Operational Model to Improve On-Time Delivery: A Peruvian Case Study in Metalworking SMEs Integrating Lean Tools and TPM
Bibliographic record
Abstract
The Peruvian metalworking sector has historically faced delays in deliveries due to poor process standardization and inadequate equipment maintenance.While previous studies have addressed Lean tools separately, limited empirical evidence exists regarding their integrated application within a single model for SMEs in this sector.This research addressed key challenges-machine stoppages, production delays, and disorganized workspaces-through an operational model combining Standard Operating Procedure (SOP), 5S, Total Productive Maintenance (TPM), and Single Minute Exchange of Die (SMED), validated through simulation and pilot implementation.SOP was defined as a standardized set of instructions ensuring the correct execution of operational tasks.The 5S tool increased audit scores from 34.4% to 84.3%, TPM improved OEE from 24% to 67%, and SMED and SOP together reduced changeover time by 64.8 minutes.The findings demonstrated measurable gains in productivity and process organization.At the industrial level, the model provides practical implications for SMEs aiming to enhance operational performance in emerging markets with resource and time constraints.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".