A machine learning-driven Six Sigma framework for enhancing the quality improvement and productivity in the Aircraft Manufacturing

Authors

  • Dwi Adi Purnama Departement of Industrial Engineering, Universitas Islam Indonesia, Yogyakarta Indonesia
  • Alfiqra Alfiqra PT Astra Otoparts Tbk, Kelapa Gading, Jakarta, 14250, Indonesia
  • Winda Nur Cahyo Departement of Industrial Engineering, Universitas Islam Indonesia, Yogyakarta, 55584, Indonesia

DOI:

https://doi.org/10.31315/opsi.v18i1.13960

Keywords:

Aircraft Manufacturing, Quality Improvement, Six Sigma, Machine Learning

Abstract

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.

References

[1] V. Bhatia et al., “Industry 4.0 in Aircraft Manufacturing: Innovative Use Cases and Patent Landscape,” in Industry 4.0 Driven Manufacturing Technologies, A. Kumar, P. Kumar, and Y. Liu, Eds., in Springer Series in Advanced Manufacturing. , Cham: Springer Nature Switzerland, 2024, pp. 103–137. doi: 10.1007/978-3-031-68271-1_5.

[2] Z. Abd Elnaby, A. Zaher, R. K. Abdel-Magied, and H. I. Elkhouly, “Improving plastic manufacturing processes with the integration of Six Sigma and machine learning techniques: a case study,” J. Ind. Prod. Eng., vol. 41, no. 1, pp. 1–18, Jan. 2024, doi: 10.1080/21681015.2023.2260384.

[3] J. Dalzochio et al., “Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges,” Comput. Ind., vol. 123, p. 103298, 2020.

[4] A. Kanawaday and A. Sane, “Machine learning for predictive maintenance of industrial machines using IoT sensor data,” in 2017 8th IEEE international conference on software engineering and service science (ICSESS), IEEE, 2017, pp. 87–90. Accessed: Dec. 02, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8342870/.

[5] M. Paolanti, L. Romeo, A. Felicetti, A. Mancini, E. Frontoni, and J. Loncarski, “Machine learning approach for predictive maintenance in industry 4.0,” in 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), IEEE, 2018, pp. 1–6. Accessed: Dec. 02, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8449150/

[6] A. Theissler, J. Pérez-Velázquez, M. Kettelgerdes, and G. Elger, “Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry,” Reliab. Eng. Syst. Saf., vol. 215, p. 107864, 2021.

[7] A. Chiarini and M. Kumar, “Lean Six Sigma and Industry 4.0 integration for Operational Excellence: evidence from Italian manufacturing companies,” Prod. Plan. Control, vol. 32, no. 13, pp. 1084–1101, Oct. 2021, doi: 10.1080/09537287.2020.1784485.

[8] B. N. Kuncoro, “Pengendalian Kualitas Produksi Dengan Metode Six-Sigma Pada Industri Amdk Produk 600 Ml Pt Tirta Investama (Aqua),” J. Tek. Dan Sci., vol. 2, no. 1, pp. 01–07, 2023.

[9] I. B. Da Silva, M. G. Cabeça, G. F. Barbosa, and S. B. Shiki, “Lean Six Sigma for the automotive industry through the tools and aspects within metrics: a literature review,” Int. J. Adv. Manuf. Technol., vol. 119, no. 3–4, pp. 1357–1383, Mar. 2022, doi: 10.1007/s00170-021-08336-0.

[10] S. Bhat, E. V. Gijo, A. M. Rego, and V. S. Bhat, “Lean Six Sigma competitiveness for micro, small and medium enterprises (MSME): an action research in the Indian context,” TQM J., vol. 33, no. 2, pp. 379–406, 2021.

[11] D. Singh and G. Singh, “Critical success factors for Six Sigma implementation in Indian SMEs: an evaluation using AHP,” Meas. Bus. Excell., vol. 25, no. 2, pp. 152–170, 2021.

[12] S. K. Gaikwad, A. Paul, M. A. Moktadir, S. K. Paul, and P. Chowdhury, “Analyzing barriers and strategies for implementing Lean Six Sigma in the context of Indian SMEs,” Benchmarking Int. J., vol. 27, no. 8, pp. 2365–2399, 2020.

[13] O. A. Kolawole, J. L. Mishra, and Z. Hussain, “Addressing food waste and loss in the Nigerian food supply chain: Use of Lean Six Sigma and Double-Loop Learning,” Ind. Mark. Manag., vol. 93, pp. 235–249, 2021.

[14] M.-V. Sánchez-Rebull, R. Ferrer-Rullan, A.-B. Hernández-Lara, and A. Niñerola, “Six Sigma for improving cash flow deficit: a case study in the food can manufacturing industry,” Int. J. Lean Six Sigma, vol. 11, no. 6, pp. 1105–1126, 2020.

[15] A. Niñerola, M.-V. Sánchez-Rebull, and A.-B. Hernández-Lara, “Quality improvement in healthcare: Six Sigma systematic review,” Health Policy, vol. 124, no. 4, pp. 438–445, 2020.

