Pergeseran Paradigma Pemeliharaan di Era Industri 4.0 : Analisis Implementasi TPM Berbasis AI dan Dampaknya pada Efisiensi Manufaktur
DOI:
https://doi.org/10.51903/juritek.v4i1.4042Keywords:
Artificial Intelligence, Industry 4.0, Total Productive MaintenanceAbstract
Industry 4.0 has driven significant transformation in manufacturing maintenance strategies, particularly in the implementation of Total Productive Maintenance (TPM). This research examines the paradigm shift from conventional TPM methods to Artificial Intelligence (AI)-based approaches, utilizing a qualitative methodology with phenomenological design and participatory observation in manufacturing environments. Results indicate that integrating AI with TPM has transformed maintenance approaches from reactive to predictive, enabling real-time anomaly detection, predictive maintenance, and maintenance schedule optimization that significantly improve Overall Equipment Effectiveness (OEE). Despite providing benefits such as reduced downtime and maintenance costs, AI-based TPM implementation faces challenges including data fragmentation, skilled human resource requirements, and organizational culture resistance. The development of collaboration between industry and academic institutions, as well as investment in edge computing infrastructure, are identified as key to maximizing the potential of AI-based TPM in enhancing operational sustainability in manufacturing.
References
Aljarrah, M. M., Zawaideh, F. H., Magableh, M., Al Wahshat, H., Mohamed, R. R., & V, A. K. (2023). Internet of Thing (IoT) and data analytics with challenges and future applications. In 2023 International Conference on Computer Science and Emerging Technologies (CSET) (pp. 1–8). IEEE. https://doi.org/10.1109/CSET58993.2023.10346664
Amangeldy, I. S., & Bissembayev, A. S. (2024). Enhancing operational efficiency in Industry 4.0: A predictive maintenance approach. Herald of the Kazakh-British Technical University, 21(2), 10–18. https://doi.org/10.55452/1998-6688-2024-21-2-10-18
Anagnostara, I. M., Sagani, A., Passias, V., Koufokotsios, N., Kourkoumpas, D.-S., & Nikolopoulos, N. (2024). Cost-oriented predictive maintenance using exponential degradation modelling: Application on manufacturing industries. In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 01–07). IEEE. https://doi.org/10.1109/ETFA61755.2024.10710954
Cavalcanti, J. H., Kovacs, T., Ko, A., & Pocsarovszky, K. (2024). Production system efficiency optimization through application of a hybrid artificial intelligence solution. International Journal of Computer Integrated Manufacturing, 37(6), 790–807. https://doi.org/10.1080/0951192X.2023.2257661
Chikwendu, O. C., Chima, A. S., & Edith, M. C. (2020). The optimization of overall equipment effectiveness factors in a pharmaceutical company. Heliyon, 6(4), e03796. https://doi.org/10.1016/j.heliyon.2020.e03796
Hallioui, A., Herrou, B., Katina, P. F., Santos, R. S., Egbue, O., Jasiulewicz-Kaczmarek, M., … Marques, P. C. (2023). A review of sustainable total productive maintenance (STPM). Sustainability, 15(16), 12362. https://doi.org/10.3390/su151612362
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
Liu, Y., Xu, S., & Zhang, B. (2020). Thriving at work: How a paradox mindset influences innovative work behavior. The Journal of Applied Behavioral Science, 56(3), 347–366. https://doi.org/10.1177/0021886319888267
Marinagi, C., Reklitis, P., Trivellas, P., & Sakas, D. (2023). The impact of Industry 4.0 technologies on key performance indicators for a resilient supply chain 4.0. Sustainability, 15(6), 5185. https://doi.org/10.3390/su15065185
Mosleuzzaman, M., Arif, I., & Siddiki, A. (2024). Design and development of a smart factory using Industry 4.0 technologies. Academic Journal on Business Administration, Innovation & Sustainability, 4(04), 89–108. https://doi.org/10.69593/ajbais.v4i04.131
Qi, Z., Du, L., Huo, R., & Huang, T. (2024). Predictive maintenance based on identity resolution and transformers in IIoT. Future Internet, 16(9), 310. https://doi.org/10.3390/fi16090310
Rahardjo, J., & Yahya, S. bin. (2010). Advanced manufacturing technology implementation process in SME: Critical success factors. Jurnal Teknik Industri, 12(2), 101–108. https://doi.org/10.9744/jti.12.2.101-108
Ralahallo, F. N., Tubalawony, J., Watumlawar, E., & Tabelessy, W. (2024). Implementasi total quality manajemen (TQM) sebagai strategi meningkatkan keunggulan bersaing pada UMKM di Negeri Waai. Abdimas Indonesian Journal, 4(1), 187–194. https://doi.org/10.59525/aij.v4i1.360
Singh, H., & Singh, B. (2024). Industry 4.0 technologies integration with lean production tools: A review. The TQM Journal, 36(8), 2507–2526. https://doi.org/10.1108/TQM-02-2022-0065
Soori, M., Arezoo, B., & Dastres, R. (2023). Internet of things for smart factories in Industry 4.0, a review. Internet of Things and Cyber-Physical Systems, 3, 192–204. https://doi.org/10.1016/j.iotcps.2023.04.006
Syamsul, N. I. (2020). Sosialisasi model House of Quality produk eceng gondok dalam perbaikan mutu pada UMKM Rumah Anyamandiri di Kota Makassar. JURNAL TEPAT: Applied Technology Journal for Community Engagement and Services, 3(2), 49–55. https://doi.org/10.25042/jurnal_tepat.v3i2.137
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
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
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.png)
