Peran Quality Engineering dalam Optimasi Proses Produksi
(Studi Literatur pada Industri Manufaktur Kontemporer)
DOI:
https://doi.org/10.55606/jurritek.v3i1.4775Keywords:
Industry 4.0, Process Optimization, Quality EngineeringAbstract
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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