Enhancing Quality Control in the Indonesian Automotive Parts Industry: A Defect Reduction Approach Through the Integration of FMEA and MSA
Bibliographic record
Abstract
This article presents the results of research related to measurement system improvement in the manufacturing process of part box 2ph (luggage box on a motorbike). The research used an integrated approach of Failure Mode and Effect Analysis (FMEA) and Measurement System Analysis (MSA) to identify and address critical measurement failures. Begin by identifying potential failure modes in the manufacturing process through FMEA, prioritizing high-risk failure modes, and then align these with critical measurements for subsequent evaluation. Implement MSA on the identified critical measurements, ensuring the measurement system's reliability and precision, and use the integrated results to guide corrective actions and improvements, creating a synchronized approach to enhance overall process quality. The limitation of integrating FMEA and MSA that focuses only on measurement involves limitations in addressing non-measurement aspects of potential failures, such as design issues, overall process variability, and qualitative aspects, so it may not provide a holistic picture related to the risk of failure in manufacturing processes. Key findings showed that errors in the measuring instruments, recorded on the measurement check sheets, were the focal point of urgent improvement. Root cause analysis implicated factors such as errors at the start of the project and lack of confirmation from the measurement department. In response, key initiatives involved full calibration of measuring instruments, control of the calibration schedule, appraiser training for consistency of skills and perceptions, and additional operators in quality checking and diameter sizing to reduce workload. Prior to improvement, MSA analysis revealed a significant level of uncertainty, with the Gage Repeatability and Reproducibility (GRR) value reaching 76.90%. Implementation of the improvements resulted in a dramatic reduction of GRR to 8.97%, signaling a positive transformation of the measurement system. The previously unacceptable system became reliable, with Number of Distinct Categories (ndc) values reaching 15, indicating consistency in providing information related to process changes. The results of this study provide valuable insights for further development in the manufacturing industry. By focusing on improvement strategies involving strengthening the calibration of measuring instruments, training appraisers, and adding operators, significant improvements in measurement quality and consistency can be achieved. The implications of these findings create a foundation for a proactive, datadriven approach to addressing and preventing measurement failures in manufacturing processes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".