[16] B. Vasconcellos De Araujo, “Lean Six Sigma in Services: An Application of the Methodology in the Attendiment Sector of an Exam Laboratory,” Sci. J. Bus. Manag., vol. 8, no. 3, p. 119, 2020, doi: 10.11648/j.sjbm.20200803.13.

[17] A. Bousdekis, K. Lepenioti, D. Apostolou, and G. Mentzas, “A review of data-driven decision-making methods for industry 4.0 maintenance applications,” Electronics, vol. 10, no. 7, p. 828, 2021.

[18] C. Li, Y. Chen, and Y. Shang, “A review of industrial big data for decision making in intelligent manufacturing,” Eng. Sci. Technol. Int. J., vol. 29, p. 101021, 2022.

[19] S. Kumar, A. K. Kar, and P. V. Ilavarasan, “Applications of text mining in services management: A systematic literature review,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 1, p. 100008, 2021.

[20] S. Ozcan, A. Homayounfard, C. Simms, and J. Wasim, “Technology roadmapping using text mining: A foresight study for the retail industry,” IEEE Trans. Eng. Manag., vol. 69, no. 1, pp. 228–244, 2021.

[21] V. Gaspersz, “Pedoman implementasi program six sigma terintegrasi dengan ISO 9001: 2000, MBNQA, dan HACCP,” 2002.

[22] R. Godina, B. G. R. Silva, and P. Espadinha-Cruz, “A DMAIC integrated fuzzy FMEA model: a case study in the Automotive Industry,” Appl. Sci., vol. 11, no. 8, p. 3726, 2021.

[23] A. Mittal, P. Gupta, V. Kumar, A. Al Owad, S. Mahlawat, and S. Singh, “The performance improvement analysis using Six Sigma DMAIC methodology: A case study on Indian manufacturing company,” Heliyon, vol. 9, no. 3, 2023, Accessed: Dec. 02, 2024. [Online]. Available: https://www.cell.com/heliyon/fulltext/S2405-8440(23)01832-7.

[24] H. Rifqi, A. Zamma, S. B. Souda, and M. Hansali, “Lean manufacturing implementation through DMAIC approach: A case study in the automotive industry,” Qual. Innov. Prosper., vol. 25, no. 2, pp. 54–77, 2021.

[25] A. Trimarjoko, H. Hardi Purba, and A. Nindiani, “Consistency of DMAIC phases implementation on Six Sigma method in manufacturingand service industry: a literature review,” Manag. Prod. Eng. Rev., 2020, Accessed: Dec. 02, 2024. [Online]. Available: https://journals.pan.pl/dlibra/show-content?id=119029.

[26] A. Widodo and D. Soediantono, “Benefits of the six sigma method (dmaic) and implementation suggestion in the defense industry: A literature review,” Int. J. Soc. Manag. Stud., vol. 3, no. 3, pp. 1–12, 2022.

[27] J. Susetyo, W. Winarni, and C. Hartanto, “Aplikasi Six Sigma DMAIC dan Kaizen sebagai metode pengendalian dan perbaikan kualitas produk,” J. Teknol., vol. 4, no. 1, pp. 78–87, 2011.

[28] R. S. Rodrigues, P. P. Balestrassi, A. P. Paiva, A. Garcia-Diaz, and F. J. Pontes, “Aircraft interior failure pattern recognition utilizing text mining and neural networks,” J. Intell. Inf. Syst., vol. 38, no. 3, pp. 741–766, Jun. 2012, doi: 10.1007/s10844-011-0176-1.

[29] J. Juwandi and D. Kamsin, “Pengaruh Kompensasi, Karakteristik Pekerjaan Dan Kepuasan Kerja Terhadap Kinerja Karyawan Departemen Quality Assurance Dan Quality Control Pada PT. Yasunaga Indonesia Di Kabupaten Serang,” JMB J. Manaj. Dan Bisnis, vol. 9, no. 1, pp. 1–9, 2020.

[30] R. F. N. Janah, H. C. Wahyuni, and I. Marodiyah, “Quality Improvement of Health Plaster Products With Six Sigma Method and QCDSME Analysis,” Spektrum Ind., vol. 22, no. 1, pp. 14–24, 2024.

[31] A. H. Stani, C. D. Ariani, D. Supriyadi, and M. I. Gazian, “Implementation of the QCDMSE Strategy in the Community Empowerment Program to Utilize the Sulawesi Masked Owls as a Natural Predator for Rodents,” Prospect J. Pemberdaya. Masy., vol. 2, no. 2, pp. 94–103, 2023.

Downloads

Published

2025-06-30

Issue

Vol. 18 No. 1 (2025): OPSI - June 2025

Section

Article

License

Authors who publish articles in this journal agree to the following conditions:

  1. Copyright remains with the author and gives the Opsi journal the right as a priority to publish its articles with Creative Commons Attribution 4.0 International license. Which allows articles to be shared with acknowledgement of the author of the article and this journal as the place of publication.
  2. Authors can distribute their articles on a non-exclusive basis (e.g. in university repositories or books) with notification or acknowledgement of publication in Opsi journals.
  3. Authors are allowed to post their work online (e.g. on a personal website or in a university repository) before and after the submission process (see The Effect of Open Access)
 

Lisensi Creative Commons

This work is Licensed Under a Creative Commons Attribution 4.0 International license.