Peran Quality Engineering dalam Optimasi Proses Produksi

(Studi Literatur pada Industri Manufaktur Kontemporer)

Authors

  • Siti Nur Hamidah Universitas Sehati Indonesia
  • Moh. Ayip Fathani Universitas Sehati Indonesia
  • Zulfadlillah Zulfadlillah Universitas Sehati Indonesia
  • Kardita Kardita Universitas Sehati Indonesia

DOI:

https://doi.org/10.55606/jurritek.v3i1.4775

Keywords:

Industry 4.0, Process Optimization, Quality Engineering

Abstract

This systematic literature review examines the evolving role of Quality Engineering (QE) in optimizing production processes within Industry 4.0 contexts. By analyzing 78 peer-reviewed studies (2010–2025), the research identifies critical shifts in QE methodologies, emphasizing integration with artificial intelligence (AI), machine learning (ML), and real-time digital twin technologies. Key findings reveal enhanced robustness through adaptive optimization algorithms (e.g., Bayesian optimization, NSGA-II), improved defect prediction via AI-driven quality control systems, and streamlined process interoperability through Manufacturing Execution Systems (MES) and Quality 4.0 frameworks. The study underscores digital integration as a catalyst for reducing variability, accelerating decision-making, and aligning quality management with Industry 4.0’s demands for agility and interconnected systems. Recommendations include adopting hybrid methodologies combining classical Six Sigma with ML-driven analytics and investing in workforce training for digital QMS adoption.

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References

Antić, S. (2024). The impact of MES digital technology on the digitalization of the quality management system (QMS): A case study. Tehnika, 79(4), 497–504. https://doi.org/10.5937/tehnika2404497A

Antonino, P. O., Capilla, R., Pelliccione, P., Schnicke, F., Espen, D., Kuhn, T., & Schmid, K. (2022). A Quality 4.0 model for architecting industry 4.0 systems. Advanced Engineering Informatics, 54, 101801. https://doi.org/10.1016/j.aei.2022.101801

Bertocci, F., Grandoni, A., Fidanza, M., & Berni, R. (2021). A guideline for implementing a robust optimization of a complex multi-stage manufacturing process. Applied Sciences, 11(4), 1418. https://doi.org/10.3390/app11041418

Broday, E. E. (2022). The evolution of quality: From inspection to Quality 4.0. International Journal of Quality and Service Sciences, 14(3), 368–382. https://doi.org/10.1108/IJQSS-09-2021-0121

Carvalho, A. V., & Lima, T. M. (2022). Quality 4.0 and cognitive engineering applied to quality management systems: A framework. Applied System Innovation, 5(6), 115. https://doi.org/10.3390/asi5060115

Escobar, C. A., Macias, D., McGovern, M., Hernandez-de-Menendez, M., & Morales-Menendez, R. (2022). Quality 4.0 – An evolution of Six Sigma DMAIC. International Journal of Lean Six Sigma, 13(6), 1200–1238. https://doi.org/10.1108/IJLSS-05-2021-0091

Hosokawa, T., & Miyagi, Z. (2019). Quality engineering-based management: A proposal for achieving total optimisation of large systems. Total Quality Management & Business Excellence, 30(sup1), S182–S194. https://doi.org/10.1080/14783363.2019.1665843

Javaid, M., Haleem, A., Pratap Singh, R., & Suman, R. (2021). Significance of Quality 4.0 towards comprehensive enhancement in manufacturing sector. Sensors International, 2, 100109. https://doi.org/10.1016/j.sintl.2021.100109

Kudryavtseva, S. S., Matusevich, I. R., & Khaliulin, R. A. (2022). Technology of digitalization of quality management systems of business processes of the enterprise. Izvestiya of Samara Scientific Center of the Russian Academy of Sciences, 24(4), 37–41. https://doi.org/10.37313/1990-5378-2022-24-4-37-41

Naidu, N. V. R. (2008). RETRACTED: Mathematical model for quality cost optimization. Robotics and Computer-Integrated Manufacturing, 24(6), 811–815. https://doi.org/10.1016/j.rcim.2008.03.018

Resman, M., Herakovič, N., & Debevec, M. (2025). Integrating digital twin technology to achieve higher operational efficiency and sustainability in manufacturing systems. Systems, 13(3), 180. https://doi.org/10.3390/systems13030180

Santacruz, E. G., Romero, D., Noguez, J., & Wuest, T. (2025). Integrated Quality 4.0 framework for quality improvement based on Six Sigma and machine learning techniques towards zero-defect manufacturing. The TQM Journal, 37(4), 1115–1155. https://doi.org/10.1108/TQM-11-2023-0361

Santoso, S., Kusnanto, E., & Saputra, M. R. (2022). Perbandingan metode pengumpulan data dalam penelitian kualitatif dan kuantitatif serta aplikasinya dalam penelitian akuntansi interpretatif. OPTIMAL Jurnal Ekonomi dan Manajemen, 2(3), 351–360. https://doi.org/10.55606/optimal.v2i3.4457

Solonytska, I. V., Popel, O. V., Mardar, M. R., & Ganchev, S. S. (2024). Evolution of quality control systems: From Taylor to modern standards. Scientific Works, 88(2), 132–137. https://doi.org/10.15673/swonaft.v88i2.3055

Stanković, K., Jelić, D., Tomašević, N., & Krstić, A. (2024). Manufacturing process optimization for real-time quality control in multi-regime conditions: Tire tread production use case. Journal of Manufacturing Systems, 76, 293–313. https://doi.org/10.1016/j.jmsy.2024.07.015

Tang, J., Lin, X., Zhao, F., & Chen, X. (2024). Process quality control through Bayesian optimization with adaptive local convergence. Chemical Engineering Science, 293, 120039. https://doi.org/10.1016/j.ces.2024.120039

Tripathi, V., Chattopadhyaya, S., Mukhopadhyay, A. K., Saraswat, S., Sharma, S., Li, C., … Georgise, F. B. (2022). A novel smart production management system for the enhancement of industrial sustainability in Industry 4.0. Mathematical Problems in Engineering, 2022, 1–24. https://doi.org/10.1155/2022/6424869

Xiong, S. (2020). Personalized optimization and its implementation in computer experiments. IISE Transactions, 52(5), 528–536. https://doi.org/10.1080/24725854.2019.1630866

Yin, X., Niu, Z., He, Z., Li, Z., & Lee, D. (2020). An integrated computational intelligence technique based operating parameters optimization scheme for quality improvement oriented process-manufacturing system. Computers & Industrial Engineering, 140, 106284. https://doi.org/10.1016/j.cie.2020.106284

Zonnenshain, A., & Kenett, R. S. (2020). Quality 4.0—the challenging future of quality engineering. Quality Engineering, 32(4), 614–626. https://doi.org/10.1080/08982112.2019.1706744

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Published

2024-04-30

How to Cite

Siti Nur Hamidah, Moh. Ayip Fathani, Zulfadlillah Zulfadlillah, & Kardita Kardita. (2024). Peran Quality Engineering dalam Optimasi Proses Produksi : (Studi Literatur pada Industri Manufaktur Kontemporer). JURAL RISET RUMPUN ILMU TEKNIK, 3(1), 232–240. https://doi.org/10.55606/jurritek.v3i1.4775

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