Decrease The Level Of Complaints In A Company Of Air Compressors Through Lean Manufacturing Based On Dmaic Methodology
Bibliographic record
Abstract
In the field of mining and construction, the machinery and equipment subsector face challenges in customer service quality due to repeated reprocessing caused by a lack of preventive maintenance.This research proposed a solution based on Lean Manufacturing by applying Total Productive Maintenance (TPM), following the steps of a DMAIC methodology, and conducting simulations using Arena software.The initiative focused on streamlining the rental process, reducing response times, and improving the reliability of compressors.Positive outcomes were anticipated, including increased equipment availability, reduced response times, a decrease in the failure rate, and improved customer satisfaction.Furthermore, simulation results revealed a significant transformation: the average customer service time decreased by 35.49% (from 1714.70 to 1106.03 minutes), and the average preparation time of a compressor before rental decreased by 32.81% (from 2750.53 minutes to 1847.86 minutes).In the economic analysis, the feasibility of the proposal was demonstrated using @risk software, with a Net Present Value (NPV) of $139,024 and an Internal Rate of Return (IRR) of 34.83%, surpassing the Cost of Capital (COK).This research aimed not only to enhance key performance indicators but also to position the company as a leader in efficiency and customer satisfaction in the competitive machinery and equipment sector.This holistic approach not only strengthened operational efficiency and cost reduction but also contributed to the sustainable development of the machinery and equipment sector.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".