An Integrated DMAIC-Real-Time Quality Control Framework for Quality Control in Garment Manufacturing

Authors

  • Adhe Rizky Anugerah Yogyakarta State University Author
  • Amar Al Haq Industrial Engineering, Faculty of Engineering, Yogyakarta State University Author
  • Ahmad Habib Ridho Fathurrosyiddin Yogyakarta State University Author
  • Syalfa Oktiva Nanda Yogyakarta State University Author
  • Saprina Melita Sari Yogyakarta State University Author
  • Farrel Hilmy Dzkay Dermawan Yogyakarta State University Author

DOI:

https://doi.org/10.20961/performa.v25i2.3440

Keywords:

Production defect, Six Sigma, Quality Improvement

Abstract

This study proposes an integrated DMAIC–Real-Time Quality Control (RT-QC) framework to enhance defect reduction in garment manufacturing. While Six Sigma has been widely implemented in manufacturing industries, limited studies have explored its integration with real-time quality monitoring systems, particularly in labor-intensive industries within emerging economies. This research addresses that gap by developing and implementing an integrated quality improvement framework in a medium-scale garment manufacturer. Using the DMAIC approach combined with real-time inspection control, the study systematically identified root causes of defects and implemented targeted corrective actions. The results demonstrate a structured and systematic framework for defect identification, process capability analysis, and sigma-level performance monitoring through the integration of DMAIC and real-time quality control. The integrated approach not only enhances responsiveness to quality deviations but also strengthens process stability and continuous improvement culture. The findings contribute theoretically by extending Six Sigma implementation through real-time monitoring integration and practically by offering a replicable framework for similar labor-intensive manufacturing environments.

References

Ahmad, D. M., Gáspár, L., Shaheen, H. M., Al-Shihabi, T. A., Maya, R. A., & Pinto, F. S. (2025). Improving material tracking for sustainable construction: A standard operating procedure (SOP) framework for resource efficiency. Buildings, 15(11). https://doi.org/10.3390/buildings15111941

Akbar, A., Khan, I., Khan, D., Ali, S., Shuaib, K., Ansari, J., & Khan Wassan, R. (2024). Using the potential of Six Sigma (DMAIC) at sewing section of a Pakistan based garments manufacturing company. 19(12). www.arpnjournals.com

Ewnetu, M., & Gzate, Y. (2023). Assembly operation productivity improvement for garment production industry through the integration of lean and work-study, a case study on Bahir Dar textile share company in garment, Bahir Dar, Ethiopia. Heliyon, 9(7). https://doi.org/10.1016/j.heliyon.2023.e17917

Faishal, M., Mohamad, E., Asih, H. M., Rahman, A. A. A., Ibrahim, A. Z., & Adiyanto, O. (2024). The use of Lean Six Sigma to improve the quality of coconut shell briquette products. Multidisciplinary Science Journal, 6(2). https://doi.org/10.31893/MULTISCIENCE.2024005

Fibriani, L., Kesy Garside, A., & Amallynda, I. (2023). Lean Six Sigma approach to improve the production process in the garment company: A case study. Journal of Engineering and Management in Industrial System, 11(2), 153–168. https://doi.org/10.21776/ub.jemis.2023.011.02.7

Fridkin, S., Winokur, M., & Gamliel, A. (2024). Development of a quality deterioration index for sustainable quality management in high-tech electronics manufacturing. Sustainability (Switzerland), 16(15). https://doi.org/10.3390/su16156592

Garcia-Garcia, G., Coulthard, G., Jagtap, S., Afy-Shararah, M., Patsavellas, J., & Salonitis, K. (2021). Business process re-engineering to digitalise quality control checks for reducing physical waste and resource use in a food company. Sustainability (Switzerland), 13(22). https://doi.org/10.3390/su132212341

Garg, A., Raina, K., & Sharma, R. (2020). Reducing soldering defects in mobile phone manufacturing company: A DMAIC approach. IOP Conference Series: Materials Science and Engineering, 748(1). https://doi.org/10.1088/1757-899X/748/1/012027

Haridy, S., Alamassi, B., Maged, A., Shamsuzzaman, M., Al Owad, A., & Bashir, H. (2023). Economic statistical model of the np chart for monitoring defectives. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-40151-3

Hoque, I., & Maalouf, M. M. (2022). Quality intervention, supplier performance and buyer–supplier relationships: Evidence from the garment industry. Benchmarking, 29(8), 2337–2358. https://doi.org/10.1108/BIJ-02-2021-0075

Hosen, S., Hamzah, S. R. ah, Arif Ismail, I., Noormi Alias, S., Faiq Abd Aziz, M., & Rahman, M. M. (2024). Training & development, career development, and organizational commitment as the predictor of work performance. Heliyon, 10(1). https://doi.org/10.1016/j.heliyon.2023.e23903

Huang, Y., & Xiang, Z. (2022). RPDNet: Automatic fabric defect detection based on a convolutional neural network and repeated pattern analysis. Sensors, 22(16). https://doi.org/10.3390/s22166226

