automotive industry, offering a roadmap to optimize manufacturing operations and thrive in a competitive market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 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".