machine learning-driven Six Sigma framework for enhancing the quality improvement and productivity in the Aircraft Manufacturing
The aviation industry, a pillar of global transportation, is under constant pressure to increase productivity and efficiency while maintaining strict quality requirements. Airctraft defects in production can result in significant financial losses, lead to costly rework, delays, and even safety risk...
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| Format: | Article |
| Language: | Indonesian |
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Universitas Pembangunan Nasional "Veteran" Yogyakarta
2025-06-01
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| Series: | OPSI |
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| Online Access: | https://jurnal.upnyk.ac.id/index.php/opsi/article/view/13960 |
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| author | Dwi Adi Purnama Alfiqra Alfiqra Winda Nur Cahyo |
| author_facet | Dwi Adi Purnama Alfiqra Alfiqra Winda Nur Cahyo |
| author_sort | Dwi Adi Purnama |
| collection | DOAJ |
| description | The aviation industry, a pillar of global transportation, is under constant pressure to increase productivity and efficiency while maintaining strict quality requirements. Airctraft defects in production can result in significant financial losses, lead to costly rework, delays, and even safety risks. This study proposes a framework to improve productivity and efficiency in aircraft manufacturing and analyze quality control using machine learning, Six Sigma, and the QCDSME (Quality-Cost-Delivery-Safety-Morale) method. The DMAIC (Define-Measure-Analyze-Improve-Control) stage is a reference in the implementation steps of the Six Sigma method of the Airbus A320. The sigma value in this study was obtained on average for 40 periods of 4.61 sigma and a DPMO of 1225.69. At the analyze stage, a fishbone diagram is used to find the root cause of the problem. Furthermore, a machine learning analysis was performed using the text mining method to identify the most common product components that frequently have defects in Airbus A320 and identify the main factors causing defects, by the human factor. The enhance stage suggests a rise in overcoming challenges with the QCDSME method. Overall, it was discovered that the number of defects fell while the sigma improved and this method can enhance industry performance. |
| format | Article |
| id | doaj-art-ea95df414aec42c7be1d240cb923de3e |
| institution | Kabale University |
| issn | 1693-2102 2686-2352 |
| language | Indonesian |
| publishDate | 2025-06-01 |
| publisher | Universitas Pembangunan Nasional "Veteran" Yogyakarta |
| record_format | Article |
| series | OPSI |
| spelling | doaj-art-ea95df414aec42c7be1d240cb923de3e2025-08-20T04:01:02ZindUniversitas Pembangunan Nasional "Veteran" YogyakartaOPSI1693-21022686-23522025-06-0118113615110.31315/opsi.v18i1.1396011322machine learning-driven Six Sigma framework for enhancing the quality improvement and productivity in the Aircraft ManufacturingDwi Adi Purnama0Alfiqra Alfiqra1Winda Nur Cahyo2Departement of Industrial Engineering, Universitas Islam Indonesia, Yogyakarta IndonesiaPT Astra Otoparts Tbk, Kelapa Gading, Jakarta, 14250, IndonesiaDepartement of Industrial Engineering, Universitas Islam Indonesia, Yogyakarta, 55584, IndonesiaThe aviation industry, a pillar of global transportation, is under constant pressure to increase productivity and efficiency while maintaining strict quality requirements. Airctraft defects in production can result in significant financial losses, lead to costly rework, delays, and even safety risks. This study proposes a framework to improve productivity and efficiency in aircraft manufacturing and analyze quality control using machine learning, Six Sigma, and the QCDSME (Quality-Cost-Delivery-Safety-Morale) method. The DMAIC (Define-Measure-Analyze-Improve-Control) stage is a reference in the implementation steps of the Six Sigma method of the Airbus A320. The sigma value in this study was obtained on average for 40 periods of 4.61 sigma and a DPMO of 1225.69. At the analyze stage, a fishbone diagram is used to find the root cause of the problem. Furthermore, a machine learning analysis was performed using the text mining method to identify the most common product components that frequently have defects in Airbus A320 and identify the main factors causing defects, by the human factor. The enhance stage suggests a rise in overcoming challenges with the QCDSME method. Overall, it was discovered that the number of defects fell while the sigma improved and this method can enhance industry performance.https://jurnal.upnyk.ac.id/index.php/opsi/article/view/13960aircraft manufacturingquality improvementsix sigmamachine learning |
| spellingShingle | Dwi Adi Purnama Alfiqra Alfiqra Winda Nur Cahyo machine learning-driven Six Sigma framework for enhancing the quality improvement and productivity in the Aircraft Manufacturing OPSI aircraft manufacturing quality improvement six sigma machine learning |
| title | machine learning-driven Six Sigma framework for enhancing the quality improvement and productivity in the Aircraft Manufacturing |
| title_full | machine learning-driven Six Sigma framework for enhancing the quality improvement and productivity in the Aircraft Manufacturing |
| title_fullStr | machine learning-driven Six Sigma framework for enhancing the quality improvement and productivity in the Aircraft Manufacturing |
| title_full_unstemmed | machine learning-driven Six Sigma framework for enhancing the quality improvement and productivity in the Aircraft Manufacturing |
| title_short | machine learning-driven Six Sigma framework for enhancing the quality improvement and productivity in the Aircraft Manufacturing |
| title_sort | machine learning driven six sigma framework for enhancing the quality improvement and productivity in the aircraft manufacturing |
| topic | aircraft manufacturing quality improvement six sigma machine learning |
| url | https://jurnal.upnyk.ac.id/index.php/opsi/article/view/13960 |
| work_keys_str_mv | AT dwiadipurnama machinelearningdrivensixsigmaframeworkforenhancingthequalityimprovementandproductivityintheaircraftmanufacturing AT alfiqraalfiqra machinelearningdrivensixsigmaframeworkforenhancingthequalityimprovementandproductivityintheaircraftmanufacturing AT windanurcahyo machinelearningdrivensixsigmaframeworkforenhancingthequalityimprovementandproductivityintheaircraftmanufacturing |