Iqbal, S., Hanif, M., & Khan, S. (2024). Impact of staff training on university productivity through job satisfaction: A study of ISO 9001- certified institutions. PLoS ONE, 19(7 July). https://doi.org/10.1371/journal.pone.0306799

Kwilinski, A., & Kardas, M. (2024). The role of the Pareto principle in quality management within Industry 4.0: A comprehensive bibliometric analysis. In Virtual Economics (Vol. 7, Number 3, pp. 7–24). The London Academy of Science and Business. https://doi.org/10.34021/ve.2024.07.03(1)

Lucantoni, L., Antomarioni, S., Ciarapica, F. E., & Bevilacqua, M. (2025). A data-driven framework for supporting the total productive maintenance strategy. Expert Systems with Applications, 268. https://doi.org/10.1016/j.eswa.2024.126283

Margetis, G., Valakou, K., Ntoa, S., Gavgiotaki, D., & Stephanidis, C. (2025). Adaptive augmented reality user interfaces for real-time defect visualization and on-the-fly reconfiguration for zero-defect manufacturing. Sensors, 25(9). https://doi.org/10.3390/s25092789

Mittal, A., Gupta, P., Kumar, V., Al Owad, A., Mahlawat, S., & Singh, S. (2023a). The performance improvement analysis using Six Sigma DMAIC methodology: A case study on Indian manufacturing company. Heliyon, 9(3). https://doi.org/10.1016/j.heliyon.2023.e14625

Monday, L. M. (2022). Define, Measure, Analyze, Improve, Control (DMAIC) methodology as a roadmap in quality improvement. Global Journal on Quality and Safety in Healthcare, 5(2), 44–46. https://doi.org/10.36401/jqsh-22-x2

Mustafa Ibrahim, M. (2023). Reducing machine breakdown time using DMAIC approach in a garment industry. In International Journal of Engineering Applied Sciences and Technology (Vol. 8). http://www.ijeast.com

Peruchi, R. S., Rotela Junior, P., Brito, T. G., Paiva, A. P., Balestrassi, P. P., & Mendes Araujo, L. M. (2020). Integrating multivariate statistical analysis into Six Sigma DMAIC projects: A case study on AISI 52100 hardened steel turning. IEEE Access, 8, 34246–34255. https://doi.org/10.1109/ACCESS.2020.2973172

Pranowo, I. D., & Artanto, D. (2022). Smart monitoring system using NodeMCU for maintenance of production machines. Indonesian Journal of Electrical Engineering and Computer Science, 25(2), 788–795. https://doi.org/10.11591/ijeecs.v25.i2.pp788-795

Psarommatis, F., & Azamfirei, V. (2024). Zero defect manufacturing: A complete guide for advanced and sustainable quality management. In Journal of Manufacturing Systems (Vol. 77, pp. 764–779). Elsevier B.V. https://doi.org/10.1016/j.jmsy.2024.10.022

Putra, R. C., Widya, A. R., & Maulidani, B. C. (2025). Penerapan metode Six Sigma Define, Measure, Analyze, Improve and Control (DMAIC) untuk mengurangi produk cacat pada proses pembuatan pagar minimalis di PT. XYZ. Jurnal Teknik Industri Terintegrasi, 8(3), 3497–3502. https://doi.org/10.31004/jutin.v8i3.48574

Shafi, I., Mazhar, M. F., Fatima, A., Alvarez, R. M., Miró, Y., Espinosa, J. C. M., & Ashraf, I. (2023). Deep learning-based real time defect detection for optimization of aircraft manufacturing and control performance. Drones, 7(1). https://doi.org/10.3390/drones7010031

Silva, M. M., de Gusmão, A. P. H., Poleto, T., Silva, L. C. E., & Costa, A. P. C. S. (2014). A multidimensional approach to information security risk management using FMEA and fuzzy theory. International Journal of Information Management, 34(6), 733–740. https://doi.org/10.1016/j.ijinfomgt.2014.07.005

Surucu, O., Gadsden, S. A., & Yawney, J. (2023). Condition monitoring using machine learning: A review of theory, applications, and recent advances. In Expert Systems with Applications (Vol. 221). Elsevier Ltd. https://doi.org/10.1016/j.eswa.2023.119738

Wahyu Purnomo, D., & Hermansyah, M. (2025). Quality control analysis on 100ML bottle products with fault tree analysis method approach and failur mode and effect analysis. JKIE (Journal Knowledge Industrial Engineering), 11(3), 153–161. https://doi.org/10.35891/jkie.v11i3.6164

Wang, H., Chen, Y., Wang, L., Liu, Q., Yang, S., & Wang, C. (2023). Advancing herbal medicine: Enhancing product quality and safety through robust quality control practices. In Frontiers in Pharmacology (Vol. 14). Frontiers Media SA. https://doi.org/10.3389/fphar.2023.1265178

Downloads

Published

2026-10-12

Issue

Vol. 25 No. 2 (2026): Performa: Media Ilmiah Teknik Industri (Issue in Progress)

Section

Articles

License

Copyright (c) 2026 Performa: Media Ilmiah Teknik Industri

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.